7309 lines
1.2 MiB
7309 lines
1.2 MiB
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"TIE ioloptst </>\n",
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"®) ToruOkadaOi\n",
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"\n",
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"ey nalakath.org\n",
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"\n"
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]
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}
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],
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"source": [
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"import pandas as pd\n",
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"import pytesseract\n",
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"from PIL import Image\n",
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"\n",
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"# Read screenshot\n",
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"image = Image.open('../Pictures/SCR-20251115-myav.jpeg')\n",
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"\n",
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"# Extract text\n",
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"text = pytesseract.image_to_string(image)\n",
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"print(text)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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'SCR-20250216-magv.png', 'SCR-20241028-olyr.png', 'SCR-20250215-sigg.png', 'SCR-20250215-lwwf.png', 'SCR-20241204-glyn.png', 'SCR-20250202-tijx.png', 'SCR-20250417-pgsd.png', 'SCR-20250122-mgsn.png', 'SCR-20250429-nknb.png', 'SCR-20250323-lgjf.png', 'SCR-20250414-jmyg.png', 'SCR-20250216-lqhz.png', 'SCR-20250413-pllg.png', 'SCR-20250209-sfvd.png', 'SCR-20250411-tpkn.png', 'SCR-20240805-rvvb.png', 'SCR-20250416-nvbq.png', 'SCR-20250420-rxof.png', 'SCR-20250120-tcqs.png', 'SCR-20250219-lklz.png', 'SCR-20250323-lisj.png', 'SCR-20250213-grby.png', 'SCR-20241023-oksb.png', 'SCR-20250210-uiof.png', 'SCR-20250202-msqz.png', 'SCR-20250215-rrmm.png', 'SCR-20250212-souw.png', 'SCR-20241217-ovmb.png', 'SCR-20241218-tkdy.png', 'SCR-20250218-rggg.png', 'SCR-20250312-qigj.png', 'SCR-20250416-nudr.png', 'SCR-20241110-qtuz.png', 'SCR-20250418-nxbv.png', 'SCR-20250410-pfhj.png', 'SCR-20250411-tzeo.png', 'SCR-20250117-llpb.png', 'SCR-20240803-iuga.png', 'SCR-20241201-ibxk.png', 'SCR-20240805-iulc.png', 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'SCR-20241204-kflq.png', 'SCR-20250111-owhx.png', 'SCR-20250411-otco.png', 'SCR-20241204-kjve.png', 'SCR-20250128-lozv.png', 'SCR-20250807-uadi.png', 'SCR-20250413-qetu.png', 'SCR-20250103-kdav.png', 'SCR-20250328-lrgo.png', 'SCR-20250201-nnex.png', 'SCR-20240807-mybz.png', 'SCR-20250303-qhjm.png', 'SCR-20250403-noih.png', 'SCR-20250323-kjol.png', 'SCR-20240916-rbni.png', 'SCR-20250420-qekc.png', 'SCR-20241226-nmgq.png', 'SCR-20250404-mtpq.png', 'SCR-20250408-kqcs.png', 'SCR-20250105-labv.png', 'SCR-20250319-jssg.png', 'SCR-20250218-jpsi.png', 'SCR-20250510-jakx.png', 'SCR-20240805-hxuc.png', 'SCR-20250207-sare.png', 'SCR-20250415-sjxj.png', 'SCR-20240804-tthv.png', 'SCR-20240920-mitm.png', 'SCR-20250201-uiqd.png', 'SCR-20250417-pjoa.png', 'SCR-20251113-hgvn.png', 'SCR-20240806-qqqd.png', 'SCR-20240924-mhen.png', 'SCR-20240805-tihg.png', 'SCR-20241219-ndli.png', 'SCR-20241110-qgvo.png', 'SCR-20250413-prii.png', 'SCR-20250202-klzi.png', 'SCR-20241204-hiaf.png', 'SCR-20250322-slyt.png', 'SCR-20240923-gsih.png', 'SCR-20250119-rfnw.png', 'SCR-20250118-rtpo.png', 'SCR-20250410-pebz.png', 'SCR-20250310-gtvh.png', 'SCR-20250416-nsqd.png', 'SCR-20250201-swfi.png', 'SCR-20240803-iqru.png', 'SCR-20250310-lwsx.png', 'SCR-20250201-susj.png', 'SCR-20250206-najx.png', 'SCR-20250413-qvyi.png', 'SCR-20250328-mzow.png', 'SCR-20250413-ptqh.png', 'SCR-20241003-lbbb.png', 'SCR-20250210-uhjl.png', 'SCR-20250309-qbgg.png', 'SCR-20251002-mlgy.png', 'SCR-20250212-tkmm.png', 'SCR-20250218-kmyr.png', 'SCR-20250303-qdrc.png', 'SCR-20250403-njyd.png', 'SCR-20250202-osgc.png', 'SCR-20241219-nwvo.png', 'SCR-20241219-iyxu.png', 'SCR-20250202-tmjn.png', 'SCR-20241219-mydj.png', 'SCR-20250217-mzio.png', 'SCR-20250414-kdae.png', 'SCR-20250202-hqeb.png', 'SCR-20241230-lduj.png', 'SCR-20250207-ranw.png', 'SCR-20250312-ndak.png', 'SCR-20250309-pynb.png', 'SCR-20250322-mfrz.png', 'SCR-20250117-mkdm.png', 'SCR-20250323-kwme.png', 'SCR-20250727-lizu.png', 'SCR-20250330-qyyg.png', 'SCR-20250120-nyva.png', 'SCR-20250207-mhdu.png', 'SCR-20250117-kzll.png', 'SCR-20250218-surt.png', 'SCR-20250420-qarq.png', 'SCR-20250218-mqni.png', 'SCR-20250307-mump.png', 'SCR-20241123-omws.png', 'SCR-20250406-kdah.png', 'SCR-20241204-gmes.png', 'SCR-20250117-miii.png', 'SCR-20250410-pgur.png', 'SCR-20250204-mzde.png', 'SCR-20250405-pdwq.png', 'SCR-20250322-taqo.png', 'SCR-20250411-qwxv.png', 'SCR-20250411-tjob.png', 'SCR-20250323-lflb.png', 'SCR-20250218-kttt.png', 'SCR-20250123-imxu.png', 'SCR-20250216-lwrv.png', 'SCR-20250220-lgdj.png', 'SCR-20250207-ktdd.png', 'SCR-20240804-ohfb.png', 'SCR-20250309-ldep.png', 'SCR-20250625-tndv.png', 'SCR-20250204-jpfx.png', 'SCR-20250303-rcfy.png', 'SCR-20250319-hgvt.png', 'SCR-20250109-sbxv.png', 'SCR-20250417-osnm.png']\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"import os\n",
|
||
"scr = []\n",
|
||
"for i in os.listdir(\"/Users/Aman/Pictures\"):\n",
|
||
" if i.endswith(\".png\") and i.startswith(\"SCR\"):\n",
|
||
" scr.append(i)\n",
|
||
"\n",
|
||
"print(scr)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 3,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"['/Users/Aman/Pictures/SCR-20250414-jufn.png', '/Users/Aman/Pictures/SCR-20241110-puyy.png', '/Users/Aman/Pictures/SCR-20250411-qwcf.png', '/Users/Aman/Pictures/SCR-20250120-nzey.png', '/Users/Aman/Pictures/SCR-20250416-nlpj.png', '/Users/Aman/Pictures/SCR-20250415-kdbe.png', '/Users/Aman/Pictures/SCR-20250119-omsb.png', '/Users/Aman/Pictures/SCR-20250307-nskw.png', '/Users/Aman/Pictures/SCR-20240805-igin.png', '/Users/Aman/Pictures/SCR-20250319-hing.png', '/Users/Aman/Pictures/SCR-20250416-necb.png', '/Users/Aman/Pictures/SCR-20250309-omkr.png', '/Users/Aman/Pictures/SCR-20250422-nwsd.png', '/Users/Aman/Pictures/SCR-20241211-hzfl.png', '/Users/Aman/Pictures/SCR-20250821-kwda.png', '/Users/Aman/Pictures/SCR-20250217-kmmg.png', '/Users/Aman/Pictures/SCR-20250202-tlzo.png', '/Users/Aman/Pictures/SCR-20250206-nafb.png', '/Users/Aman/Pictures/SCR-20250122-scvx.png', '/Users/Aman/Pictures/SCR-20250218-kbje.png', '/Users/Aman/Pictures/SCR-20250411-teqq.png', '/Users/Aman/Pictures/SCR-20241229-kijo.png', '/Users/Aman/Pictures/SCR-20250416-jsck.png', '/Users/Aman/Pictures/SCR-20240924-lrph.png', '/Users/Aman/Pictures/SCR-20250212-thgc.png', '/Users/Aman/Pictures/SCR-20250102-jcyt.png', '/Users/Aman/Pictures/SCR-20250211-szpe.png', '/Users/Aman/Pictures/SCR-20250309-puce.png', '/Users/Aman/Pictures/SCR-20250207-razh.png', '/Users/Aman/Pictures/SCR-20250213-hjzn.png', '/Users/Aman/Pictures/SCR-20250413-qvlg.png', '/Users/Aman/Pictures/SCR-20250116-pzdf.png', '/Users/Aman/Pictures/SCR-20250201-swsg.png', '/Users/Aman/Pictures/SCR-20250411-qhct.png', '/Users/Aman/Pictures/SCR-20250202-lbnr.png', '/Users/Aman/Pictures/SCR-20250117-kjpd.png', '/Users/Aman/Pictures/SCR-20250319-jnfy.png', '/Users/Aman/Pictures/SCR-20250214-soiu.png', '/Users/Aman/Pictures/SCR-20250413-qgun.png', '/Users/Aman/Pictures/SCR-20250403-ntiu.png', '/Users/Aman/Pictures/SCR-20250416-ooic.png', '/Users/Aman/Pictures/SCR-20241110-rqum.png', '/Users/Aman/Pictures/SCR-20250403-nmet.png', '/Users/Aman/Pictures/SCR-20250117-kwhd.png', '/Users/Aman/Pictures/SCR-20250210-ujqk.png', '/Users/Aman/Pictures/SCR-20241216-twmg.png', '/Users/Aman/Pictures/SCR-20250411-szdk.png', '/Users/Aman/Pictures/SCR-20250413-qgxi.png', '/Users/Aman/Pictures/SCR-20250202-szra.png', '/Users/Aman/Pictures/SCR-20250202-tjau.png', '/Users/Aman/Pictures/SCR-20250125-oral.png', '/Users/Aman/Pictures/SCR-20250215-qzdo.png', '/Users/Aman/Pictures/SCR-20250408-ofsy.png', '/Users/Aman/Pictures/SCR-20250202-tmhk.png', '/Users/Aman/Pictures/SCR-20250210-uauh.png', '/Users/Aman/Pictures/SCR-20250215-rtxc.png', '/Users/Aman/Pictures/SCR-20250403-sllc.png', '/Users/Aman/Pictures/SCR-20250125-lerx.png', '/Users/Aman/Pictures/SCR-20241226-qhaf.png', '/Users/Aman/Pictures/SCR-20241230-ojrt.png', '/Users/Aman/Pictures/SCR-20240805-irkj.png', '/Users/Aman/Pictures/SCR-20250224-qzkj.png', '/Users/Aman/Pictures/SCR-20251015-lycj.png', '/Users/Aman/Pictures/SCR-20250319-hnfi.png', '/Users/Aman/Pictures/SCR-20250426-osoo.png', '/Users/Aman/Pictures/SCR-20250201-ugsx.png', '/Users/Aman/Pictures/SCR-20250215-lehg.png', '/Users/Aman/Pictures/SCR-20250317-phkw.png', '/Users/Aman/Pictures/SCR-20250125-nmka.png', '/Users/Aman/Pictures/SCR-20250119-opdy.png', '/Users/Aman/Pictures/SCR-20250210-ocws.png', '/Users/Aman/Pictures/SCR-20250309-pkkh.png', '/Users/Aman/Pictures/SCR-20250408-nfav.png', '/Users/Aman/Pictures/SCR-20241204-kgsl.png', '/Users/Aman/Pictures/SCR-20250201-uadv.png', '/Users/Aman/Pictures/SCR-20250212-tqdn.png', '/Users/Aman/Pictures/SCR-20250201-nfil.png', '/Users/Aman/Pictures/SCR-20241116-nkiw.png', '/Users/Aman/Pictures/SCR-20250211-uptd.png', '/Users/Aman/Pictures/SCR-20241217-sdxr.png', '/Users/Aman/Pictures/SCR-20250210-txqi.png', '/Users/Aman/Pictures/SCR-20250303-qgtm.png', '/Users/Aman/Pictures/SCR-20250201-silb.png', '/Users/Aman/Pictures/SCR-20250209-shca.png', 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'/Users/Aman/Pictures/SCR-20250727-lizu.png', '/Users/Aman/Pictures/SCR-20250330-qyyg.png', '/Users/Aman/Pictures/SCR-20250120-nyva.png', '/Users/Aman/Pictures/SCR-20250207-mhdu.png', '/Users/Aman/Pictures/SCR-20250117-kzll.png', '/Users/Aman/Pictures/SCR-20250218-surt.png', '/Users/Aman/Pictures/SCR-20250420-qarq.png', '/Users/Aman/Pictures/SCR-20250218-mqni.png', '/Users/Aman/Pictures/SCR-20250307-mump.png', '/Users/Aman/Pictures/SCR-20241123-omws.png', '/Users/Aman/Pictures/SCR-20250406-kdah.png', '/Users/Aman/Pictures/SCR-20241204-gmes.png', '/Users/Aman/Pictures/SCR-20250117-miii.png', '/Users/Aman/Pictures/SCR-20250410-pgur.png', '/Users/Aman/Pictures/SCR-20250204-mzde.png', '/Users/Aman/Pictures/SCR-20250405-pdwq.png', '/Users/Aman/Pictures/SCR-20250322-taqo.png', '/Users/Aman/Pictures/SCR-20250411-qwxv.png', '/Users/Aman/Pictures/SCR-20250411-tjob.png', '/Users/Aman/Pictures/SCR-20250323-lflb.png', '/Users/Aman/Pictures/SCR-20250218-kttt.png', '/Users/Aman/Pictures/SCR-20250123-imxu.png', '/Users/Aman/Pictures/SCR-20250216-lwrv.png', '/Users/Aman/Pictures/SCR-20250220-lgdj.png', '/Users/Aman/Pictures/SCR-20250207-ktdd.png', '/Users/Aman/Pictures/SCR-20240804-ohfb.png', '/Users/Aman/Pictures/SCR-20250309-ldep.png', '/Users/Aman/Pictures/SCR-20250625-tndv.png', '/Users/Aman/Pictures/SCR-20250204-jpfx.png', '/Users/Aman/Pictures/SCR-20250303-rcfy.png', '/Users/Aman/Pictures/SCR-20250319-hgvt.png', '/Users/Aman/Pictures/SCR-20250109-sbxv.png', '/Users/Aman/Pictures/SCR-20250417-osnm.png']\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"from pathlib import Path\n",
|
||
"path = Path(\"/Users/Aman/Pictures\")\n",
|
||
"scr = [str(f.absolute()) for f in path.glob(\"SCR*.png\")]\n",
|
||
"print(scr)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 19,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"SCR-20250414-jufn.png\n",
|
||
"bedpe_higlass.png\n",
|
||
"SCR-20241110-puyy.png\n",
|
||
"SCR-20250411-qwcf.png\n",
|
||
"SCR-20250120-nzey.png\n",
|
||
"SCR-20250416-nlpj.png\n",
|
||
"Photo Booth Library\n",
|
||
"SCR-20250415-kdbe.png\n",
|
||
"SCR-20250119-omsb.png\n",
|
||
"SCR-20250307-nskw.png\n",
|
||
"SCR-20240805-igin.png\n",
|
||
"SCR-20250319-hing.png\n",
|
||
"SCR-20250416-necb.png\n",
|
||
"SCR-20250309-omkr.png\n",
|
||
"SCR-20250422-nwsd.png\n",
|
||
"SCR-20241211-hzfl.png\n",
|
||
"sample5\n",
|
||
"SCR-20250821-kwda.png\n",
|
||
"fastqc_results\n",
|
||
"SCR-20250217-kmmg.png\n",
|
||
"SCR-20250202-tlzo.png\n",
|
||
"SCR-20250206-nafb.png\n",
|
||
"area.png\n",
|
||
"SCR-20250122-scvx.png\n",
|
||
"SCR-20250218-kbje.png\n",
|
||
"SCR-20250411-teqq.png\n",
|
||
"SCR-20241229-kijo.png\n",
|
||
"SCR-20250416-jsck.png\n",
|
||
"SCR-20240924-lrph.png\n",
|
||
"SCR-20250212-thgc.png\n",
|
||
"SCR-20250102-jcyt.png\n",
|
||
"SCR-20250211-szpe.png\n",
|
||
"SCR-20250309-puce.png\n",
|
||
"SCR-20250207-razh.png\n",
|
||
"SCR-20250213-hjzn.png\n",
|
||
"SCR-20250413-qvlg.png\n",
|
||
"SCR-20250116-pzdf.png\n",
|
||
"SCR-20250201-swsg.png\n",
|
||
"SCR-20250411-qhct.png\n",
|
||
"SCR-20250202-lbnr.png\n",
|
||
"sample3_megahit.png\n",
|
||
"SCR-20250117-kjpd.png\n",
|
||
"SCR-20250319-jnfy.png\n",
|
||
"SCR-20250214-soiu.png\n",
|
||
"SCR-20250413-qgun.png\n",
|
||
"SCR-20250403-ntiu.png\n",
|
||
"SCR-20250416-ooic.png\n",
|
||
"bedpe_plot.png\n",
|
||
"SCR-20241110-rqum.png\n",
|
||
"SCR-20250403-nmet.png\n",
|
||
"SCR-20250117-kwhd.png\n",
|
||
"SCR-20250210-ujqk.png\n",
|
||
"SCR-20241216-twmg.png\n",
|
||
"SCR-20250411-szdk.png\n",
|
||
"SCR-20250413-qgxi.png\n",
|
||
"SCR-20250202-szra.png\n",
|
||
"SCR-20250202-tjau.png\n",
|
||
"SCR-20250125-oral.png\n",
|
||
"SCR-20250215-qzdo.png\n",
|
||
"SCR-20250408-ofsy.png\n",
|
||
"SCR-20250202-tmhk.png\n",
|
||
"SCR-20250210-uauh.png\n",
|
||
"SCR-20250215-rtxc.png\n",
|
||
"graph_sample5_spades.png\n",
|
||
"SCR-20250403-sllc.png\n",
|
||
"SCR-20250125-lerx.png\n",
|
||
"SCR-20241226-qhaf.png\n",
|
||
"SCR-20241230-ojrt.png\n",
|
||
"SCR-20240805-irkj.png\n",
|
||
"SCR-20250224-qzkj.png\n",
|
||
"SCR-20251015-lycj.png\n",
|
||
"SCR-20250319-hnfi.png\n",
|
||
"SCR-20250426-osoo.png\n",
|
||
"SCR-20250201-ugsx.png\n",
|
||
"SCR-20250215-lehg.png\n",
|
||
"SCR-20250317-phkw.png\n",
|
||
"SCR-20250125-nmka.png\n",
|
||
"SCR-20250119-opdy.png\n",
|
||
"SCR-20250210-ocws.png\n",
|
||
"SCR-20250309-pkkh.png\n",
|
||
"SCR-20250408-nfav.png\n",
|
||
"SCR-20241204-kgsl.png\n",
|
||
"SCR-20240802-sqpk.jpeg\n",
|
||
"SCR-20250201-uadv.png\n",
|
||
"SCR-20250212-tqdn.png\n",
|
||
"SCR-20250201-nfil.png\n",
|
||
"multiqc_data\n",
|
||
"SCR-20241116-nkiw.png\n",
|
||
"SCR-20250211-uptd.png\n",
|
||
"workflow_aman.png\n",
|
||
"SCR-20241217-sdxr.png\n",
|
||
"SCR-20250210-txqi.png\n",
|
||
"SCR-20250303-qgtm.png\n",
|
||
"SCR-20250201-silb.png\n",
|
||
"SCR-20250209-shca.png\n",
|
||
"SCR-20241104-sbtb.png\n",
|
||
"SCR-20250218-pift.png\n",
|
||
"SCR-20240805-stza.jpeg\n",
|
||
"SCR-20250411-ocbx.png\n",
|
||
"SCR-20250404-mzgz.png\n",
|
||
"SCR-20250202-ldlh.png\n",
|
||
"SCR-20251115-nebw.png\n",
|
||
"coolpup_pileup_analysis.ipynb\n",
|
||
"SCR-20250323-kdyp.png\n",
|
||
"SCR-20241028-okrx.png\n",
|
||
"SCR-20250421-meum.png\n",
|
||
"SCR-20250411-tdyo.png\n",
|
||
"SCR-20250119-otil.png\n",
|
||
"SCR-20250125-lpfs.png\n",
|
||
"SCR-20250119-ovrd.png\n",
|
||
"SCR-20250218-ptku.png\n",
|
||
"SCR-20250323-ljmd.png\n",
|
||
"SCR-20250210-uavg.png\n",
|
||
"CHG.png\n",
|
||
"SCR-20241217-sbon.png\n",
|
||
"SCR-20250118-rpfy.png\n",
|
||
"SCR-20250102-kcjm.png\n",
|
||
"SCR-20250216-pwna.png\n",
|
||
"SCR-20250322-sfaf.png\n",
|
||
"SCR-20250206-sofq.png\n",
|
||
"SCR-20241007-ndax.png\n",
|
||
"SCR-20250216-lwfo.png\n",
|
||
"SCR-20240920-mezb.png\n",
|
||
"SCR-20250204-jpho.png\n",
|
||
"SCR-20241204-gnyt.png\n",
|
||
"SCR-20250116-suqb.png\n",
|
||
"wood_sample_5_megahit_output_secondtry\n",
|
||
"SCR-20250206-kdum.png\n",
|
||
"SCR-20250312-rnsj.png\n",
|
||
"SCR-20241110-pyuf.png\n",
|
||
"SCR-20250322-mnpq.png\n",
|
||
"SCR-20250121-nqns.png\n",
|
||
"SCR-20241116-nviu.png\n",
|
||
"sample1_bbmap.png\n",
|
||
"SCR-20250131-kvwo.png\n",
|
||
"2024.10.31.15.43.58.HiCImage.svg\n",
|
||
"SCR-20241129-jfrq.png\n",
|
||
".DS_Store\n",
|
||
"SCR-20250416-jtjs.png\n",
|
||
"SCR-20250215-sqtk.png\n",
|
||
"imp_commands.png\n",
|
||
"SCR-20240924-nhck.png\n",
|
||
"SCR-20241123-omug.png\n",
|
||
"wood_sample_1_megahit_output_secondtry\n",
|
||
"SCR-20241018-irmc.png\n",
|
||
"SCR-20250217-mkio.png\n",
|
||
"SCR-20251121-msll.png\n",
|
||
"SCR-20250202-tist.png\n",
|
||
"SCR-20250201-ncvo.png\n",
|
||
"SCR-20250319-hmbp.png\n",
|
||
"SCR-20250416-isfz.png\n",
|
||
"SCR-20250322-syyr.png\n",
|
||
"SCR-20250303-rlxz.png\n",
|
||
"SCR-20250423-bljv.png\n",
|
||
"SCR-20250402-ptgn.jpeg\n",
|
||
"SCR-20250218-kvbq.png\n",
|
||
"SCR-20241210-ihkz.png\n",
|
||
"SCR-20250214-tphm.png\n",
|
||
"SCR-20241028-oncp.png\n",
|
||
"SCR-20250215-qdwl.png\n",
|
||
"SCR-20250410-pfgj.png\n",
|
||
"SCR-20241217-sbte.png\n",
|
||
"SCR-20250411-ubqx.png\n",
|
||
"SCR-20240925-mrnv.png\n",
|
||
"SCR-20241026-tkfm.jpeg\n",
|
||
"SCR-20250330-qcfn.png\n",
|
||
"SCR-20241110-qsku.png\n",
|
||
"fastqc_2\n",
|
||
"SCR-20241204-glla.png\n",
|
||
"wood_sample_5_spades_out\n",
|
||
"SCR-20250322-sqcu.png\n",
|
||
"SCR-20250117-kecl.png\n",
|
||
"SCR-20241230-reme.png\n",
|
||
"c1c2hic\n",
|
||
"SCR-20241219-nbby.png\n",
|
||
"SCR-20250216-okjd.png\n",
|
||
"SCR-20250410-pddf.png\n",
|
||
"SCR-20250216-mfym.png\n",
|
||
"SCR-20250204-nexj.png\n",
|
||
"Archive\n",
|
||
"SCR-20250309-nibt.png\n",
|
||
"SCR-20251128-nhvv.png\n",
|
||
"SCR-20250218-sxxd.png\n",
|
||
"SCR-20250207-mecu.png\n",
|
||
"SCR-20241110-rsli.png\n",
|
||
"SCR-20250213-gsqb.png\n",
|
||
"SCR-20241226-tllx.png\n",
|
||
"SCR-20250201-ucjx.png\n",
|
||
"SCR-20240909-tlcd.png\n",
|
||
"SCR-20250413-qouc.png\n",
|
||
"SCR-20241117-kzpb.png\n",
|
||
"SCR-20250201-opju.png\n",
|
||
"SCR-20241204-kijp.png\n",
|
||
"SCR-20250117-mlli.png\n",
|
||
"SCR-20241110-qbzt.jpeg\n",
|
||
"SCR-20250212-tjfa.png\n",
|
||
"SCR-20240918-omsf.png\n",
|
||
"SCR-20250411-tntd.png\n",
|
||
"SCR-20250117-ktvs.png\n",
|
||
"SCR-20250330-tjvd.png\n",
|
||
"SCR-20250405-nwiv.png\n",
|
||
"SCR-20250309-liov.png\n",
|
||
"SCR-20250117-khsn.png\n",
|
||
"SCR-20250322-swcf.png\n",
|
||
"SCR-20240924-lwlp.png\n",
|
||
"SCR-20250216-kqfc.png\n",
|
||
"SCR-20250403-smdn.png\n",
|
||
"SCR-20250311-sgxw.png\n",
|
||
"SCR-20241222-kjyj.png\n",
|
||
"SCR-20241211-jiku.png\n",
|
||
"SCR-20250204-onbg.png\n",
|
||
"SCR-20250418-jgfs.png\n",
|
||
"SCR-20250215-scdz.png\n",
|
||
"SCR-20240924-nfcp.png\n",
|
||
"SCR-20250416-nhek.png\n",
|
||
"SCR-20241110-rpgz.png\n",
|
||
"SCR-20250125-oaaw.png\n",
|
||
"SCR-20250216-lsee.png\n",
|
||
"SCR-20250114-rpcq.png\n",
|
||
"plotHiCFragmentSize_data.pdf\n",
|
||
"SCR-20250218-pwlu.png\n",
|
||
"SCR-20250218-ihds.png\n",
|
||
".localized\n",
|
||
"SCR-20251121-mphe.png\n",
|
||
"SCR-20250215-kkdr.png\n",
|
||
"SCR-20251121-mpia.png\n",
|
||
"SCR-20241028-oluk.png\n",
|
||
"SCR-20241225-nhjv.png\n",
|
||
"SCR-20251112-kswt.png\n",
|
||
"SCR-20250119-oqtd.png\n",
|
||
"SCR-20251103-ixoh.png\n",
|
||
"SCR-20250217-klqm.png\n",
|
||
"SCR-20250309-pqil.jpeg\n",
|
||
"SCR-20250204-ljih.png\n",
|
||
"SCR-20250319-hguq.png\n",
|
||
"SCR-20241218-qpje.png\n",
|
||
"SCR-20250212-tgbe.png\n",
|
||
"SCR-20250218-nemx.png\n",
|
||
"SCR-20240802-rihm.jpeg\n",
|
||
"SCR-20241219-lcoj.png\n",
|
||
"pup.pdf\n",
|
||
"SCR-20250309-qeqv.png\n",
|
||
"SCR-20250323-ruau.png\n",
|
||
"SCR-20250217-msii.png\n",
|
||
"SCR-20250117-kish.png\n",
|
||
"SCR-20251115-myav.jpeg\n",
|
||
"SCR-20241110-ronv.png\n",
|
||
"SCR-20250202-kctw.png\n",
|
||
"sample5_megahit.png\n",
|
||
"SCR-20241008-ojas.png\n",
|
||
"SCR-20250413-qesn.png\n",
|
||
"SCR-20241204-glsg.png\n",
|
||
"SCR-20240804-jgjh.jpeg\n",
|
||
"SCR-20250319-hgix.png\n",
|
||
"SCR-20250131-ldag.png\n",
|
||
"SCR-20251118-mzhm.png\n",
|
||
"SCR-20250214-uard.png\n",
|
||
"SCR-20250215-rsmq.png\n",
|
||
"SCR-20250416-thlg.png\n",
|
||
"SCR-20241204-hfte.png\n",
|
||
"SCR-20250119-rizp.png\n",
|
||
"SCR-20240802-sert.jpeg\n",
|
||
"SCR-20250410-pdzq.png\n",
|
||
"SCR-20250411-tlfr.png\n",
|
||
"SCR-20250218-qciy.png\n",
|
||
"SCR-20240805-srce.png\n",
|
||
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|
||
"TADs_chr1_improved.png\n",
|
||
"SCR-20241230-pvdy.png\n",
|
||
"SCR-20250411-tfsa.png\n",
|
||
"SCR-20250309-ktps.png\n",
|
||
"SCR-20250207-qwpg.png\n",
|
||
"SCR-20250201-sfoy.png\n",
|
||
"SCR-20250612-jdxi.png\n",
|
||
"SCR-20250322-mlzl.png\n",
|
||
"SCR-20241217-seoc.png\n",
|
||
"Athaliana_447_TAIR10.fa.fai\n",
|
||
"SCR-20241204-hhlw.png\n",
|
||
"SCR-20250125-lopy.jpeg\n",
|
||
"SCR-20250117-klnj.png\n",
|
||
"sample2_megahit.png\n",
|
||
"SCR-20241123-ncww.png\n",
|
||
"SCR-20250219-svru.png\n",
|
||
"SCR-20250918-kjxn.png\n",
|
||
"SCR-20250215-sibz.png\n",
|
||
"fastqc_0\n",
|
||
"SCR-20240924-ovgx.jpeg\n",
|
||
"SCR-20241202-otuh.png\n",
|
||
"SCR-20250421-miic.png\n",
|
||
"SCR-20240803-jctg.png\n",
|
||
"SCR-20241220-svdb.png\n",
|
||
"SCR-20241009-kror.png\n",
|
||
"SCR-20250102-slif.png\n",
|
||
"SCR-20250105-llyq.png\n",
|
||
"SCR-20250413-qxjf.png\n",
|
||
"SCR-20241026-snns.png\n",
|
||
"SCR-20240920-lyry.png\n",
|
||
"SCR-20250309-pugt.png\n",
|
||
"sample3.png\n",
|
||
"SCR-20250122-mwen.png\n",
|
||
"SCR-20250402-pqls.png\n",
|
||
"SCR-20241128-natm.png\n",
|
||
"SCR-20250418-nwzx.png\n",
|
||
"SCR-20250416-nfap.png\n",
|
||
"SCR-20250417-oyqp.png\n",
|
||
"sample2.png\n",
|
||
"SCR-20250416-nsyx.png\n",
|
||
"SCR-20250202-tgue.png\n",
|
||
"SCR-20250414-jfww.png\n",
|
||
"SCR-20250603-mzaf.png\n",
|
||
"chr10\n",
|
||
"SCR-20250410-lrnf.png\n",
|
||
"SCR-20250405-qgej.png\n",
|
||
"SCR-20250309-kuyh.png\n",
|
||
"SCR-20241129-jqtc.png\n",
|
||
"SCR-20251014-tmlf.png\n",
|
||
"SCR-20240924-knse.png\n",
|
||
"SCR-20250219-javx.png\n",
|
||
"bluh\n",
|
||
"SCR-20251115-rffv.jpeg\n",
|
||
"SCR-20250416-jalc.png\n",
|
||
"SCR-20250201-rivh.png\n",
|
||
"SCR-20250319-hnno.png\n",
|
||
"SCR-20250119-ooow.png\n",
|
||
"SCR-20250309-pxbt.png\n",
|
||
"SCR-20240816-ccsg.png\n",
|
||
"SCR-20250219-jeys.png\n",
|
||
"wood_sample_1_spades_out\n",
|
||
"SCR-20241203-lqlu.png\n",
|
||
"SCR-20250303-qxix.png\n",
|
||
"SCR-20240806-qwna.png\n",
|
||
"SCR-20241201-ibuz.png\n",
|
||
"SCR-20250319-hixc.png\n",
|
||
"SCR-20250122-mhbh.png\n",
|
||
"SCR-20250213-jxjg.png\n",
|
||
"SCR-20250220-okoi.png\n",
|
||
"SCR-20250413-qnfy.png\n",
|
||
"SCR-20250117-lldq.png\n",
|
||
"SCR-20241123-ntpx.png\n",
|
||
"SCR-20250311-ofnd.png\n",
|
||
"SCR-20250415-kcgf.png\n",
|
||
"SCR-20250319-jvnx.png\n",
|
||
"SCR-20250216-magv.png\n",
|
||
"CG.png\n",
|
||
"SCR-20241028-olyr.png\n",
|
||
"SCR-20250215-sigg.png\n",
|
||
"SCR-20250215-lwwf.png\n",
|
||
"SCR-20241204-glyn.png\n",
|
||
"SCR-20250202-tijx.png\n",
|
||
"sample4_bbmap.png\n",
|
||
"SCR-20250417-pgsd.png\n",
|
||
"SCR-20250122-mgsn.png\n",
|
||
"SCR-20250429-nknb.png\n",
|
||
"SCR-20250323-lgjf.png\n",
|
||
"SCR-20250414-jmyg.png\n",
|
||
"SCR-20250216-lqhz.png\n",
|
||
"SCR-20250413-pllg.png\n",
|
||
"SCR-20250209-sfvd.png\n",
|
||
"SCR-20250411-tpkn.png\n",
|
||
"SCR-20240805-rvvb.png\n",
|
||
"SCR-20250416-nvbq.png\n",
|
||
"SCR-20250420-rxof.png\n",
|
||
"SCR-20250120-tcqs.png\n",
|
||
"SCR-20250219-lklz.png\n",
|
||
"SCR-20250323-lisj.png\n",
|
||
"SCR-20250213-grby.png\n",
|
||
"SCR-20241023-oksb.png\n",
|
||
"SCR-20250210-uiof.png\n",
|
||
"count_atlast.png\n",
|
||
"SCR-20250202-msqz.png\n",
|
||
"SCR-20250215-rrmm.png\n",
|
||
"plotMA.pdf\n",
|
||
"SCR-20250212-souw.png\n",
|
||
"SCR-20241217-ovmb.png\n",
|
||
"SCR-20241218-tkdy.png\n",
|
||
"SCR-20250218-rggg.png\n",
|
||
"SCR-20250312-qigj.png\n",
|
||
"SCR-20250416-nudr.png\n",
|
||
"SCR-20241110-qtuz.png\n",
|
||
"SCR-20250418-nxbv.png\n",
|
||
"sample1_megahit.png\n",
|
||
"SCR-20250410-pfhj.png\n",
|
||
"SCR-20250411-tzeo.png\n",
|
||
"SCR-20250117-llpb.png\n",
|
||
"SCR-20240803-iuga.png\n",
|
||
"SCR-20241201-ibxk.png\n",
|
||
"SCR-20240805-iulc.png\n",
|
||
"SCR-20250215-rvcu.png\n",
|
||
"SCR-20250221-nvjj.png\n",
|
||
"SCR-20250202-kaeb.png\n",
|
||
"SCR-20250309-ptja.png\n",
|
||
"SCR-20250207-scsq.png\n",
|
||
"SCR-20250117-kkme.png\n",
|
||
"SCR-20241217-bltj.png\n",
|
||
"SCR-20250209-rjpb.png\n",
|
||
"SCR-20250416-omkb.png\n",
|
||
"SCR-20250414-miod.png\n",
|
||
"SCR-20250117-mmjq.png\n",
|
||
"SCR-20240807-ldpg.png\n",
|
||
"SCR-20250416-nenx.png\n",
|
||
"SCR-20240805-tare.png\n",
|
||
"SCR-20250411-tdbm.png\n",
|
||
"SCR-20250328-jize.png\n",
|
||
"SCR-20250104-jtqk.png\n",
|
||
"SCR-20250503-pnli.png\n",
|
||
"SCR-20250215-renn.png\n",
|
||
"SCR-20250322-matz.png\n",
|
||
"SCR-20250217-khfn.png\n",
|
||
"graph_sample3_spades.png\n",
|
||
"SCR-20250303-qkzc.png\n",
|
||
"SCR-20250411-swci.png\n",
|
||
"SCR-20250210-ocxu.png\n",
|
||
"SCR-20250413-qcvl.png\n",
|
||
"SCR-20250312-ntsb.png\n",
|
||
"SCR-20250415-nvdk.png\n",
|
||
"SCR-20250322-tgff.png\n",
|
||
"SCR-20250201-rqni.png\n",
|
||
"SCR-20250202-syuz.png\n",
|
||
"SCR-20250701-lzlx.png\n",
|
||
"SCR-20241219-mvoe.png\n",
|
||
"Archive.zip\n",
|
||
"SCR-20250404-nwhk.png\n",
|
||
"SCR-20250411-tkhw.png\n",
|
||
"SCR-20250217-mksz.png\n",
|
||
"SCR-20250106-oilr.png\n",
|
||
"SCR-20250309-qatv.png\n",
|
||
"SCR-20250322-suab.png\n",
|
||
"SCR-20250218-psyh.png\n",
|
||
"SCR-20250517-kijk.png\n",
|
||
"SCR-20241204-kflq.png\n",
|
||
"SCR-20250111-owhx.png\n",
|
||
"SCR-20250411-otco.png\n",
|
||
"SCR-20241204-kjve.png\n",
|
||
"SCR-20250128-lozv.png\n",
|
||
"SCR-20250807-uadi.png\n",
|
||
"SCR-20250413-qetu.png\n",
|
||
"SCR-20250103-kdav.png\n",
|
||
"SCR-20250328-lrgo.png\n",
|
||
"SCR-20250201-nnex.png\n",
|
||
"SCR-20240807-mybz.png\n",
|
||
"SCR-20250303-qhjm.png\n",
|
||
"SCR-20250403-noih.png\n",
|
||
"SCR-20250323-kjol.png\n",
|
||
"SCR-20240916-rbni.png\n",
|
||
"SCR-20250420-qekc.png\n",
|
||
"SCR-20241226-nmgq.png\n",
|
||
"SCR-20250404-mtpq.png\n",
|
||
"SCR-20250408-kqcs.png\n",
|
||
"SCR-20250105-labv.png\n",
|
||
"SCR-20250319-jssg.png\n",
|
||
"SCR-20250218-jpsi.png\n",
|
||
"SCR-20250510-jakx.png\n",
|
||
"SCR-20240805-hxuc.png\n",
|
||
"SCR-20250207-sare.png\n",
|
||
"SCR-20250415-sjxj.png\n",
|
||
"SCR-20240804-tthv.png\n",
|
||
"SCR-20240920-mitm.png\n",
|
||
"SCR-20250201-uiqd.png\n",
|
||
"SCR-20250417-pjoa.png\n",
|
||
"SCR-20251113-hgvn.png\n",
|
||
"SCR-20240806-qqqd.png\n",
|
||
"SCR-20240924-mhen.png\n",
|
||
"SCR-20240805-tihg.png\n",
|
||
"SCR-20241109-odug.jpeg\n",
|
||
"SCR-20241219-ndli.png\n",
|
||
"SCR-20241110-qgvo.png\n",
|
||
"SCR-20250413-prii.png\n",
|
||
"SCR-20250202-klzi.png\n",
|
||
"SCR-20241204-hiaf.png\n",
|
||
"SCR-20250322-slyt.png\n",
|
||
"SCR-20240923-gsih.png\n",
|
||
"SCR-20250119-rfnw.png\n",
|
||
"SCR-20250118-rtpo.png\n",
|
||
"SCR-20250410-pebz.png\n",
|
||
"SCR-20250310-gtvh.png\n",
|
||
"SCR-20250416-nsqd.png\n",
|
||
"SCR-20250201-swfi.png\n",
|
||
"SCR-20240803-iqru.png\n",
|
||
"SCR-20250310-lwsx.png\n",
|
||
"SCR-20250201-susj.png\n",
|
||
"SCR-20250206-najx.png\n",
|
||
"SCR-20250413-qvyi.png\n",
|
||
"SCR-20250328-mzow.png\n",
|
||
"Archive 2\n",
|
||
"SCR-20250413-ptqh.png\n",
|
||
"SCR-20241003-lbbb.png\n",
|
||
"SCR-20250210-uhjl.png\n",
|
||
"kink_bend.png\n",
|
||
"SCR-20250309-qbgg.png\n",
|
||
"SCR-20251002-mlgy.png\n",
|
||
"SCR-20250212-tkmm.png\n",
|
||
"SCR-20250218-kmyr.png\n",
|
||
"sample2_bbmap.png\n",
|
||
"SCR-20250303-qdrc.png\n",
|
||
"SCR-20250403-njyd.png\n",
|
||
"chr9\n",
|
||
"SCR-20250202-osgc.png\n",
|
||
"SCR-20241219-nwvo.png\n",
|
||
"SCR-20241219-iyxu.png\n",
|
||
"SCR-20250202-tmjn.png\n",
|
||
"SCR-20241219-mydj.png\n",
|
||
"SCR-20240807-ncyq.jpeg\n",
|
||
"SCR-20250217-mzio.png\n",
|
||
"SCR-20250414-kdae.png\n",
|
||
"SCR-20250202-hqeb.png\n",
|
||
"SCR-20241230-lduj.png\n",
|
||
"wood_sample_3_megahit_output_secondtry\n",
|
||
"SCR-20250207-ranw.png\n",
|
||
"sorted.GLDS-251_rna-seq_13JUN2017HiSeq_Run_Sample_235_239_UMISS_Hoeksema_GTTTCG_L003_R1_001_1M.fastq.bam.bai\n",
|
||
"SCR-20250312-ndak.png\n",
|
||
"SCR-20250309-pynb.png\n",
|
||
"SCR-20250322-mfrz.png\n",
|
||
"SCR-20250117-mkdm.png\n",
|
||
"SCR-20250323-kwme.png\n",
|
||
"SCR-20250727-lizu.png\n",
|
||
"SCR-20250330-qyyg.png\n",
|
||
"SCR-20250120-nyva.png\n",
|
||
"wood_sample_3_spades_out\n",
|
||
"SCR-20250207-mhdu.png\n",
|
||
"SCR-20250117-kzll.png\n",
|
||
"SCR-20250218-surt.png\n",
|
||
"SCR-20250420-qarq.png\n",
|
||
"SCR-20250218-mqni.png\n",
|
||
"SCR-20250307-mump.png\n",
|
||
"SCR-20241123-omws.png\n",
|
||
"SCR-20250406-kdah.png\n",
|
||
"SCR-20241204-gmes.png\n",
|
||
"try2_sample5\n",
|
||
"SCR-20250117-miii.png\n",
|
||
"SCR-20250410-pgur.png\n",
|
||
"count_table.txt\n",
|
||
"SCR-20250204-mzde.png\n",
|
||
"sample4_megahit.png\n",
|
||
"SCR-20250405-pdwq.png\n",
|
||
"plotHiCFragment_data.pdf\n",
|
||
"SCR-20250322-taqo.png\n",
|
||
"SCR-20250411-qwxv.png\n",
|
||
"jan.ipynb\n",
|
||
"SCR-20250411-tjob.png\n",
|
||
"SCR-20250323-lflb.png\n",
|
||
"SCR-20250218-kttt.png\n",
|
||
"SCR-20250123-imxu.png\n",
|
||
"SCR-20250216-lwrv.png\n",
|
||
"SCR-20250220-lgdj.png\n",
|
||
"SCR-20250207-ktdd.png\n",
|
||
"SCR-20240804-ohfb.png\n",
|
||
"chr1\n",
|
||
"SCR-20250309-ldep.png\n",
|
||
"SCR-20250625-tndv.png\n",
|
||
"SCR-20250204-jpfx.png\n",
|
||
"SCR-20250303-rcfy.png\n",
|
||
"SCR-20251115-nakw.jpeg\n",
|
||
"SCR-20250319-hgvt.png\n",
|
||
"SCR-20250109-sbxv.png\n",
|
||
"CHH.png\n",
|
||
"SCR-20250417-osnm.png\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"from pathlib import Path\n",
|
||
"path = Path(\"/Users/Aman/Pictures\")\n",
|
||
"for f in path.iterdir():\n",
|
||
" print(f.name)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 4,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"['/Users/Aman/Pictures/SCR-20250414-jufn.png',\n",
|
||
" '/Users/Aman/Pictures/SCR-20241110-puyy.png',\n",
|
||
" '/Users/Aman/Pictures/SCR-20250411-qwcf.png',\n",
|
||
" '/Users/Aman/Pictures/SCR-20250120-nzey.png',\n",
|
||
" '/Users/Aman/Pictures/SCR-20250416-nlpj.png',\n",
|
||
" '/Users/Aman/Pictures/SCR-20250415-kdbe.png',\n",
|
||
" '/Users/Aman/Pictures/SCR-20250119-omsb.png',\n",
|
||
" '/Users/Aman/Pictures/SCR-20250307-nskw.png',\n",
|
||
" '/Users/Aman/Pictures/SCR-20240805-igin.png',\n",
|
||
" '/Users/Aman/Pictures/SCR-20250319-hing.png',\n",
|
||
" '/Users/Aman/Pictures/SCR-20250416-necb.png',\n",
|
||
" '/Users/Aman/Pictures/SCR-20250309-omkr.png',\n",
|
||
" '/Users/Aman/Pictures/SCR-20250422-nwsd.png',\n",
|
||
" '/Users/Aman/Pictures/SCR-20241211-hzfl.png',\n",
|
||
" '/Users/Aman/Pictures/SCR-20250821-kwda.png',\n",
|
||
" '/Users/Aman/Pictures/SCR-20250217-kmmg.png',\n",
|
||
" '/Users/Aman/Pictures/SCR-20250202-tlzo.png',\n",
|
||
" '/Users/Aman/Pictures/SCR-20250206-nafb.png',\n",
|
||
" '/Users/Aman/Pictures/SCR-20250122-scvx.png',\n",
|
||
" '/Users/Aman/Pictures/SCR-20250218-kbje.png',\n",
|
||
" '/Users/Aman/Pictures/SCR-20250411-teqq.png',\n",
|
||
" '/Users/Aman/Pictures/SCR-20241229-kijo.png',\n",
|
||
" '/Users/Aman/Pictures/SCR-20250416-jsck.png',\n",
|
||
" '/Users/Aman/Pictures/SCR-20240924-lrph.png',\n",
|
||
" '/Users/Aman/Pictures/SCR-20250212-thgc.png',\n",
|
||
" '/Users/Aman/Pictures/SCR-20250102-jcyt.png',\n",
|
||
" '/Users/Aman/Pictures/SCR-20250211-szpe.png',\n",
|
||
" '/Users/Aman/Pictures/SCR-20250309-puce.png',\n",
|
||
" '/Users/Aman/Pictures/SCR-20250207-razh.png',\n",
|
||
" '/Users/Aman/Pictures/SCR-20250213-hjzn.png',\n",
|
||
" '/Users/Aman/Pictures/SCR-20250413-qvlg.png',\n",
|
||
" '/Users/Aman/Pictures/SCR-20250116-pzdf.png',\n",
|
||
" '/Users/Aman/Pictures/SCR-20250201-swsg.png',\n",
|
||
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|
||
" '/Users/Aman/Pictures/SCR-20250411-tjob.png',\n",
|
||
" '/Users/Aman/Pictures/SCR-20250323-lflb.png',\n",
|
||
" '/Users/Aman/Pictures/SCR-20250218-kttt.png',\n",
|
||
" '/Users/Aman/Pictures/SCR-20250123-imxu.png',\n",
|
||
" '/Users/Aman/Pictures/SCR-20250216-lwrv.png',\n",
|
||
" '/Users/Aman/Pictures/SCR-20250220-lgdj.png',\n",
|
||
" '/Users/Aman/Pictures/SCR-20250207-ktdd.png',\n",
|
||
" '/Users/Aman/Pictures/SCR-20240804-ohfb.png',\n",
|
||
" '/Users/Aman/Pictures/SCR-20250309-ldep.png',\n",
|
||
" '/Users/Aman/Pictures/SCR-20250625-tndv.png',\n",
|
||
" '/Users/Aman/Pictures/SCR-20250204-jpfx.png',\n",
|
||
" '/Users/Aman/Pictures/SCR-20250303-rcfy.png',\n",
|
||
" '/Users/Aman/Pictures/SCR-20250319-hgvt.png',\n",
|
||
" '/Users/Aman/Pictures/SCR-20250109-sbxv.png',\n",
|
||
" '/Users/Aman/Pictures/SCR-20250417-osnm.png']"
|
||
]
|
||
},
|
||
"execution_count": 4,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"scr[:]"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 5,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"text_scr = []\n",
|
||
"for img in scr[:]:\n",
|
||
" image = Image.open(img)\n",
|
||
"\n",
|
||
" # Extract text\n",
|
||
" text = pytesseract.image_to_string(image)\n",
|
||
" text_scr.append(text)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 6,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"['Generate .pairs and bam files\\n\\nThe pairtools split command is used to split the final .pairsam into two files: .sam (or .bam )\\nand .pairs ( .pairsam has two extra columns containing the alignments from which the Micro-C\\npair was extracted, these two columns are not included in .pairs files)\\n\\npairtools split options:\\n\\nParameter Function\\n\\nOutput pairs file. If the path ends with .gz or .Iz4 the output is pbgzip-/lz4c-\\n-output-pairs compressed. If you wish to pipe the command and output the pairs fils to\\nstdout use - instead of file name\\n\\nOutput sam file. If the file name extension is .bam, the output will be written\\nin bam format. If you wish to pipe the command, use - instead of a file name.\\n\\n-output-sam please note that in this case the sam format will be used (and can be later\\nconverted to bam file e.g. with the command samtools view -bS -@16 -o\\ntemp.bam\\n\\nCommand:\\n\\npairtools split —-nproc-in <cores> —-nproc-out <cores> —-output-pairs <mapped.pairs> \\\\\\n--output-sam <unsorted.bam> <dedup.pairsam>\\n\\nExample:\\n\\npairtools split --nproc-in 8 —-nproc-out 8 --output-pairs mapped.pairs -—-output-sam unsorted. bz\\n\\nThe .pairs file can be used for generating contact matrix\\n',\n",
|
||
" '.\\n\\n@ Vivaldi File Edit View Bookmarks Mail Tools Window’ Help CS @ s} -¥ 3} © €& @)) Q & SunNov 10 18:04\\n© taltech moodle @ alternative to igv browse fi pannzer2 [ Pannzer2 fa ekhidna2.biocenter.helsi li ekhidna2.biocenter.helsi li ekhidna2.biocenter.helsi\\n\\n(a) —_ > YD VY @NotSecure ekhidna2.biocenter.helsinki.fi/barcosel/tmp//S2Z0dlJVpH4/index.html |_a > wo Se ly © F @ @\\n\\nY Speed Dial ¥Y Imported From... Y Imported From... Online Bewerbung QGIS API Docume... qgis - Trying to pe... New Script - Earth... Pastebin.com - #1... TargetP 2.0- DTU... https://www.mood... OnePlus 12R revie... Whois “Indian”in.. v A »\\n\\nJob status: Finished\\n\\nTitle: ecoli_nano\\nProteins: 5204\\nDatabase: uniprot.Oct2024 consisting of 88511531046 letters and 248838886 sequences\\nURL: _http://ekhidna2.biocenter.helsinki .fi/barcosel/tmp//S2Z0d1J V pH4\\nChecksum: 3c404bc04b8e94f66e48ce69ea3b988bd4b7b699bbededd9fc52bSaf\\nSubmitted: Sun Nov 10 19:07:35 EET 2024\\nStarted: Sun Nov 10 19:07:55 EET 2024\\nProcessed: 5204\\nFinished: Sun Nov 10 19:19:54 EET 2024\\nE-mail: _helsinki.o34if@passinbox.com\\n\\nResults\\n\\n¢ HTML summary:\\n© Queries 1 to 1000\\nQueries 1001 to 2000\\nQueries 2001 to 3000\\nQueries 3001 to 4000\\nQueries 4001 to 5000\\nQueries 5001 to 5204\\n¢ Download:\\no Annotations (parseable)\\no DE prediction details\\no GO prediction details\\n¢ Logs:\\no Uploaded sequences\\no STDOUT\\no STDERR\\n\\noO 00 0 0\\n\\nCO en 100% 18:04\\n',\n",
|
||
" '4. scRepertoire on patient 4\\n\\nIn [51]: library(scRepertoire)\\n#51 <- read.delim(\"/home/rstudio/run@71-nsclc-4_VDJ_Dominant_Contigs_AIRR.tsv\", sep = \"\\\\t\", header = TRUE, stringsAs\\n\\n#contig_list <- list(S1)\\n#contig.list <- loadContigs(input = S1,\\n# format = \"AIRR\")\\n\\nIn [60]: contig_list <- loadContigs(\\ninput = \"/home/rstudio/runQ@71\",\\nformat = \"BD\"\\n)\\n\\nIn [61]: combined.TCR <— combineTCR(contig_list,\\nremoveNA = FALSE,\\nremoveMulti FALSE,\\nfilterMulti = FALSE)\\n\\n# output = a list of contig data frames that will be reduced to the reads associated with a single cell barcode. It |\\nhead(combined.TCR[[1]])\\n\\nError: Expecting a string vector: [type=integer; required=STRSXP].\\nTraceback:\\n\\n1. .constructConDfAndParseTCR(data2)\\n\\n2. rcppConstructConDfAndParseTCR(data2 %>% dplyr::arrange(., chain,\\n. cdr3_nt), unique(data2[[1]]))\\n\\n3. stop(structure(list(message = \"Expecting a string vector: [type=integer; required=STRSXP].\",\\n. call = eval(expr, envir), cppstack = NULL), class = c(\"Rcpp::not_compatible\",\\n. \"C++Error\", “error”, \"condition\") ))\\n',\n",
|
||
" 'jf====\\n\\n#\\n#\\n#\\n#\\n#\\n#\\n#\\n#\\n#\\n#\\ni\\n#\\n#\\n\\na\\n\\nAligned_sequences: 2\\n\\n1: Zm00001eb360630_RP\\n\\n2: Zm00001eb360630_RPHt4\\nMatrix: EBLOSUM62\\nGap_penalty: 10.0\\nExtend_penalty: 0.5\\n\\nLength: 460\\n\\nIdentity: 460/460 (100.0%)\\nSimilarity: 460/460 (100.0%)\\nGaps: 0/460 ( 0.0%)\\n\\nScore: 2423.0\\n',\n",
|
||
" \"Dy\\n\\n# Import python package for working with cooler files and tools for analysis\\nimport cooler\\nimport cooltools.Lib.plotting\\n\\n(s] @ 15.3s\\n\\nImportError Traceback (most recent call last)\\n\\nFile ~/anaconda3/envs/cool_notebook/lib/python3.10/site-packages/cooltools/lib/plotting. py:6\\n5 try:\\n\\n----> 6 from matplotlib.cm import register_cmap\\n\\n7 except ImportError:\\n\\nImportError: cannot import name 'register_cmap' from 'matplotlib.cm' (/usr/users/papantonis1/anaconda3/envs/cool_notebook/lib/python3.10/site-packages/matp lot lib/cm. py)\\nDuring handling of the above exception, another exception occurred:\\n\\nModuleNotFoundError Traceback (most recent call last)\\nCell In{5], line 3\\n1 # Import python package for working with cooler files and tools for analysis\\nimport cooler\\nimport cooltools. lib. plotting\\n\\n2\\n3\\n\\n—--->\\n\\nFile ~/anaconda3/envs/cool_notebook/lib/python3.10/site-packages/cooltools/lib/plotting. py:8\\nfrom matplotlib.cm import register_cmap\\n\\nexcept ImportError:\\n\\n—- from matplotlib.colormaps import register\\n10 import matplotlib as mpl\\n11 import matplotlib.pyplot as plt\\n\\nloo Nin\\n\\nis\\n\\nModuleNotFoundError: No module named 'matplotlib.colormaps'\\n\",\n",
|
||
" 'In [596]: combined_TCR <- combineTCR(\\nlist(patient3 = combined.TCR_p3, patient4 = combined. TCR_p4),\\nsamples = c(\"patient3\", \"patient4\"),\\nremoveNA = FALSE,\\n\\nremoveMulti = FALSE,\\nfilterMulti = FALSE\\n)\\nError in (function (..., row.names = NULL, check.rows = FALSE, check.names = TRUE, : arguments imply differing numb\\ner of rows: 2805, 2893, 1328, 1278, 6942, 2747, 8991, 201\\nTraceback:\\n\\n1. .checkContigs(input.data)\\n\\n2. lapply(seq_len(length(df)), function(x) {\\n\\n. df([x]] <- if (!is.data.frame(df[[x]]))\\nas. data. frame(df[[x]])\\n\\nelse df[[x]]\\n\\ndf{(x]] [df {{[x]] == \"\"] <- NA\\n\\n. df [[x]]\\n\\n» )\\n\\n+ FUN(X[[i]], ...)\\n\\nas.data. frame(df[[x]])\\n\\nas.data. frame. list (df[[x]])\\n\\ndo.call(data. frame, c(x, alis))\\n\\n(function (..., row.names = NULL, check. rows\\n\\n. fix.empty.names = TRUE, stringsAsFactors\\n\\n~f{\\n\\nNOURW\\n\\nFALSE, check.names = TRUE,\\nFALSE)\\n\\neS Ce ee\\n\\n',\n",
|
||
" \"Variable population size\\n\\nBeyond the Standard Neutral Model\\n\\nRapid fluctuations ~\\nin population size : > =\\n\\n5 =s(h+aa]\\nN Pau*3\\\\3N2N/4\\n\\n_ ol\\n2P..\\n\\nr\\n\\n=>N\\n\\ne\\n\\nLe\\n5!\\nod\\n\\nin general for period 7°:\\n\\n'\\n\\nharmonic mean population size\\n\",\n",
|
||
" 'Figure 1\\nUniversal Mitotic chromosome folding\\n\\nDecondensation\\n\\nTAD formation by\\nboundary re-activation\\n\\nM Promoter-enhancer\\n. Loop formation within TADs\\nLinearly compressed G2\\nstochastic loop array G1 A/B-compartmentalization\\n\\nby stochastic self assembly\\n\\nMarking of cell type-\\nspecific elements\\n\\nFactor dissociation\\nLoss of interphase folding ee —---__\\\\\\n\\nPromoter-enhancer\\n\\nA/B-compartments TADs\\nloops\\n\\nCell type-specific chromosome folding\\n\\nProposed model for genome folding dynamics during the cell cycle. In interphase genome\\nfolding is defined by locus-specific compartments and chromatin loops. A/B-compartments and\\npromoter-enhancer loops are cell type-specific, whereas topologically associating domains (TADs) are\\nmore tissue-invariant. In prophase many chromatin complexes dissociate from the chromosome, the\\ninterphase chromosome organization is lost and replaced by a locus-independent, universal, and cell\\ntype-invariant mitotic structure. Mitotic chromosomes form longitudinally compressed stochastically\\npositioned loop arrays. Although mitotic chromosome folding is locus-independent and universal,\\nspecific loci, such as TAD boundaries, and cell type-specific elements, such as enhancers, remain\\nmarked. In early Gi the mitotic chromosome decondenses again. Next, TAD boundaries are re-\\nactivated and TADs are re-established. Subsequently, promoter and enhancer re-associate with\\ntranscription factors and other complexes and promoter-enhancer interactions are re-established. At\\nthe same time, groups of active and inactive TADs self-assemble into higher order structures\\ncorresponding to A- and B-compartments, respectively. This model of the order of events is currently\\nhypothetical and based on theoretical considerations (see text). The figure of the mitotic chromosome\\nwas made by Maxim Imakaev, Geoff Fudenberg, Natalia Naumova, and Leonid Mirny.\\n',\n",
|
||
" 'Identification of protein features which determine metalloid-specificity | |||]\\n\\nboric acid arsenous acid\\nHO OH HO OH\\nSa” SS, a“\\nl wil\\nBnaNIP4d GTYFLIFAGCGVVVVNVLYGGIVTFPGICVTWGLIVMVMLYSTGHISGAH\\ns” D4 BnaNIP4a GTYFIIFSGCGVVVVNVLYGGKVTFPGICVTWGLIVMVMLYSVGHISGAH\\na ™é B RK SK EKER KEKE KEK KEEKKKEKKKKEEEREEKKKKE KEKKKKK\\nSs\") Ma\\n% 70 BnaNIP4d FNPAVTLTFAVFRRF PWYQVPLYIGAQLTGSLLGSLTLKLMFHVT PAAYF\\n3 60 BnaNIP4a FNPAVTICFAIFRRFPWYQVPSYIGAQLAGSLLASLTLRLMFKVT PEAFF\\n& 50 ReKKKK: Re REKKKKKKKK REKKKK KKK KERR KKK KEK *s*\\n8 40\\n£ BnaNIP4d GTTPSDSAAQALAAEILISFLLMFVISGVATDNRAVGELAGIAVGMTIIL\\n£ BnaNIP4a GTT PADSAARALVSEIILISFLLMFVISGVATDSRAIGELAGIAVGMTIIL\\nB 10 eee : wkkk kk . PERK KKKKKKKKKKKKKK | ** : EEK EKER\\na\\n° negatiy BNAaNIP4d NVFVAGPVSGASMNPARSLGPAIVMGVYDGLWIYIVGPLVGIMAGGFVYN bys ~nranipe\\ncontr BnaNIP4a NVFVAGPISGASMNPARSLGPAIVMG IWVYIVGPIIGIVAGGFVYN\\nRRR KEKE REKKKKEKKEKEEKKEKK Kk KKKEKKS pee RRR KKK\\n\\nCrop\\nPhysiology\\n73\\n\\nDiehn & Bienert, in preparation\\n',\n",
|
||
" \"In [2]: import numpy as np\\nimport matplotlib.pyplot as plt\\n\\n# Load the Q matrix from ADMIXTURE output\\nq_matrix = np. loadtxt('inp_admix.3.Q')\\n\\n# Create the plot\\nplt. figure(figsize=(10, 5))\\nfor i in range(q_matrix.shape[1]):\\nplt.bar(range(q_matrix.shape[0]), g_matrix[:, i], bottom=np.sum(q_matrix[:, :i], axis=1))\\n\\nplt.title('Ancestry Proportions (K=3)')\\nplt.xlabel('Individuals')\\nplt.ylabel('Ancestry Proportion')\\n\\nplt. show()\\n\\nAncestry Proportions (K=3)\\n\\n104\\n\\n0.87\\n\\n0.64\\n\\n0.44\\n\\nAncestry Proportion\\n\\n0.27\\n\\nie} 10 20 30 40 50\\nIndividuals\\n\\n\",\n",
|
||
" '(new_cooltools) [papantonis1@gwdu1@1 microc_data]$ wget --user-agent=\"Mozilla/5.0\" \"https: //wwwuser.gwdguser.de/~txie/polycomb/nadine_micro/C_ctcfbed.pdf\"\\n—-2025-04-16 12:39:40-- https://wwwuser.gwdguser.de/~txie/polycomb/nadine_micro/C_ctcfbed.pdf\\n\\nResolving wwwuser.gwdguser.de (wwwuser.gwdguser.de)... 134.76.203.116\\nConnecting to wwwuser.gwdguser.de (wwwuser.gwdguser.de) |134.76.203.116|:443...\\nHTTP request sent, awaiting response... 403 Forbidden\\n\\n2025-04-16 12:39:4@ ERROR 403: Forbidden.\\n\\nconnected.\\n',\n",
|
||
" 'Article | Open access | Published: 14 June 2019\\n\\nChromatin interaction maps reveal genetic regulation\\nfor quantitative traits in maize\\n\\nYong Peng, Dan Xiong, Lun Zhao, Weizhi Ouyang, Shuangqi Wang, Jun Sun, Qing Zhang, Pengpeng\\n\\nGuan, Liang Xie, Wenqiang Li, Guoliang Li@, Jianbing Yan ™ & Xingwang Li4\\n\\nNature Communications 10, Article number: 2632 (2019) | Cite this article\\n\\n> BMC Genomics. 2021 Jan 6;22:23. doi: 10.1186/s12864-020-07324-0 4\\n\\nChromatin loop anchors contain core structural components of the gene\\nexpression machinery in maize\\n\\nGina Zastrow-Hayes 1, Gregory D May?\\n\\n',\n",
|
||
" 'Extracting Hi-C contact matrix from.hic file\\n\\nThe process obtains the hic contact matrix for each chromosome from the.hic file. It will output the\\nfrequency_matrix file.\\n\\nModify the path to the input and output files in the GetBigMatrix_Cells_KRobserved.sh file: The.jar file is the path\\nwhere the juicer tools resides, and run:\\n\\nbash GetBigMatrix_Cells_KRobserved.sh O\\n\\nGenerating sub-matrix from Hi-C contact matrix\\n\\nThe process cuts the hic contact matrix of each chromosome into multiple submatrices. Modify the path to the\\ninput and output files in the Getnpymatrix_chr_all_sample.sh file, where the input file is the output file from the\\nprevious step, DPATH is the root directory of the frequence_matrix file, and run:\\n\\nbash Getnpymatrix_chr_all_sample.sh oO\\n',\n",
|
||
" 'a) = > QO. VU VV Iocalhost:8090/app Q yv @® Search Google ’ a ¢ oO et © &\\n\\nY Speed Dial yY Imported From... Y Imported From... Online Bewerbung QGIS API Docume... qgis - Trying to pe... New Script - Earth... Pastebin.com- #1... TargetP 2.0- DTU... https://www.mood... OnePlus 12R revie... Whois “Indian”in.. Yv A\\n\\n@ HiGlass About Blog Examples Plugins Docs ©\\n\\nI no+x\\n+X) sega\\n\\n3e+4\\nte+4\\n4e+3\\n\\n1e+3\\n4e+2\\n\\nte+2\\n40\\n\\n10\\n4\\n\\n4 -\\n\\ndata.mcool\\n[Current data resolution: 5.12M],\\n',\n",
|
||
" 'View’ Edit Window’ Help\\n\\n=> Q S_~ Thu 21. Aug 12:01\\n\\n© ™ Topics for HiWis | BioDatA x Collection of topics for stu: x [J Acommunity-consensus rex @ New Tab SN Figure 1 | A Practical Prote x EJ Context-SpecificGenomes* + v VAX\\n\\n€ 7G Oo Ay\\n\\nFez Oinbox CMArbeit PhD OSport gill Prof.GarySawers [ Christian R.Eckmann lJ Prof. Dr. Robert Paxton lj Martin Schattat a? Frontiers | Networks ... Network thermodyna...\\n\\nw.mdpi.com/2218-1989/13/1/126 ag\\n\\nfe\\n\\nes * p=\\n\\nTable 2. An overview of algorithms for automated reconstru specific models. Abbreviations: LP—linear programming; RMF—required metabolic function.\\n\\nAlgorithm Reference Family Input Data Comments\\nGIMME Becker etal, 2008 (38) © transcriptomics c owa threshold w\\n\\nGIMME-\\n\\nGIMMEp dbar et al., 2012 [44] like\\n\\nGIMME\\n\\n‘Schmidt etal, 2013 [45] like No thresholding\\n\\nGIMME-\\n\\nRIPTIDe 2020 [46] ik a e flux values, no thresholding,\\n\\nimat 110 [47] iMAT-Ike iptomics, prot on a sion profiles, no RMF.\\n\\ntal evid\\nini 2012 (48) iMATHIke transcriptomies, proteomics, metabolomics (qualitative) at evider\\n\\n. prior knowledge, transcriptomies, proteomics, metabolomic\\nUNIT Agren et al., 2014 [49] iMAT-Ike\\n(qualita\\nLee et al., 2012 [50] iMAT-Iike transcriptomics u jon data (RNA-seq).\\niMATike transcriptomi a expression data (RNA-seq) and regularisation.\\n\\n0105 prior knowledge, transcriptomics, proteomics, metabolomics, Removes non-core reactions and checks model consistency for core\\nae fluxomi reactions,\\n\\n2012 [53] criptomics, metabolomics Different reaction scores to determine core reactions\\n\\nind a minimal set\\nreactions,\\n\\n2014 [40] e f core reactions,\\n\\nhand Boyd, 2020, «d runtime and network compactness in comparison to\\n\\nFASTCORE.\\n\\nMBALike set of core reactions\\n\\nMBA-lke transcriptomics ‘CORE workflow for microarray data,\\n\\nPires Pacheco at al\\n\\nIFASTCORMICS a2) MBAAike transcriptomics FASTCORE workflow for RNA-seq data,\\n\\n0 at al, 2022\\nsoFASTCORMICS = transcriptomies FASTCORE workflow for scRNA-seq data,\\nSchulte and Qutub, 2016\\n\\nCORDA is MBAGike core reactions Does not require to remove all non-core react\\nJensen and Papin, 2011\\n\\nMADE eed MADE-I transcriptomies ntfes reaction activities in a sequence of measure!\\n\\nZhang et al, 2019 [57] MADE-lke transcriptomies ‘Sequentially pushes the constraints\\n\\ntent and minimal solution of flux di\\netal, 2021 [58] MADE-lke transcriptomies na\\n\\nconditions.\\n\\n',\n",
|
||
" 'library(tximport)\\n\\n# Define paths to trimmed reads and extract sample names\\nfastq_files <- list.files(path = \"/mnt/volume/data/group8/studies/trimmed\", pattern = \"*_1.fastq.gz\", full.names = TRUE\\nsamples <- basename(fastq_files) %>% sub(\"_1\\\\\\\\.fastq\\\\\\\\.gz\", \"\", «\\n\\n# Create paths to Kallisto abundance files\\nfiles <- file.path(\"/mnt/volume/data/group8/kallisto_output\", samples, \"abundance.tsv\")\\nnames(files) <- samples\\n\\n# Check that all files exist\\n\\nmissing_files <- files[!file.exists(files)]\\n\\nif (length(missing_files) > 0) {\\ncat(\"Warning: The following abundance.tsv files are missing:\\\\n\")\\nprint(missing_files)\\n\\n} else {\\ncat(\"All abundance.tsv files found.\\\\n\")\\n\\n}\\n\\n# Load the tx2gene mapping\\ntx2gene <- read.csv(\"/mnt/volume/data/group8/references/tx2gene.csv\")\\n\\n# Run tximport to summarize counts to gene level\\ntxi <- tximport(files, type = \"kallisto\", tx2gene = tx2gene\\n\\n# Check the structure of the imported object\\nstr(txi)\\n\\n# Save gene-level counts to a CSV file\\n\\nwrite.csv(txi$counts, file = \"/mnt/volume/data/group8/kallisto_output/gene_counts.csv\", row.names = TRUE)\\nwrite.csv(txig$abundance, file = \"/mnt/volume/data/group8/kallisto_output/gene_counts_abundance.csv\", row.names = TRUE\\ncat(\"Gene-level counts saved to /mnt/volume/data/group8/kallisto_output/gene_counts.csv\\\\n\")\\n\\n3] @ Os\\n\\nAll abundance.tsv files found\\n\\nError in tximport(files, type = \"kallisto\", tx2gene = tx2gene): length(files) > @ is not TRUE\\nTraceback:\\n\\n1. stopifnot(length(files) > 0)\\n2. stop(simpleError(msg, call = if (p <- sys.parent(1L)) sys.call(p)))\\n',\n",
|
||
" '© multigc\\n\\nv1.25.2\\n\\nGeneral Stats\\n\\nFastQC\\n\\nSequence Counts\\n\\nSequence Quality Histograms\\nPer Sequence Quality Scores\\nPer Base Sequence Content\\nPer Sequence GC Content\\n\\nPer Base N Content\\n\\nSequence Length Distribution\\nSequence Duplication Levels\\nOverrepresented sequences by sample\\nTop overrepresented sequences\\nAdapter Content\\n\\nStatus Checks\\n\\nSoftware Versions\\n\\nFastQC Version: @.11.4\\n\\nQuality control tool for high throughput sequencing data. URL: http://www.bioinformatics.babraham.ac.uk/projects/fastqc\\n\\nSequence Counts @ Help\\n\\nSequence counts for each sample. Duplicate read counts are an estimate only.\\n\\nPercentages Export Plot\\n\\nFastQC: Sequence Counts\\n20 samples\\n\\nwood_sample_1_forward_paired MM Unique Reads\\n\\nwood_sample_1_forward_unpaired I Duplicate Reads\\n\\nwood_sample_1_reverse_paired\\nwood_sample_1_reverse_unpaired\\nwood_sample_2_forward_paired\\nwood_sample_2_forward_unpaired\\nwood_sample_2_reverse_paired\\nwood_sample_2_reverse_unpaired\\nwood_sample_3_forward_paired\\nwood_sample_3_forward_unpaired\\nwood_sample_3_reverse_paired\\nwood_sample_3_reverse_unpaired\\nwood_sample_4_forward_paired\\nwood_sample_4_forward_unpaired\\nwood_sample_4_reverse_paired\\nwood_sample_4_reverse_unpaired\\nwood_sample_5_forward_paired\\nwood_sample_5_forward_unpaired\\n\\nwood_sample_5_reverse_paired\\n\\nwood_sample_5_reverse_unpaired\\n\\n°\\n\\n20k 40k 60k 80k 100k 120k 140k 160k\\n\\na\\noe\\ns\\n~\\n\\nNumber of reads\\nCreated with MultiQC\\n\\nC) 9 tr I- @ > » Toolbox\\n',\n",
|
||
" 'Mean Methylation Levels - CG Context Mean Methylation Levels - CHG Context Mean Methylation Levels - CHH Context\\n0.03:\\n\\n018.\\n\\n0.020\\n\\n0.02,\\n\\nMean Methylation Level\\n\\nMean Methylation Level\\n\\nMean Methylation Level\\n\\n0.00:\\n\\n0.000\\n\\nFile\\n\\nFile File\\n',\n",
|
||
" '[3]:\\n\\nild —-threads 16 /data/proj2/home/students/pst14/ref_gen/sample3/ncbi_dataset/data/GCF_932294415.1/GCF_932294415.1_dhQueRobu3.1_genomic.fna GCF\\n\\nnumSides: 4111073\\nnumLines: 4111073\\nebwtTotLen: 263108672\\nebwtTotSz: 263108672\\ncolor: 0\\n\\nreverse: 1\\n\\nTotal time for backward call to driver() for mirror index: 00:06:07\\n\\nRenaming GCF_932294415.1_dhQueRobu3.1_genomic. fna.3.bt2.tmp to GCF_932294415.1_dhQueRobu3.1_genomic. fna.\\nRenaming GCF_932294415.1_dhQueRobu3.1_genomic. fna.4.bt2.tmp to GCF_932294415.1_dhQueRobu3.1_genomic. fna.\\nRenaming GCF_932294415.1_dhQueRobu3.1_genomic. fna.1.bt2.tmp to GCF_932294415.1_dhQueRobu3.1_genomic. fna.\\nRenaming GCF_932294415.1_dhQueRobu3.1_genomic. fna.2.bt2.tmp to GCF_932294415.1_dhQueRobu3.1_genomic. fna.\\nRenaming GCF_932294415.1_dhQueRobu3.1_genomic. fna.rev.1.bt2.tmp to GCF_932294415.1_dhQueRobu3.1_genomic.\\n\\nRenaming GCF_932294415.1_dhQueRobu3.1_genomic. fna.rev.2.bt2.tmp to GCF_932294415.1_dhQueRobu3.1_genomic.\\nreal 11m47.826s\\n\\nuser 142m49. 480s\\n\\nsys 1m1. 386s\\n\\n3.bt2\\n4.bt2\\n1.bt2\\n2.bt2\\nfna.rev.1.bt2\\nfna.rev.2.bt2\\n\\n[4]:\\n\\nls -lh /data/proj2/home/students/pst14/ref_gen/sample3/ncbi_dataset/data/GCF_932294415.1/\\n\\ntotal 2.6G\\n\\n-rw------— 1 pst14 pst14 98M Jan 17 12:08 cds_from_genomic.fna\\n\\n-rw------- 1 pst14 pst14 763M Jan 17 12:07 GCF_932294415.1_dhQueRobu3.1_genomic. fna\\n—rw-rw-r-— 1 pst14 pst14 1.9K Jan 18 14:41 GCF_932294415.1_dhQueRobu3.1_genomic. fna.amb\\n—rw-rw-r-— 1 pst14 pst14 14K Jan 18 14:41 GCF_932294415.1_dhQueRobu3.1_genomic. fna.ann\\n—rw-rw-r-— 1 pst14 pst14 753M Jan 18 14:41 GCF_932294415.1_dhQueRobu3.1_genomic. fna. bwt\\n—rw-rw-r-— 1 pst14 pst14 189M Jan 18 14:41 GCF_932294415.1_dhQueRobu3.1_genomic. fna.pac\\n-rw-rw-r-— 1 pst14 pst14 377M Jan 18 14:45 GCF_932294415.1_dhQueRobu3.1_genomic. fna.sa\\n-rw------— 1 pst14 pst14 242M Jan 17 12:08 genomic.gff\\n\\n-rw------— 1 pst14 pst14 32M Jan 17 12:08 protein. faa\\n\\n-rw------— 1 pst14 pst14 146M Jan 17 12:08 rna.fna\\n\\n-rw------— 1 pst14 pst14 29K Jan 17 12:08 sequence_report.jsonl\\n\\n',\n",
|
||
" '[18]\\n\\nimport pandas as pd\\n\\nbatch_file = \"/mnt/volume/data/group8/references/Mercator_Mapped_file.csv\"\\ndf = pd. read_csv(batch_file)\\n\\nprint(df.head())\\n\\nprint (df.columns)\\n\\n® 0.0s\\n\\nParserError Traceback (most recent call last)\\n\\nCell In[18], line 4\\n\\nimport pandas as pd\\n\\nbatch_file = \"/mnt/volume/data/group8/references/Mercator_Mapped_file.csv\"\\ndf = pd. read_csv(batch_file)\\n\\nprint (df.head())\\n\\nprint (df.columns)\\n\\nLl\\n\\n---->\\n\\nIn I IS WW\\n',\n",
|
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" 'In [122]: head(combined_seurat@meta. data)\\n\\nA data.frame: 6 x 8\\n\\nlent nCount_RNA nFeature_RNA percent.mt nCount_SCT nFeature SCT integrated_snn_res.0.5 seurat_clusters\\n\\n<chr> <dbi> <int> <dbi> <dbi> <int> <fct> <fct>\\n\\n4100 zehn_dataset 23935, 5941 4.904951 1729 1148 4 4\\n11356 zehn_dataset 1299 836 4.080062 1489 835 1 1\\n21277 = zehn_dataset 2243 1335 6.776638 2090 1332 2 2\\n30102 zehn_dataset 2860 1681 5.524476 2205 1654 4 4\\n41586 zehn_dataset 1933, 1123 7.604759 1908 1121 1 1\\n41975 zehn_dataset 925 607 9.081081 1474 619 3 3\\n\\nIn [123]: table(combined_seurat$orig. ident)\\n\\nzehn_dataset\\n\\n3483\\n',\n",
|
||
" '@ Mainwindow ma @O+rzk@0C€¢ = S SunDec 29 11:22\\n\\n[ Rem ) [Juicebox 2.17.00] Hi-C Map <9>: data.allValidPairs.hic\\n\\nFile View Bookmarks Assembly Dev\\nChromosomes\\n\\n6 @ « Ge\\n\\nterre I A\\nBencmewi Brhnoeis+ Mh AT\\nCoverage i CIN\\nCoverage a i\\nBabaowdss i\\n\\nne. a whee gg\\n\\nColor Range\\na |\\n\\n137\\n\\nNormalization (Obs | Ctrl)\\n\\nShow\\n\\nNone None\\n\\nCOCO i\\nHAVANA\\nAAA\\nOO AAA\\nee\\nasin ssl Leltont aiabeteiibnmiliat,\\n\\nit |\\n\\nObserved\\n\\n—|\\n\\nEigenvector\\n\\nOMB 20 MB 40 MB 60 MB 80 MB 100 MB 120 MB 140 MB 160 MB 180 MB\\n| | | | | | | | | |\\n\\n6:92,500,001-93,000,000\\n\\n192,500,000-93,000,000\\n\\nalue: 0.82\\n\\nInter Balanced++\\n4123,500,000-124,000,000\\nbin: 247\\n\\nalue: 1.135\\nGenome-wide Balanced++\\n4123,500,000-124,000,000\\nbin: 247\\n\\nalue: 1.137\\n\\nCoverage\\n\\nLayerO << | & |\\n\\nShow Annotation Panel\\n\\n',\n",
|
||
" 'EXPLORER\\n\\n\\\\ AMAN [SSH: SCC]\\n\\n> hic-pro-git\\n\\n> mustache-git\\n\\nchrom.sizes\\n\\ncool_balance.sh\\nGEO2457_5kb_mustache_loops.bedpe\\nGEO2457_5kb.cool\\nGEO2457_dots_5kb.bedpe\\nGEO2457_expected_1kb.tsv\\nGEO2457_expected_5kb.tsv\\nGEO2457_v2.mcool\\n\\nGEO2457.hic\\nGEO2459_5kb_mustache_loops.bedpe\\nGEO2459_5kb.cool\\nGEO2459_expected_5kb.tsv\\nGEO2459_v2_expected_cis.tsv\\nGEO2459_v2.mcool\\n\\nGEO2459.hic\\n\\n$ hic2cool_aman.sh\\n\\n$ test.sh\\n\\nmY 6 oO DB\\n\\n6]\\n\\n@\\n\\nhy «D “OUTLINE\\n\\nPP aman [SSH: SCC]\\n\\nShow All Commands\\nGo to File\\n\\nFind in Files\\n\\nToggle Full Screen\\n\\nShow Settings\\n',\n",
|
||
" 'Self-fertilization TM\\n\\nParents AA x aa Hetero- Homo-\\nJ zygosity zygosity\\n\\nAa «Aa\\n\\n— !~ may\\n\\nF, generation 50\\n\\nF, generation\\n\\nF, generation\\n\\nF, generation\\n\\n¢,corerion A ss\\n\\nProf. Chns-Carolin Schon (TUM) | Plunt Brooding\\n\\nF, versus DH\\n\\n',\n",
|
||
" '@ ZoomWorkplace Meeting View Edit Window Help *- © ? © & W Se Wed Feb 12 22:18\\n\\nee <@ Meeting @® Ratula Ray\\'s screen\\n\\n(=| Layout » C O ai va\\n— 7 LJ b SP Y\\n| Reset > : 82 Replace v\\nNew 5 \" Arrange Create PDF Create PDF and Add-ins\\n\\nSlide v & Section v _ , I$ Select and Share link Share via Outlook\\nClipboard LS] Slides Paragraph Drawing Editing Adobe Acrobat Add-ins\\n\\nIs this increase in length along the PD axis of epidermis Patch A\\ncoming from increase in cell number or increase in tissue epidermis\\n\\nIncrease in length along the PD =»\\nexpansion?\\n\\naxis of the epidermis (patch B) atch B\\natc\\nepidermis\\n\\nTotal cell number Cumulative cell volume Cumulative cell area\\n(patch B epidermis) (patch B epidermis) (patch B epidermis)\\n\\n(patch B)\\n(patch B)\\n\\nNormalized PD coordinate intervals\\n\\nNormalized PD coordinate intervals\\n\\nNormalized PD coordinate intervals\\n(patch B)\\n\\nTotal cell number (patch B) Rescaled cell volume (patch B) Rescaled cell area (patch B)\\nMm Stage 2-IIl\\n\\nStage 2-IV\\nFor each PD axis interval > from stage 2-IIl to 2-V > there is mostly an increase in both mmm Stage 2-V\\n\\ne3 2 ©) 1)\\n\\nParticipants\\n\\n',\n",
|
||
" '#Update on the script\\n\\n3. Plot\\nDraw the Chalazal base and the line\\nspecifying the neck of the Nucellus\\n\\n1. Import\\nDrag and drop\\n\\n4. Measure\\n\\nSolves for two intersecting perpendiculars.\\n\\nObtains the intersection coordinates.\\n\\nAutomatically measures the kink angle at the intersection.\\n\\n2. Filter\\nBased on the image\\n\\nDraws two perpendiculars to each * 5. Save\\nof these 2 reference lines + Automates saving of ROIs for lines, points, and\\nangles in the ROI manager.\\n+ Saves a screenshot of the final image with the\\nattempt number and timestamp.\\n\\n',\n",
|
||
" 'A (effectors) ,(effectors) B\\n0.00 0.25 0.50 0.75 1.00 0.00 0.05 0.10 0.15 0.20\\n\\n10 > >\\n>! >s5 2 1.0 2\\na @ 3 3s\\n3° g°\\n5\\n1 5\\n05\\n2 10\\n15\\n= = 0.0 0)\\n0.00 0.25 0.50 0.75 1.00 0.00 0.05 0.10 0.15 0.20 0.50 -0.25 0.00 0.25 0.50 0.05 0.00 0.05 0.10\\na(non—effectors) @,(non — effectors) a(effectors) — o(non — effectors) a(effectors) — o,(non—effectors)\\nc Genes [EB eteciors FE non-otectrs D Genes — efeciors — non-ofectrs Genes — efeciors — non-efctors\\n\\n. 100: 1605:\\n\\nos\\n\\nHs a wo\\n\\n2 6 10 % i 2 26 30\\n\\n=i00 80-80-70 00-80-40 30 20 0 6 40 20 30 02 os qo Zo 0.02 00050010 0020 0050 0.100\\n\\nNes. 8\\n\\nFigure 3. Comparison of the rate of adaptive evolution and distribution of fitness effects in effector and non-effector genes. (A)\\nComparison of the estimates of the proportion of adaptive substitutions « and the rate of adaptive substitutions, w, for genes predicted\\nto encode effector proteins (blue) or not (grey). Histograms (white bars), kernel density plots, and box-and-whiskers charts are computed\\nover 100 bootstrap replicates in each case (see Material and Methods). (B) Null distributions of the differences of « and w, between\\neffectors and non-effector genes (grey histogram) and the corresponding observed statistics (red line). (C) Average distribution of fitness\\neffects (P), computed as the product of the effective population size Ne and selection coefficient s, over 100 bootstrap replicates for\\nboth effector and non-effector encoding genes. (D) Correlation of inferred parameters over 100 bootstrap replicates of effector and\\nnon-effector encoding genes, for the Gamma (negative selection) and Exponential (positive selection) components, respectively. y and\\n8: the mean and shape of the Gamma distribution of negative selection coefficients. e, mean of the exponential distribution of positive\\nselection coefficients; y, the probability that the selection coefficient is positive.\\n',\n",
|
||
" 'pwd\\n\\nls -ltrh /mnt/storage3/aman/jdump\\n\\n[18]\\n\\nZhome/aman\\ntotal 553M\\n\\n-rw-rw-r-- 1 aman\\n\\n=rw-rw-\\n\\n—rw—rw-\\n—rw—rw-\\n—rw—rw-\\n—rw—rw-\\n—rw—rw-\\n—rw—rw-\\n—rw—rw-\\n—rw—rw-\\n\\n1\\n\\naman\\naman\\naman\\naman\\naman\\naman\\naman\\naman\\naman\\n\\naman\\naman\\naman\\naman\\naman\\naman\\naman\\naman\\naman\\naman\\n\\n90M\\n68M\\n66M\\n71M\\n59M\\n41M\\n46M\\n43M\\n38M\\n37M\\n\\nJan\\nJan\\nJan\\nJan\\nJan\\nJan\\nJan\\nJan\\nJan\\nJan\\n\\neoovovveovvvove\\n\\n19:\\n19:\\n19:\\n19:\\n19:\\n19:\\n19:\\n19:\\n19:\\n19:\\n\\n29\\n29\\n29\\n29\\n29\\n29\\n29\\n29\\n29\\n29\\n\\n1_1_matrix. txt\\n2_2_mat rix.txt\\n3_3_matrix.txt\\n4_4 matrix.txt\\n5_5_matrix.txt\\n6_6_matrix.txt\\n7_T_matrix.txt\\n8_8 _matrix.txt\\n\\n9_9_matrix.txt\\n\\n10_10_matrix.txt\\n\\n',\n",
|
||
" 'Methods\\n\\n1. 3D imaging — Confocal Microscopy\\n2. Analysis tools -MorphoGraphx, ImageJ\\n3. Semi —- automated pipeline for kink angle quantification\\n\\n',\n",
|
||
" 'Normalized PD coordinate intervals\\n\\n(patch B)\\n\\n[0.75, 1.00]\\n\\n[0.50, 0.75]\\n\\n[0.25, 0.50]\\n\\n{0.00, 0.25]\\n\\nlm Stage 2V\\nlm Stage 2-II\\nlm Stage 21\\n\\n0.00 025 050 O75 1.00 125 150\\n\\nAvg. max/min ratio\\n\\n175 2.00\\n',\n",
|
||
" 'In [417]: combined_TCR <- combineTCR(\\nlist(patient3 = combined.TCR_p3, patient4 = combined.TCR_p4),\\nsamples = c(\"patient3\", \"patient4\"),\\nremoveNA = FALSE,\\nremoveMulti = FALSE,\\nfilterMulti = FALSE\\n)\\n\\nError in ‘mutate()*:\\n\\ni In argument: “TCR1 = ifelse(...)*.\\nCaused by error:\\n\\n! object \\'chain\\' not found\\nTraceback:\\n',\n",
|
||
" 'Epidermal layer\\n\\nCell Distance\\n\\nArea\\n\\nElongation\\nratios (max/min)\\n\\n| A\\n2V_COMPLETE\\n\\n|AREA\\n\\nVOLUME\\n\\nIMAXMIN\\n\\nIm\\n\\ni21V_COMPLETE\\nAREA\\n\\n(OLUME\\n\\nIM\\n\\nIM\\n\\n2\\n\\n|AREA\\nVOLUME\\n\\nIM\\n\\nIMAXMID\\n\\n[os | oc | io E\\n00.25 0.25-0.50 0500.75 _0.751.0\\n33.15772607 3827197343 23.64799146 10.92426767\\n3135001999 39, 15457599 24.53088161 10.96503692\\n(0.706271566 0,995108725 1.192089571 1.181832515\\n(0.732093524 0.931383858 1.266275197 1.096748819\\n\\n0-0.25 0.25-0.50 0500.75 _0.75-1.0\\n\\n19.24792115 23,49681932 10.94572609 8.722510114\\n18.18032267 23.80134919 11.80305564 8.888929006\\n(0.893448068 1,017750253 1.0180320991.131369444\\n0.902037793 0.954909091 0.966313675 1.244435577\\n\\n0-0.25 0.25-0.50 0.500.795 _ 0.75-1.0\\n\\n16.28634765 12.20109527 10.94945835 2.736923127\\n16.16750129 12.75007749 10.70985075 2.372292061\\n(0.918495544 0,946698623 1.136496971.195292929\\n(0.920755369 0.961650794 1.1124675321.198502646\\n',\n",
|
||
" 'Sample # Contigs | Largest Contig | Total Length | GC% | N50 L50 | N90 L90\\nSample1 | 3 90525 135394 35.69 | 90525 | 1 19048 | 3\\nSample 2 | 3 86962 132228 36.96 | 86962 | 1 18214 | 3\\nSample 3 | 3 90511 135343 35.70 | 90511 1 18997 | 3\\nSample 4 | 3 87225 131459 36.30 | 87225 | 1 18324 | 3\\nSample 5 | 3 86372 131640 36.88 | 86372 | 1 18374 | 3\\n\\n',\n",
|
||
" 'In [39]: contig_list <- loadContigs(\\ninput = \"/home/rstudio/rund71\",\\nformat = \"BD\"\\n)\\n\\nIn [40]: combined. TCR <- combineTCR(contig_list,\\nremoveNA = FALSE,\\nremoveMulti = FALSE,\\n\\nfilterMulti = FALSE)\\n\\n# output = a list of contig data frames that will be reduced to the reads associated with a single cell barcode. It\\nhead(combined.TCR[[1]])\\n\\nError: Expecting a string vector: [type=integer; required=STRSXP].\\nTraceback:\\n\\n1. .constructConDfAndParseTCR(data2)\\n2. rcppConstructConDfAndParseTCR(data2 %>% dplyr::arrange(., chain,\\ncdr3_nt), unique(data2[[1]]))\\n3. stop(structure(list(message = \"Expecting a string vector: [type=integer; required=STRSXP].\",\\n. call = eval(expr, envir), cppstack = NULL), class = c(\"Rcpp::not_compatible\",\\n. \"C++Error\", “error\", \"condition\") ))\\n',\n",
|
||
" \"#Busco\\n\\n#pwd\\n\\n#mkdir illegal_logging_trees/fastqc_raw/busco_megahit_secondtry\\ncd illegal_logging_trees/fastqc_raw/busco_megahit_secondtry\\n\\npwd\\n\\nfor i in {1..5}; do\\nbusco -i /data/proj2/home/students/pst14/illegal_logging_trees/fastqc_raw/wood_sample_${i}_megahit_output_secondtry/final.contigs.fa -m genome -o BUSCO_megahit_output_secondtry -c 8 -f\\n\\ndone\\nbash\\nbash: cd: illegal_logging_trees/fastqc_raw/busco_megahit_secondtry: No such file or directory\\ndata/proj2/home/students/pst14/illegal_ logging _trees/fastqc_ raw/busco megahit secondtry\\n2025-01-08 18:29:37 INFO: soe Start a BUSCO v5.8.2 analysis, current time: @1/08/2025 18:29:37 x\\n2025-01-08 18:29:37 INFO: Configuring BUSCO with local environment\\n2025-01-08 18:29:37 WARNING: Running Auto Lineage Selector as no lineage dataset was specified. This will take a little longer than normal. If you know what lineage dataset you want to use, please specify this in the config f\\n2025-01-08 18:29:37 INFO: Running genome mode\\n2025-01-08 18:29:38 INFO: Downloading information on latest versions of BUSCO data...\\n2025-01-08 18:29:48 INFO: Input file is /data/proj2/home/students/psti4/illegal_logging_trees/fastqc raw/wood sample _1 megahit output _secondtry/final.contigs.fa\\n2025-01-08 18:29:48 INFO: No lineage specified. Running lineage auto selector\\n2025-01-08 18:29:48 INFO: see Starting Auto Select Lineage eK\\n\\nThis process runs BUSCO on the generic lineage datasets for the domains archaea, bacteria and eukaryota. Once the optimal domain is selected, BUSCO automatically attempts to find the most appropriate BUSCO dataset to use\\n--auto-lineage-euk and --auto-lineage-prok are also available if you know your input assembly is, or is not, an eukaryote. See the user guide for more information.\\nA reminder: Busco evaluations are valid when an appropriate dataset is used, i.e., the dataset belongs to the lineage of the species to test. Because of overlapping markers/spurious matches among domains, busco matches i |\\n\\n2025-01-08 18:29:48 WARNING: The auto-lineage pipeline is not yet available using the parent dataset eukaryota_odb12. Reverting to eukaryota_odb10.\\n2025-01-08 18:29:48 INFO: Downloading file 'https://busco-data.ezlab.org/v5/data/lineages/archaea_odb12.2024-11-14.tar.gz'\\n2025-01-08 18:29:49 INFO: Decompressing file '/data/proj2/home/students/pst14/illegal_logging_trees/fastqc_raw/busco_megahit_secondtry/busco_downloads/lineages/archaea_odb12.tar.gz'\\n\\n2025-01-08 18:\\n2025-01-08 18:\\n2025-01-08 18:\\n\\n:14 INFO: Running BUSCO using lineage dataset archaea_odb12 (prokaryota, 2024-11-14)\\n:14 INFO: Running 1 job(s) on bbtools, starting at 01/08/2025 18:30:14\\n:20 INFO: [bbtools] 1 of 1 task(s) completed\\n\\n2025-01-08 18: INFO: seek Run Prodigal on input to predict and extract genes\\n\\n2025-01-08 18: INFO: Running Prodigal with genetic code 11 in single mode\\n\\n2025-01-08 18: INFO: Running 1 job(s) on prodigal, starting at 01/08/2025 18:30:20\\n\\n2025-01-08 18: INFO: [prodigal] 1 of 1 task(s) completed\\n\\n2025-01-08 18: INFO: Genetic code 11 selected as optimal\\n\\n2025-01-08 18:42:11 INFO: Results written in /data/proj2/home/students/pst14/illegal logging _trees/fastqc raw/busco megahit secondtry/BUSCO megahit output secondtry\\n2025-01-08 18:42:11 INFO: For assistance with interpreting the results, please consult the userguide: https://busco.ezlab.org/busco userguide.html\\n\\n2025-01-08 18:42:11 INFO: Visit this page https://gitlab.com/ezlab/busco#how-to-cite-busco to see how to cite BUSCO\\n\\nOutput is truncated. View as a scrollable element or open in a text editor. Adjust cell output settings...\\n\\n\",\n",
|
||
" '[23]: print(gt.shape) # (number of variants, number of samples) @ ® Vv -€ o®\\nprint(gt[:5, :5]) # View the first 5 rows and columns “\\n\\n(477227, 60, 2)\\n\\nAttributeError Traceback (most recent call last)\\nInput In [23], in <cell line: 2>()\\n\\n1 print(gt.shape) # (number of variants, number of samples)\\n=== 2 print(gt [25, 251) # View the first 5 rows and columns\\n\\nFile ~/.conda/envs/test_allel_env/lib/python3.9/site-packages/allel/abc.py:285, in DisplayableArray.__str__(self)\\n284 def __str__(self):\\n—-> 285 return\\n\\nFile ~/.conda/envs/test_allel_env/lib/python3.9/site-packages/allel/abc.py:392, in DisplayAs2D.to_str(self, row_threshold, col_threshold, row\\n_edgeitems, col_edgeitems)\\n391 def to_str(self, row_threshold=6, col_threshold=10, row_edgeitems=3, col_edgeitems=5):\\n--> 392 items =\\n393\\n\\n394 s =\\n\\n',\n",
|
||
" \"In [26]: |# Remove the extra dimension (variants x samples x 1 + variants x samples)\\ngt_h = gt.haploidify_samples()\\n\\nIn [27]: haploid_gt = gt_h.squeeze()\\n\\nIn [28]: X = np.array(haploid_gt, dtype=float) # Convert to NumPy array\\nX[X == -1] = np.nan # Set missing data to NaN\\n\\n# Replace NaNs with mean per SNP\\ncol_means = np.nanmean(X, axis=@)\\ninds = np.where(np.isnan(X) )\\n\\nX[inds] = np.take(col_means, inds[1])\\n\\nIn [29]: from allel.stats.decomposition import GenotypePCA\\n\\n# Initialize PCA model (use 'standard' scaler since Patterson might not work well for haploids)\\nmodel = GenotypePCA(n_components=10, scaler=None)\\n\\n# Fit and transform haplotype data\\nmodel. fit(X)\\ncoords = model.transform(X)\\n\",\n",
|
||
" '© trimmed\\n\\nSRR21866470_1_trimmed.fastq.gz\\nSRR21866470_2_trimmed.fastq.gz\\nSRR21866471_1_trimmed.fastq.gz\\nSRR21866471_2_trimmed.fastq.gz\\nSRR21866472_1_trimmed.fastq.gz\\nSRR21866472_2_trimmed.fastq.gz\\nSRR21866473_1_trimmed.fastq.gz\\nSRR21866473_2_trimmed.fastq.gz\\nSRR21866474_1_trimmed.fastq.gz\\nSRR21866474_2_trimmed.fastq.gz\\nSRR21866475_1_trimmed.fastq.gz\\nSRR21866475_2_trimmed.fastq.gz\\n\\\\ trimmed_reports\\n\\n<> SRR21866470_fastp.html\\n\\n{} SRR21866470_fastp.json\\n\\n<> SRR21866471_fastp.html\\n\\n{} SRR21866471_fastp.json\\n\\n© SRR21866472_fastp.html\\n\\n{} SRR21866472_fastp.json\\n\\n/mnt/volume/data/group8/studies/trimmed_reports/SRR21866472_fastp.json\\n\\n{} SRR21866473_fastp.json\\n<> SRR21866474_fastp.html\\n{} SRR21866474_fastp.json\\n<> SRR21866475_fastp.html\\n{} SRR21866475_fastp.json\\n',\n",
|
||
" 'In\\n\\nIn\\n\\nIn\\n\\n[217]:\\n\\n[218]:\\n\\n[219]:\\n\\n2. scRepertoire on patient 3\\n\\nlibrary(scRepertoire)\\n\\n$1 <- read.delim(\"/home/rstudio/run@7@/run@70—-nsclc-3_VDJ_Dominant_Contigs_AIRR.tsv\", sep = \"\\\\t\",\\n\\ncontig_list <- list(S1)\\ncontig. list <- loadContigs(input = $1,\\n\\ndata(\"contig_list\")\\n#head(contig_list)\\n\\nhead(contig_list[[1]])\\n\\nA data.frame: 6 x 18\\n\\nbarcode\\n\\n<chr>\\n\\nAAACCTGAGTACGACG-\\n1\\nAAACCTGAGTACGACG-\\n1\\nAAACCTGCAACACGCC-\\n1\\nAAACCTGCAACACGCC-\\n1\\nAAACCTGCAGGCGATA-\\n\\n1\\nAAACCTGCAGGCGATA-\\n1\\n\\nis_cell\\n\\n<chr>\\n\\nTrue\\n\\nTrue\\n\\nTrue\\n\\nTrue\\n\\nTrue\\n\\nTrue\\n\\nformat = \"AIRR\")\\n\\ncontig_id\\n<chr>\\n\\nAAACCTGAGTACGACG-\\n1_contig_1\\n\\nAAACCTGAGTACGACG-\\n1_contig 2\\n\\nAAACCTGCAACACGCC-\\n1_contig 4\\n\\nAAACCTGCAACACGCC-\\n1_contig 2\\n\\nAAACCTGCAGGCGATA-\\n1_contig_4\\n\\nAAACCTGCAGGCGATA-\\n1_contig 2\\n\\ncombined. TCR_p3 <- combineTCR(contig_list,\\n\\nremoveNA FALSE,\\nFALSE,\\nFALSE)\\n\\nremoveMulti\\nfilterMulti\\n\\nhigh_confidence\\n\\n<chr>\\n\\nTrue\\n\\nTrue\\n\\nTrue\\n\\nTrue\\n\\nTrue\\n\\nTrue\\n\\nlength\\n\\n<int>\\n\\n500\\n\\n478\\n\\n506\\n\\n470\\n\\n558\\n\\n505\\n\\nchain\\n\\n<chr>\\n\\nTRA\\n\\nTRB\\n\\nTRA\\n\\nTRB\\n\\nTRA\\n\\nTRB\\n\\nv_gene\\n\\n<chr>\\n\\nTRAV25\\n\\nTRBV5-1\\n\\nTRAV38-\\n2/DV8\\n\\nTRBV10-\\n3\\n\\nTRAV12-\\n1\\n\\nTRBVO\\n\\nd_gene\\n\\n<chr>\\n\\nNone\\n\\nNone\\n\\nNone\\n\\nNone\\n\\nNone\\n\\nNone\\n\\nigene\\n\\n<chr>\\n\\nTRAJ20\\n\\nTRBJ2-\\n7\\n\\nTRAJ5S2\\n\\nTRBJ2-\\n\\nTRAJS\\n\\nTRBJ2-\\n2\\n\\nc_gene\\n\\n<chr>\\n\\nTRAC\\n\\nTRBC2\\n\\nTRAC\\n\\nTRBC2\\n\\nTRAC\\n\\nTRBC2\\n\\nheader\\n\\nfull_length productive\\n\\n<chr>\\n\\nTrue\\n\\nTrue\\n\\nTrue\\n\\nTrue\\n\\nTrue\\n\\nTrue\\n\\n<chr>\\n\\nTrue\\n\\nTrue\\n\\nTrue\\n\\nTrue\\n\\nTrue\\n\\nTrue\\n\\nTRUE, stl\\n\\nCAYRS,\\n\\nCASS\\n\\n# output = a list of contig data frames that will be reduced to the reads associated with a single cell barcode. It\\nhead (combined. TCR[[1]])\\n\\nA data-frame: 6 x 11\\n',\n",
|
||
" '6/14] COPY environment.yml /\\n\\nse CfaNsterring COMNLEXL. /.97KD\\n\\n[ 2/14] RUN echo \\'Acquire::AllowInsecureRepositories \"true\";\\' > /etc/apt/apt.conf.d/99insecure\\n\\n[ 3/14] RUN apt-get update && apt-get install -y build-essential wget unzip bzip2\\n\\n[ 4/14] RUN locale-gen en_US.UTF-8\\n\\n=> [ 5/14] RUN wget https://repo.continuum.io/miniconda/Miniconda3-py37_4.8.2-Linux-x86_64.sh -O /tmp/miniconda.sh &&\\nc\\nE\\n\\nRROR [ 7/14] RUN conda env create -f /environment.yml && conda clean -a\\n\\n> [ 7/14] RUN conda env create -f /environment.yml && conda clean -a:\\n1.829 Collecting package metadata (repodata.json): ...working... Killed\\n\\nCOPY environment.yml /\\n\\nView build details: docker-deskto\\n\\naman@Laptop-von-Aman juicer_hpro % I\\n=\\n\\n#use environment.yml from hic-pro github\\n\\n|\\n|\\n55 | >>> RUN conda env create -f /environment.yml && conda clean -a\\n| ENV PATH=\"/usr/local/anaconda/envs/HiC-Pro_v3.1.@/bin:$PATH\"\\n|\\n\\ndashboard/build/desktop—linux/desktop—linux/1txxronw9uw7j f3rknixoemaf\\n\\ngcc\\n\\ng++\\n\\n&& apt-get update --allow-unauthenticated\\n\\nopenjdk-11-jdk git curl make ca-certificates vim pyth\\n\\nbash /tmp/miniconda.sh -b -p /usr/local/anaconda &&\\n\\nERROR: failed to solve: ResourceExhausted: process \"/bin/sh -c conda env create -f /environment.yml && conda clean -a\" did not complete successfully: cannot allocate memory\\n\\nrm /tmp/miniconda.sh\\n\\n15.\\n184.\\n\\n21.\\n\\n494,\\n\\n7s\\n2s\\n\\n-6s\\n\\n4s\\n\\n-1s\\n\\n1s\\n\\n',\n",
|
||
" 'Coverage\\n\\n1000 4\\n\\n1500 4\\n\\n2000 4\\n\\n2500 4\\n\\n3000 4\\n\\nfull matrix\\n\\n0.75 4\\n\\n0.50\\n\\n0.25 4\\n\\ncoverage ratio\\n\\n0.00 +\\n\\nlo“?\\n\\n10-2\\n\\n10-3\\n\\n10-*\\n\\n10-5\\n\\ncorrected frequencies\\n',\n",
|
||
" 'Genome vv Tracks ¥ Sample Info v Session v Share Bookmark Save Image Circular View v Help v\\n\\nIGV oxford_e...me.fasta _ tig00000002:1,604,261-1,606,695 § Q. 2,435 bp (Select Tracks ) (\"Crosshairs )(_Center Line )(TrackLabels) @ iE +)\\n\\nC D)\\n\\n604,300 bp 1,604,500 bp 1,604,700 bp 1,604,900 bp 1,605,100 bp 1,605,300 bp 1,605,500 bp 1,605,700 bp 1,605,900 bp 1,606,100 bp 1,606,300 bp 1,606,500 bp 1,606,\\nL i 1 L L L 1 L L L 1 L L L 1 L L L 1 L L L 1 L L L 1 L L L 1 L L L 1 L L L 1 L L i 1 L L L 1 L\\n\\n%\\nee Pe | ZZ\\n\\npdeC_1 IKAOHOFJ_01847 ssb\\n\\npdeC_2\\n',\n",
|
||
" '12 #dependencies\\n\\n13 >>> RUN apt-get update && apt-get install -y \\\\\\n\\n14 | >>> build-essential \\\\\\n\\n15 >>> wget \\\\\\n\\n16 | >>> unzip \\\\\\n\\n17 | >>> bzip2 \\\\\\n\\n18 >>> gcc \\\\\\n\\n19 >>> gt+ \\\\\\n\\n20 >>> openjdk-11-jdk \\\\\\n\\n21 >>> git \\\\\\n\\n22 | >>> curl \\\\\\n\\n23 | >>> make \\\\\\n\\n24 | >>> ca-certificates \\\\\\n\\n25 | >>> vim \\\\\\n\\n26 >>> python3 \\\\\\n\\n27 | >>> python3-pip \\\\\\n\\n28 >>> zlibig-dev \\\\\\n\\n29 | >>> libncurses5-dev \\\\\\n\\n30 | >>> libbz2-dev \\\\\\n\\n31 | >>> liblzma-dev \\\\\\n\\n32 | >>> samtools \\\\\\n\\n33 | >>> locales \\\\\\n\\n34 >>> && apt-get clean && rm -rf /var/lib/apt/lists/*\\n\\n35\\nERROR: failed to solve: process \"/bin/sh -c apt-get update && apt-get install -y build-essential wget unzip bzip2 gcc gt+ openjdk-11-jdk git curl make ca-certificat\\nes vim python3 python3-pip zlib1g-dev libncurses5-dev libbz2-dev liblzma-dev samtools locales && apt-get clean && rm -rf /var/lib/apt/lists/*\" did not complete successf\\n\\nully: exit code: 100\\n\\nView build details: docker-desktop://dashboard/build/desktop—linux/desktop—linux/7rnkdoghq317qwxyt33wc77ng\\naman@Laptop-von—Aman juicer_hpro %\\n\\n',\n",
|
||
" \"[19]: #only extracting the alternate allele count\\n\\n#By selecting only the alternate allele counts, the genotypes are converted into a simple numerical coding: @ for homozygous reference (0/0),\\ngn = gt.to_allele_counts()[:, :, 1]\\n\\n[31]: coords, model = allel.pca( ®@hkVeaPpe\\ngn,\\nn_components=10,\\nscaler='patterson',\\nploidy=2\\n\\n°@\\n\",\n",
|
||
" 'la:\\n\\n# Unique chromosomes\\nchromosomes = df[\\'chromosome\\'].unique()\\n\\n# Create a plot for each chromosome\\nplt.figure(figsize=(8, 6))\\nfor chrom in chromosomes:\\nchrom_data = df[df{\\'chromosome\\'] == chrom]\\nplt.plot(chrom_data[\\'distance\\'], chrom_data[\\'contact_count\\'], label=f\"Chr {chrom}\")\\n\\n# Log-log scale\\nplt.xscale(\"log\")\\nplt.yscale(\"log\")\\n\\n# Labels and legend\\n\\nplt.xlabel(\"Genomic Distance (bp)\")\\nplt.ylabel(\"Hi-C Contact Frequency\")\\nplt.title(\"Chromosome-Specific Hi-C Contact Decay\")\\nplt. legend()\\n\\nplt.show()\\n\\nKeyError Traceback (most recent call last)\\nFile ~/. local/lib/python3. 10/site-packages/pandas/core/indexes/base.py:3805, in Index.get_loc(self, key)\\n3804 try:\\n\\nFile index.pyx:167, in pandas._libs. index. IndexEngine.get_loc()\\nFile index.pyx:196, in pandas._libs. index. IndexEngine.get_loc()\\n\\nFile pandas/_libs/hashtable_class_helper.pxi:7081, in pandas._libs.hashtable.Py0bjectHashTable.get_item()\\n\\nFile pandas/_libs/hashtable_class_helper.pxi:7089, in pandas._libs.hashtable.Py0bjectHashTable.get_item()\\n\\nKeyError: ‘chromosome’\\n\\nThe above exception was the direct cause of the following exception:\\n\\nKeyError Traceback (most recent call last)\\nCell Inf2], line 2\\n\\n1 # Unique chromosomes\\n> 2 chromosomes - df f!chFOneSOnel. unique()\\n\\n4 # Create a plot for each chromosome\\n\\n5 plt.figure(figsize=(8, 6))\\n\\nFile ~/. local/lib/python3. 10/site-packages/pandas/core/frame.py:4102, in DataFrame._getitem_(self, key)\\n4100 if self.columns.nlevels >\\n\\n4101 return self._getitem_multilevel(key)\\n\\n4103 if is_integer(indexer) :\\n4104 indexer = [indexer]\\n\\nFile ~/. local/lib/python3. 10/site-packages/pandas/core/indexes/base.py:3812, in Index.get_loc(self, key)\\n\\n3807 if isinstance(casted_key, slice) or (\\n3808 isinstance(casted_key, abc.Iterable)\\n3809 and any(isinstance(x, slice) for x in casted_key)\\n3810\\n3811 raise InvalidIndexError(key)\\n-> 3812 raise KeyError(key) from err\\n3813 except TypeError:\\n3814 # If we have a listlike key, _check_indexing_error will raise\\n3815 # InvalidIndexError. Otherwise we fall through and re-raise\\n3816 # the TypeError.\\n3817 _seLf._check_indexing_error(key)\\n\\nKeyError: ‘chromosome’\\n\\n',\n",
|
||
" 'Who is “Indian” in the Gulf? Race, Labor and\\nCitizenship - MERIP\\n\\nhttps://merip.org/2021/06/who-is-indian-in-\\nthe-gulf-race-labor-and-citizenship/\\n\\nAbout Blog Examples Plugins Docs ©\\n\\nno+xX\\nO+X Ser4 ym\\n\\n2e+4\\nte+4\\n5et+3\\n\\n2e+3\\n1e+3\\n5e+2\\n\\n2e+2\\nte+2\\n50\\n\\nchr1_chr1.mcool\\n[Current data resolution: 5.12M],\\n\\n',\n",
|
||
" 'In\\n\\nIn\\n\\n[113]:\\n\\n[114]:\\n\\ncombined_seurat\\n\\nAn object of class Seurat\\n\\n48197 features across 3483 samples within 3 assays\\n\\nActive assay: integrated (3000 features, 3000 variable features)\\n2 layers present: data, scale.data\\n2 other assays present: RNA, SCT\\n2 dimensional reductions calculated: pca, umap\\n\\npatient3_transform\\npatient4_transform\\n\\nAn object of class Seurat\\n\\n44925 features across 3160 samples within 2 assays\\n\\nActive assay: SCT (21234 features, 3000 variable features)\\n3 layers present: counts, data, scale.data\\n1 other assay present: RNA\\n\\nAn object of class Seurat\\n\\n30351 features across 323 samples within 2 assays\\n\\nActive assay: SCT (13866 features, 3000 variable features)\\n3 layers present: counts, data, scale.data\\n1 other assay present: RNA\\n',\n",
|
||
" 'In\\n\\nIn\\n\\nIn\\n\\n[231]:\\n\\n[232]:\\n\\n[233]:\\n\\n4. scRepertoire on patient 4\\n\\nlibrary(scRepertoire)\\n\\n#S1 <- read.delim(\"/home/rstudio/run@71-nsclc—4_VDJ_Dominant_Contigs_AIRR.tsv\", sep = \\'\"\\\\t\", header =\\n\\n#contig_list\\n#contig.list\\n#\\n\\n$2 <- read.delim(\"/home/rstudio/run@71-nsclc-4_VDJ_Dominant_Contigs_AIRR.tsv\", sep = \"\\\\t\", header =\\n\\n<- list(S1)\\n<- loadContigs (input\\nformat\\n\\ncontig_list <- loadContigs(input = $2, format =\\n\\n\"AIRR\")\\n\\n#converting columns in TCR data to character (string).\\n\\ncontig_list <- lapply(contig_list, function(df) {\\nfor (col in c(\"cdr3_nt\", \"cdr3\", \"chain\", \"barcode\", \"v_gene\", \"j_gene\", \"d_gene\", \"c_gene\")) {\\nif (col %in% names(df)) df[[col]] <- as.character(df[[col]])\\n\\ndf\\n3)\\n\\ncombined. TCR_p4 <- combineTCR(contig_list,\\nremoveNA = FALSE,\\n\\n# output = a list of contig data frames that will be reduced to the reads associated with a\\n\\nremoveMulti =\\nfilterMulti =\\n\\nhead (combined. TCR[[1]])\\n\\nA data.frame: 6 x 11\\n\\nbarcode\\n\\n<chr>\\n\\n4 1002101\\n\\n3 10279593\\n\\n5 10300542\\n\\n6 10311423\\n\\n8 10627379\\n\\n10 10742731\\n\\nTCR1\\n\\n<chr>\\n\\nTRAV19°01.TRAJ27*01.TRAC\\n\\nTRAV13-2\"01.TRAJ26°01.TRAC\\n\\nTRAV21*02.TRAJ34*01.TRAC\\n\\nTRAV21*01.TRAJ15*01.TRAC\\n\\nTRAV29/DV5*01.TRAJ47*01.TRAC\\n\\nTRAV9-2\"01.TRAJ43*01.TRAC\\n\\nFALSE,\\nFALSE)\\n\\ncdr3_aat\\n\\n<chr>\\n\\nCAPTPMQANQP\\n\\nCAENTRGRRSEFCL.\\n\\nCAAYNTDKLIF\\n\\nCAVVNQAGTALIF\\n\\nCAASRYGNKLVF\\n\\nCALGEGDMRF\\n\\nedr3_nt1\\n\\n<chr>\\n\\nTGTGCCCCAACACCAATGCAGGCAAATCAACCTTT\\n\\nTGTGCAGAGAATACGAGGGGTAGGAGGTCAGAAT GTCTIT\\n\\nTGTGCTGCTTATAACACCGACAAGCTCATCTTT\\n\\nTGTGCTGTAGTTAACCAGGCAGGAACTGCTCTGATCTTT\\n\\nTGTGCAGCAAGCAGATATGGAAACAAACTGGTCTTT\\n\\nTGTGCTCTGGGGGAGGGTGACATGCGCTIT\\n\\nTRBV27*01.TRBD2*02.TRBJ2-\\n\\nTRUE, stringsAs\\n\\nTRUE, stringsAsl\\n\\nsingle cell barcode. It\\n\\nTCR2\\n<chr>\\n\\nTRBV11-2*01.NA.TRBJ1-\\n\\n¢\\nfroitraci OAS\\n\\nTRBV6-\\n2°01.TRBD1*01.TRBJ1- —C/\\n1°01.TRBC1\\n\\nNA\\n\\nTRBV11-\\n2°01.TRBD2\"02.TRBJ1- c\\n2°01.TRBC1\\n\\n2°01.TRBC2 Cre)\\n\\nTRBV11-\\n2°01.TRBD1*01.TRBJ2-\\n7°01.TRBC2\\n\\nCASS\\n',\n",
|
||
" '[pst14@frontend ref_gen]$ head -n 3@ alignment_stats_3.txt | grep *SN | cut -f 2-\\nraw total sequences:\\nfiltered sequences:\\nsequences: 371284\\nis sorted: 1\\n\\n1st fragments: 185642\\nlast fragments: 185642\\n\\nreads\\nreads\\nreads\\n\\nMapped: 371284\\nmapped and paired:\\nunmapped: @\\n\\n371284 # excluding supplementary and secondary reads\\n)\\n\\n371284 # paired-end technology bit set + both mates mapped\\n\\nreads properly paired: 371228 # proper-pair bit set\\n\\nreads paired: 371284 # paired-end technology bit set\\n\\nreads duplicated: (7) # PCR or optical duplicate bit set\\nreads MQ@: 166772 # mapped and MQ=0\\n\\nreads QC failed: ()\\n\\nnon-primary alignments: @\\n\\nsupplementary alignments: 89\\n\\ntotal length: 48168357 # ignores clipping\\n\\ntotal first fragment length: 24092091 # ignores clipping\\n\\ntotal\\nbases\\nbases\\nbases\\nbases\\n\\nlast fragment leng\\nMapped: 48168357\\nmapped (cigar):\\ntrimmed: 0\\nduplicated:\\n\\nth: 24076266 # ignores clipping\\n# ignores clipping\\n48164712 # more accurate\\n\\n()\\n\\nCpst14efrontend ref_gen]$\\n\\n',\n",
|
||
" '@FastQC Report\\n\\nSummary\\n\\nQeasic Statistics\\nOre base sequence quality\\n\\nOber sequence quality scores\\n\\nOber base sequence content\\nQeer sequence GC content\\nOeer base N content\\n\\nQ sequence Length Distribution\\nQseauence Duplication Levels\\nQoverrepresented sequences\\nQadapter Content\\n\\nQrxmmer Content\\n\\nQbasic Statistics\\n\\na\\n\\nFilename\\n\\nFile type\\n\\nEncoding\\n\\nTotal Sequences\\n\\nSequences flagged as poor quality\\nSequence length\\n\\n%GC\\n\\nwood_sample_4_forward_paired. fq.gz\\nConventional base calls\\n\\nSanger / Illumina 1.9\\n\\n180607\\n\\n)\\n\\n30-150\\n\\n37\\n\\n@per base sequence quality\\n\\nQuality scores across all bases (Sanger / Illumina 1.9 encoding)\\n\\n40\\n\\n16\\n\\n14\\n12\\n10\\n\\noN B&O\\n\\n12345 67 8 9 1519\\n\\n30-34 45-49 60-64 75-79 90-94 105-109 120-124 135-139 150\\n',\n",
|
||
" 'Command Example (with SnpEff):\\n\\n1. Build a database for the species reference genome if not already available:\\n\\nbash GO Copy @ Edit\\n\\nsnpEff build -gff3 -v reference_genome\\n\\n2. Annotate the variants:\\n\\nbash GO Copy @ Edit\\n\\nsnpEff reference_genome variants.vcf.gz > annotated_variants.vcf\\n',\n",
|
||
" 'Total identified loops according to the study (long-range\\nloops 2 20 kb): 1,177; No parameters mentioned in paper\\nand SI\\n\\nLoops containing at least one dACR: 614\\n\\nEstimated percentage of loops that are dACR loops:\\n614/1177 * 100 = 52.17%\\n\\napproximately 52.17% of the estimated total loops in the\\nmaize genome would be dACR loops\\n\\n52.17% of ‘x’ number of total loops = 1177;\\nX= 1177 * 100/ 52.17\\nX = 2256;\\n\\nClose to the number of total loops found\\n',\n",
|
||
" '@ Vivaldi File Edit View Bookmarks Mail Tools Window’ Help 0 @m @ B+ 8 © & BW & SF Q BS TueAprs 16:08\\n\\n(BR = QQ) © 6 wwwaeridderlab.nl/vacancies-0 = a& [] Q Search Startpage |_ a 2 ¢ @ *»*§ © FF ®@ e@\\nOnline Bewerbung QGIS API Docume... qgis - Trying to pe... New Script - Earth... Pastebin.com - #1... TargetP 2.0- DTU... https://www.mood... OnePlus 12R revie.... Whois “Indian\"in.. A Y Hi-C maize A63v.... SRA Links for BioP... »\\nQ\\n&\\n&\\nV e t D R ° d d Currently we don\\'t have any open positions, but\\nad Cd N Cc I es a e | e r candidates who match the interest of our group, 6\\nare encouraged to send their CV to j.deridder- oO\\nG ro U p 4@umcutrecht.nl.\\n©\\nStudents who are interested in an internship in the\\nde Ridder group, may send their application to ea\\nj.deridder-4@umcutrecht.nl. iia\\nG\\nDe Ridder Group Reach out\\nCenter for Molecular Medicine, UMC Utrecht Dr. Jeroen de Ridder — Associate Professor\\nj.deridder-4@umcutrecht.nl\\nStratenum 3.123\\nUniversiteitsweg 100 &\\na chill_brain_wander v < toluene123/scrna_complet« li localhost ).« Search results | IRB Barcelc A Error 404: Page not found | A Our current research projec I | Vacancies — De Ridder Lal + @& Ww\\n\\n& 0 @ 8 S CI CQ Rest —O———— 100% + 16:08\\n',\n",
|
||
" 'Q multigc\\n\\nPer Sequence Quality Scores @EGNNID)\\n\\n@ Help\\n\\nwi The number of reads with average quality scores. Shows if a subset of reads has poor quality.\\nGeneral Stats Export Plot\\nFastQc FastQC: Per Sequence Quality Scores\\n20 samples\\n\\nSequence Counts\\n\\nSequence Quality Histograms 35k\\n\\nPer Sequence Quality Scores\\n\\nPer Base Sequence Content 30k\\n\\nPer Sequence GC Content ask\\n\\nPer Base N Content\\n\\nSequence Length Distribution 20k\\n\\nSequence Duplication Levels\\n\\nOverrepresented sequences by sample isk\\n\\nTop overrepresented sequences\\n\\nAdapter Content we\\n\\nStatus Checks 5k\\n\\nSoftware Versions\\n\\n°\\n10 1s 20 2s 30 35\\n@Help\\n\\nIe > + Toolbox\\n\\n9 CF\\n\\n©\\n\\nPer Base Sequence Content (9)\\n\\nThe proportion of each base position for which each of the four normal DNA bases has been called.\\n\\n& Click a sample row to see a line plot for that dataset.\\n\\n© Rollover for sample name\\nMA: - %G: -\\n\\nPosition: - %T: - %C:\\n\\n',\n",
|
||
" 'Observed Contact Count\\n\\nGenomic Distance vs Observed Counts\\n\\n10°\\nGenomic Distance (bp)\\n\\n',\n",
|
||
" 'chril startl endl chr2 start2 end2 readID strand1 strand2\\n',\n",
|
||
" \"o-Yof = AinTiating: H1iv—-rro-master/scripts/srce/cutsite_trimming.cpp\\n5.037 creating: HiC-Pro-master/test-op/\\n\\n5.038 inflating: HiC-Pro-master/test-op/config_test_as.txt\\n\\n5.038 inflating: HiC-Pro-master/test-op/config_test_cap.txt\\n\\n5.038 inflating: HiC-Pro-master/test-op/config_test_dnase.txt\\n\\n5.038 inflating: HiC-Pro-master/test-op/config_test_latest.txt\\n5.038 inflating: HiC-Pro-master/test-op/run-test-op.sh\\n\\n5.038 finishing deferred symbolic links:\\n\\n5.038 HiC-Pro-master/doc/themes/paris/logos -> ../../_static/logos/\\n5.095 Make sure internet connection works for your shell prompt under current user's privilege ...\\n5.096 Starting HiC-Pro installation !\\n\\n5.122 Exit - Error : Configuration file not found\\n\\n41 # Install HiC-Pro\\n\\n42 | >>> RUN cd /opt && \\\\\\n\\n43 | >>> wget https://github.com/nservant/HiC-Pro/archive/master.zip -O hicpro_latest.zip && \\\\\\n\\n44 | >>> unzip hicpro_latest.zip && \\\\\\n\\n45 | >>> cd HiC-Pro-master/scripts/install && \\\\\\n\\n46 | >>> bash install_dependencies.sh -c config-install.txt -p /opt/hicpro -o /opt/hicpro/HiC-Pro_3.1.@ -q && \\\\\\n47 | >>> cd /opt/HiC-Pro-master && \\\\\\n\\n48 | >>> make install && \\\\\\n\\n49 | >>> 1n -s /opt/hicpro/bin/HiC-Pro /usr/local/bin/HiC-Pro && \\\\\\n\\n5@ | >>> rm -rf /opt/hicpro_latest.zip /opt/HiC-Pro-master\\n\\n\",\n",
|
||
" 'This figure illustrates the copper (Cu) sparing mechanism in cyanobacteria and some algae, where\\ncytochrome cg (Cyt c.) replaces plastocyanin (PC) under copper-deficient conditions.\\nPlastocyanin, a copper-dependent protein, facilitates electron transport between the cytochrome\\nbef complex and photosystem | (PSI) in the thylakoid lumen. During copper scarcity, copper\\nresponse regulators (CRR) activate the CYC6 gene, encoding the iron-dependent cytochrome Ce,\\nallowing photosynthesis to continue while conserving copper for other critical processes.\\nAdditionally, CRR triggers protease activity, degrading plastocyanin to prevent the accumulation of\\nnon-functional, copper-deficient proteins. Experimental evidence from the blot shows that under\\ncopper limitation, cytochrome c, levels increase as plastocyanin levels decline, while the addition\\nof copper restores plastocyanin expression and reduces cytochrome c, levels. This mechanism\\ndemonstrates efficient resource allocation and functional substitution to maintain electron\\n\\ntransport during copper deficiency.\\n',\n",
|
||
" '(base) aman@unicorn: /mnt/storage3/aman/20000/interchr_matrix$ ls\\n\\nbuild_matrices.log\\n\\nbuild_matrix_automating.sh\\n\\nchri1@_chr1@.matrix\\n\\nchr1@_chr1@_abs.bed\\nchr1@_chr1@_ord.bed\\n\\nchri_chri.matrix\\nchri_chri1@.matrix\\nchri_chr1@_abs.bed\\nchri_chr1@_ord.bed\\nchri_chri_abs.bed\\nchri_chri_ord.bed\\nchri_chr2.matrix\\nchri_chr2_abs.bed\\nchri_chr2_ord.bed\\nchri_chr3.matrix\\nchri_chr3_abs.bed\\nchri_chr3_ord.bed\\n\\nchri_chr4.matrix\\nchri_chr4_abs.bed\\nchri_chr4_ord.bed\\nchri_chr5.matrix\\nchri_chr5_abs.bed\\nchri_chr5_ord.bed\\nchri_chr6é.matrix\\nchri_chr6_abs.bed\\nchri_chr6_ord.bed\\nchri_chr7.matrix\\nchri_chr7_abs.bed\\nchri_chr7_ord.bed\\nchri_chr8.matrix\\nchri_chr8_abs.bed\\nchri_chr8_ord.bed\\nchri_chr9.matrix\\nchri_chr9_abs.bed\\n\\nchri_chr9_ord.bed\\nchr2_chri1@.matrix\\nchr2_chr1@_abs.bed\\nchr2_chr1@_ord.bed\\nchr2_chr2.matrix\\nchr2_chr2_abs.bed\\nchr2_chr2_ord.bed\\nchr2_chr3.matrix\\nchr2_chr3_abs.bed\\nchr2_chr3_ord.bed\\nchr2_chr4.matrix\\nchr2_chr4_abs.bed\\nchr2_chr4_ord.bed\\nchr2_chr5.matrix\\nchr2_chr5_abs.bed\\nchr2_chr5_ord.bed\\nchr2_chr6.matrix\\n\\nchr2_chr6_abs.bed\\nchr2_chr6_ord.bed\\nchr2_chr7.matrix\\nchr2_chr7_abs.bed\\nchr2_chr7_ord.bed\\nchr2_chr8.matrix\\nchr2_chr8_abs.bed\\nchr2_chr8_ord.bed\\nchr2_chr9.matrix\\nchr2_chr9_abs.bed\\nchr2_chr9_ord.bed\\nchr3_chri1@.matrix\\nchr3_chr1@_abs.bed\\nchr3_chr1@_ord.bed\\nchr3_chr3.matrix\\nchr3_chr3_abs.bed\\nchr3_chr3_ord.bed\\n\\nchr3_chr4.matrix\\nchr3_chr4_abs.bed\\nchr3_chr4_ord.bed\\nchr3_chr5.matrix\\nchr3_chr5_abs.bed\\nchr3_chr5_ord.bed\\nchr3_chr6é.matrix\\nchr3_chr6_abs.bed\\nchr3_chr6_ord.bed\\nchr3_chr7.matrix\\nchr3_chr7_abs.bed\\nchr3_chr7_ord.bed\\nchr3_chr8.matrix\\nchr3_chr8_abs.bed\\nchr3_chr8_ord.bed\\nchr3_chr9.matrix\\nchr3_chr9_abs.bed\\n\\nchr3_chr9_ord.bed\\nchr4_chri@.matrix\\nchr4_chr1@_abs.bed\\nchr4_chr1@_ord.bed\\nchr4_chr4.matrix\\nchr4_chr4_abs.bed\\nchr4_chr4_ord.bed\\nchr4_chr5.matrix\\nchr4_chr5_abs.bed\\nchr4_chr5_ord.bed\\nchr4_chr6é.matrix\\nchr4_chr6_abs.bed\\nchr4_chr6_ord.bed\\nchr4_chr7.matrix\\nchr4_chr7_abs.bed\\nchr4_chr7_ord.bed\\nchr4_chr8.matrix\\n\\nchr4_chr8_abs.bed\\nchr4_chr8_ord.bed\\nchr4_chr9.matrix\\nchr4_chr9_abs.bed\\nchr4_chr9_ord.bed\\nchr5_chri@.matrix\\nchr5_chr1@_abs.bed\\nchr5_chr1@_ord.bed\\nchr5_chr5.matrix\\nchr5_chr5_abs.bed\\nchr5_chr5_ord.bed\\nchr5_chr6é.matrix\\nchr5_chr6_abs.bed\\nchr5_chr6_ord.bed\\nchr5_chr7.matrix\\nchr5_chr7_abs.bed\\nchr5_chr7_ord.bed\\n\\nchr5_chr8.matrix\\nchr5_chr8_abs.bed\\nchr5_chr8_ord.bed\\nchr5_chr9.matrix\\nchr5_chr9_abs.bed\\nchr5_chr9_ord.bed\\nchr6é_chri1@.matrix\\nchr6_chr1@_abs.bed\\nchr6_chr1@_ord.bed\\nchr6é_chr6é.matrix\\nchr6_chr6_abs.bed\\nchr6_chr6_ord.bed\\nchr6_chr7.matrix\\nchr6_chr7_abs.bed\\nchr6_chr7_ord.bed\\nchr6é_chr8.matrix\\nchr6_chr8_abs.bed\\n\\nchr6_chr8_ord.bed\\nchr6é_chr9.matrix\\nchr6_chr9_abs.bed\\nchr6_chr9_ord.bed\\nchr7_chri1@.matrix\\nchr7_chr1@_abs.bed\\nchr7_chr1@_ord.bed\\nchr7_chr7.matrix\\nchr7_chr7_abs.bed\\nchr7_chr7_ord.bed\\nchr7_chr8.matrix\\nchr7_chr8_abs.bed\\nchr7_chr8_ord.bed\\nchr7_chr9.matrix\\nchr7_chr9_abs.bed\\nchr7_chr9_ord.bed\\nchr8_chri1@.matrix\\n\\nchr8_chr1@_abs.bed\\nchr8_chr1@_ord.bed\\nchr8_chr8.matrix\\nchr8_chr8_abs.bed\\nchr8_chr8_ord.bed\\nchr8_chr9.matrix\\nchr8_chr9_abs.bed\\nchr8_chr9_ord.bed\\nchr9_chri1@.matrix\\nchr9_chr1@_abs.bed\\nchr9_chr1@_ord.bed\\nchr9_chr9.matrix\\nchr9_chr9_abs.bed\\nchr9_chr9_ord.bed\\n',\n",
|
||
" '@ Mainwindow @®@@6OeOr+ezek@ee =) FS Q SS MonDec30 1\\n\\n[ Rem ) [Juicebox 2.17.00] Hi-C Map <9>: data.allValidPairs.hic\\n\\nFile View Bookmarks Assembly Dev\\nChromosomes\\n\\n6 @ « Ge\\n\\nNormalization (Obs | Ctrl) Resolution (BP) Color Range\\n\\n6:113,950,001-114,000,000\\n\\n100 MB\\n\\nmerge... <p> [> oO\\n\\n10000... <—@\\nLayerO <<\\n\\nShow Annotation Panel\\n\\n',\n",
|
||
" \"Divergence time (At)\\n\\nReference sequence\\nmums Methylated\\n\\n= Unmethylated\\n\\nGeneration 0 Generation 1 Generation m Generation 30\\nSe — — — | no ee |\\n— Es —_> ! '\\nI — — —_— | — — = — i]\\n' a see — ts — at —\\n1 7 >\\n, So — — — — —\\n! — — —_— — — —\\nFounder v id id\\na ST — Se — Se —\\n— ea —_ — = = — — = — — a —\\na — a —\\n1 a — SSS — SS —\\n! — te — —_—_—_— -— — — a —\\n| — re ——\\nI SS —= SS — SS —\\n1 _— — —_— — | —\\ni — ——_ —> |\\na — SS —_ — —\\n&\\n\\nAt = 30 + 30 = 60\\n\",\n",
|
||
" 'The two terms are related but not exactly the same. Synteny generally refers to the conservation of\\nblocks of genes on the same chromosome across species, regardless of whether the order is exactly\\nmaintained. In contrast, colinear sequence clusters specifically describe groups of genes that not\\nonly lie on the same chromosome but also retain the same linear order (and often orientation) across\\n\\ngenomes. Thus, colinearity is a stricter, more detailed aspect of synteny.\\n',\n",
|
||
" 'PRC1 PRC2\\n\\n',\n",
|
||
" \"In [30]:\\n\\nimport matplotlib.pyplot as plt\\n\\n# Compute variance explained\\nexplained_variance = model.eigenvalues_ / model.eigenvalues_.sum()\\n\\n# Scatter plot of first two PCs\\n\\nplt.scatter(coords[:, 0], coords[:, 1], alpha=0.7)\\n#plt.xlabel(f'PC1 ({explained_variance[0]*100:.2f}%)')\\n#plt.ylabel(f'PC2 ({explained_variance[1]*100:.2f}%)')\\nplt.title('PCA of Haploid Data')\\n\\nplt.show()\\n\\nAttributeError Traceback (most recent call last)\\nInput In [30], in <cell line: 4>()\\n1 import matplotlib.pyplot as plt\\n3 # Compute variance explained\\n----> 4 explained_variance = modeWeigenvalues) / model.eigenvalues_.sum()\\n7 # Scatter plot of first two PCs\\n8 plt.scatter(coords[:, @], coords[:, 1], alpha=0.7)\\n\\nAttributeError: 'GenotypePCA' object has no attribute 'eigenvalues_'\\n\",\n",
|
||
" '(fithic_aman) [papantonis1@gwdu101 ~]$ fithic -i ~/rbp1_interactions_5kb.txt.gz -f ~/frag_5kb.txt.gz -o ~/aman/microc_project/loop_calling_premade_hic/fithic_results/ -r 5000\\n\\nFragment file not found\\n(fithic_aman) [papantonis1@gwdu101 ~]$ fithic -i ~/rbp1_interactions_5kb.txt.gz -f ~/frag_5kb.txt -o ~/aman/microc_project/loop_calling_premade_hic/fithic_results/ -r 5000\\n\\nReading fragments file from: /usr/users/papantonis1/frag_5kb.txt\\n\\nReading interactions file from: /usr/users/papantonis1/rbp1_interactions_5kb.txt.gz\\nOutput path being used from /usr/users/papantonis1/aman/microc_project/loop_calling_premade_hic/fithic_results/\\nFixed size option detected... Fast version of FitHiC will be used\\n\\nResolution is 5.@ kb\\n\\nNo bias file\\n\\nThe number of spline passes is 1\\n\\nThe number of bins is 100\\n\\nThe number of reads required to consider an interaction is 1\\n\\nThe name of the library for outputted files will be FitHiC\\n\\nUpper Distance threshold is inf\\n\\nLower Distance threshold is @\\n\\nOnly intra-chromosomal regions will be analyzed\\n\\nLower bound of bias values is 0.5\\n\\nUpper bound of bias values is 2\\n\\nAll arguments processed. Running FitHiC now...\\n\\nReading the contact counts file to generate bins...\\n\\nInteractions file read. Time took 511.9415831565857\\n\\nFragments file read. Time took 1.054295539855957\\n\\nWriting /usr/users/papantonis1/aman/microc_project/loop_calling_premade_hic/fithic_results/FitHiC. fithic_pass1.res5000.txt\\n\\nSpline fit Pass 1 starting...\\n\\nOutlier threshold is... 5.268321405383386e-11\\n\\nWriting p-values and q-values to file /usr/users/papantonis1/aman/microc_project/loop_calling_premade_hic/fithic_results/FitHiC.spline_pass1.significances.txt\\nNumber of outliers is... 64847\\n\\nFit-Hi-C completed successfully\\n',\n",
|
||
" '# VCF analysis\\nfor i in xbowtie.vcf; do\\n\\ngrep -c \\'*##\\' \"$i\" # Count the lines starting with \\'##\\'\\n\\ngrep --color \\'*#CHROM\\' \"$i\" # Show the line starting with \\'#CHROM\\'\\ngrep -v \"*#\" -c \"$i\" ## Count the lines not starting with \\'#\\'\\n\\ngrep -v \"#\" \"$i\" | grep -c \"TYPE=snp\"\\n\\necho \"SNPS above\"\\ngrep -v \"#\" \"$i\"\\ngrep -v \"#\" \"$i\"\\ngrep -v \"#\" \"$i\"\\ngrep -v \"#\" \"$i\"\\n\\n| grep -c \"TYPE=mnp\"\\n| grep -c \"TYPE=ins\"\\n| grep -c \"TYPE=del\"\\n| grep -c \"TYPE=complex\"\\n\\necho \"Analysis complete for $i\"\\n\\ndone\\n',\n",
|
||
" '@ Terminal Shell Edit View Window Help SU GB O+ 8 © & WD ® F Q B® SatFeb15 12:24\\n\\nee@ aman — aman@unicorn: ~/fihic_bias — ssh -L 9005:localhost:9005 aman@10.162.143.69 — 208x63\\n\\n-20.00.jar hiccups --cpu --threads 16 -r 500000 -f @.15 -p 1.5 -i 12 -d 250000 /mnt/storage3/aman/hicpro2juicebox/data.allValidPairs.hic ~/hiccups_5@@kb/\\n\\n-20.00.jar hiccups --cpu --threads 16 -r 500000 -f @.15 -p 2 -i 12 -d 250000 /mnt/storage3/aman/hicpro2juicebox/data.allValidPairs.hic ~/hiccups_5@@kb/\\n\\n-20.00.jar hiccups --cpu --threads 16 -r 500000 -f @.15 -p 1 -i 12 -d 250000 /mnt/storage3/aman/hicpro2juicebox/data.allValidPairs.hic ~/hiccups_5@@kb/\\n\\n-20.00.jar hiccups --cpu --threads 16 -r 100000 -f @.2 -p 2 -i 12 -d 250000 /mnt/storage3/aman/hicpro2juicebox/data.allValidPairs.hic ~/hiccups_5@@kb/\\n\\n-20.00.jar hiccups --cpu --threads 16 -r 1000000 -f @.2 -p 2 -i 12 -d 250000 /mnt/storage3/aman/hicpro2juicebox/data.allValidPairs.hic ~/hiccups_5@@kb/\\n\\n-20.0@.jar hiccups --cpu —-threads 16 -r 25000 -f @.2 -p 2 -i 12 /mnt/storage3/aman/hicpro2juicebox/data.allValidPairs.hic ~/hiccups_5@0kb/\\n\\n-20.00.jar hiccups --cpu —-threads 16 -r 50000 -f @.2 -p 2 -i 12 /mnt/storage3/aman/hicpro2juicebox/data.allValidPairs.hic ~/hiccups_5@0kb/\\n\\n-20.00.jar hiccups --cpu --threads 16 -r 25000 -f @.25 -p 2 -i 14 -d 25000 /mnt/storage3/aman/hicpro2juicebox/data.allValidPairs.hic ~/hiccups_5@@kb/\\n\\n-20.00.jar hiccups --cpu --threads 16 -r 50000 -f @.25 -p 2 -i 14 -d 50000 /mnt/storage3/aman/hicpro2juicebox/data.allValidPairs.hic ~/hiccups_5@@kb/\\n\\njava -jar ~/juicer/CPU/common/juicer_too -20.00.jar hiccups --cpu --threads 16 -r 5000,10000,25000 -f 0.30 -p 1.5 -i 10 -d 50000 /mnt/storage3/aman/hicpro2juicebox/data.allValidPairs.hic ~/hiccups_results/\\njava -jar ~/juicer/CPU/common/juicer_too -20.00.jar hiccups --cpu --threads 16 -r 5000, 25000 -f @.3@ -p 1 -i 10 -d 50000 /mnt/storage3/aman/hicpro2juicebox/data.allValidPairs.hic ~/hiccups_results/\\n\\njava -jar ~/juicer/CPU/common/juicer_tools.2.20.0@.jar hiccups --cpu --threads 16 -r 25000 -f @.3@ -p 1 -i 10 -d 50000 /mnt/storage3/aman/hicpro2juicebox/data.allValidPairs.hic ~/hiccups_results/\\n\\nnano /home/aman/hiccups_results/enriched_pixels_25000.bedpe\\njava -jar ~/juicer/CPU/common/juicer_tools.2.20.0@.jar hiccups --cpu --threads 16 -m 512 -c all -r 5000,10000 -k KR -f .1,.1 -p 4,2 -i 7,5 -t @.02,1.5,1.75,2 -d 20000,20000,50000 /mnt/storage3/aman/hicpro2jui\\ncebox/data.allValidPairs.hic ~/hiccups_optimized_results/\\njava -jar ~/juicer/CPU/common/juicer_tools.2.20.0@.jar hiccups --cpu --threads 16 -m 512 -c all -r 5000,10000 -k KR -f .1,.1 -p 4,2 -i 7,5 -t @.02,1.5,1.75,2 -d 20000, 25000,50000 /mnt/storage3/aman/hicpro2jui\\ncebox/data.allValidPairs.hic ~/hiccups_optimized_results/\\njava -jar ~/juicer/CPU/common/juicer_tools.2.20.0@.jar hiccups --cpu --threads 16 -m 512 -c all -r 5000,10000 -k KR -f .1,.1 -p 4,2 -i 7,5 -t @.02,1.5,1.75,2 -d 20000,50000 /mnt/storage3/aman/hicpro2juicebox/\\ndata.allValidPairs.hic ~/hiccups_optimized_results/\\njava -jar ~/juicer/CPU/common/juicer_tools.2.20.0@.jar hiccups --cpu --threads 16 -r 5000,10000 -k KR -f .1 -p 4 -i 7 -t @.02,1.5,1.75,2 -d 20000 /mnt/storage3/aman/hicpro2juicebox/data.allValidPairs.hic ~/hi\\nccups_optimized_results/\\njava -jar ~/juicer/CPU/common/juicer_tools.2.20.0@.jar hiccups --cpu --threads 16 -r 5000,10000 -f .1 -p 4 -i 7 -t @.02,1.5,1.75,2 -d 20000 /mnt/storage3/aman/hicpro2juicebox/data.allValidPairs.hic ~/hiccups_\\noptimized_results/\\njava -jar ~/juicer/CPU/common/juicer_tools.2.20.0@.jar hiccups --cpu --threads 16 -r 5000,10000 -f 2 -p 4 -i 7 -t @.02,1.5,1.75,2 -d 20000 /mnt/storage3/aman/hicpro2juicebox/data.allValidPairs.hic ~/hiccups_o\\nptimized_results/\\n\\ncd ~/hiccups_optimized_results/\\njava -jar ~/juicer/CPU/common/juicer_tools.2.20.0@.jar hiccups --cpu --threads 16 -r 5000,10000 -f @.2 -p 4 -i 7 -t 0.02,1.5,1.75,2 -d 20000 /mnt/storage3/aman/hicpro2juicebox/data.allValidPairs.hic ~/hiccup\\n_optimized_results/\\n\\n~/juicer/CPU/common/juicer_tools.2.20.0@.jar hiccups -h\\n\\n~/juicer/CPU/common/juicer_tools.2.20.0@.jar hiccups\\n\\ncat ~/.bash_history | hiccups\\n\\ncat ~/.bash_history | grep hiccups\\n\\njava -jar ~/juicer/CPU/common/juicer_tools.2.20.0@.jar hiccups --cpu --threads 16 -r 10000 -i /mnt/storage3/aman/data.allValidPairs.hic ~/hiccups2_10kb/\\n\\njava -jar ~/juicer/CPU/common/juicer_tools.2.20.0@.jar hiccups --cpu --threads 16 -i /mnt/storage3/aman/data.allValidPairs.hic ~/hiccups2_1@kb/\\n\\nmkdir hiccups2_1@kb\\n\\njava -jar ~/juicer/CPU/common/juicer_tools.2.20.0@.jar hiccups --cpu --threads 16 -r 10000 -i /mnt/storage3/aman/data.allValidPairs.hic ~/hiccups2_10kb/\\n\\njava -jar ~/juicer/CPU/common/juicer_tools.2.20.0@.jar hiccups --cpu --threads 16 -r 10000 /mnt/storage3/aman/data.allValidPairs.hic ~/hiccups2_10kb/\\n\\n1s -lh ~/hiccups2_1@kb/\\n\\nwe -l ~/hiccups2_10kb/fdr_thresholds_10000\\n\\njava -jar ~/juicer/CPU/common/juicer_tools.2.20.0@.jar hiccups --cpu --threads 16 -r 10000 /mnt/storage3/aman/data.allValidPairs.hic ~/hiccups2_10kb/\\n\\njava -jar ~/juicer/CPU/common/juicer_tools.2.20.0@.jar hiccups --cpu --threads 16 /mnt/storage3/aman/data.allValidPairs.hic ~/hiccups2_10kb/\\n\\n1s ~/hiccups2_10kb/\\n\\nwe -l1 ~/hiccups2_10kb/*\\n\\ncat ~/hiccups2_10kb/fdr_thresholds_5000\\n\\n1s -ltrh ~/hiccups2_10kb/\\n\\n(base) aman@unicorn:~/fihic_bias$ java -jar ~/juicer/CPU/common/juicer_tools.2.20.0@.jar hiccups --cpu --threads 16 -r 1000@ -i /mnt/storage3/aman/data.allValidPairs.hic ~/hiccupsfinal_10kb/\\n\\nWARNING: sun.reflect.Reflection.getCallerClass is not supported. This will impact performance.\\n\\nWARN [2025-@2-15T11:23:46,503] [Globals.java:138] [main] Development mode is enabled\\n\\nUsage: juicer_tools hiccups [-m matrixSize] [-k normalization (NONE/VC/VC_SQRT/KR)] [-c chromosome(s)] [-r resolution(s)] [--restrict] [-f fdr] [-p peak width] [-i window] [-t thresholds] [-d centroid dista\\nneces] <hicFile> <outputDirectory> [specified_loop_list]\\n\\n(base) aman@unicorn:~/fihic_bias$ java -jar ~/juicer/CPU/common/juicer_tools.2.20.0@.jar hiccups --cpu --threads 16 -r 10000 /mnt/storage3/aman/data.allValidPairs.hic ~/hiccupsfinal_10kb/\\n\\nWARNING: sun.reflect.Reflection.getCallerClass is not supported. This will impact performance.\\n\\nWARN [2025-@2-15T11:24:14,443] [Globals.java:138] [main] Development mode is enabled\\n\\nReading file: /mnt/storage3/aman/data.allValidPairs.hic\\n\\nUsing the following configurations for HiCCUPS:\\n\\nConfig res: 10000 peak: 2 window: 5 fdr: 10% radius: 20000\\n\\nWARNING - You are using the CPU version of HiCCUPS.\\n\\nThe GPU version of HiCCUPS is the official version and has been tested extensively.\\n\\nThe CPU version only searches for loops within 8MB (by default) of the diagonal and is still experimental.\\n\\nUsing 16 CPU thread(s) for primary task\\n\\nWarning Hi-C map may be too sparse to find many loops via HiCCUPS.\\n\\nRunning HiCCUPS for resolution 10000\\n\\njava -jar ~/juicer/CPU/common/juicer_too\\njava -jar ~/juicer/CPU/common/juicer_too\\njava -jar ~/juicer/CPU/common/juicer_too\\njava -jar ~/juicer/CPU/common/juicer_too\\njava -jar ~/juicer/CPU/common/juicer_too\\njava -jar ~/juicer/CPU/common/juicer_too\\njava -jar ~/juicer/CPU/common/juicer_too\\njava -jar ~/juicer/CPU/common/juicer_too\\njava -jar ~/juicer/CPU/common/juicer_too\\n\\nDHOHDHHHHHHHD\\nNNNNNNNNNNN\\n\\n',\n",
|
||
" 'Cannot Connect to R Session\\n\\nx) Could not connect to the R session on RS\\n\\nServer.\\n\\nError occurred during transmission (6)\\n\\n',\n",
|
||
" 'Alignment and\\nChimera Handling\\n\\nSS. SS\\n\\n',\n",
|
||
" 'Variable population size\\n\\nBeyond the Standard Neutral Model\\n\\nSlow fluctuations\\nin population size : = =\\n\\n4 Need:\\nA, 7 T << min[N, |\\n\\n',\n",
|
||
" '@ Vivaldi\\n\\nat)\\n\\nv Speed Dial\\n\\nFile Edit View Bookmarks\\n\\nS\\n\\n&% Workspaces v\\n\\n—- > a U8\\n\\nv Imported From... YY Imported From...\\n\\n© sic all pats\\n\\nSources\\n\\n+ Add source\\n\\nSelect all sources\\n\\n]\\n\\nFormulae.pdf\\n\\nLecture O.pdf\\n\\nLecture 1.pdf\\n\\nLecture 1_1.pdf\\n\\nLecture 2.pdf\\n\\nLecture 3.pdf\\n\\nLecture 4.pdf\\n\\nLecture 5.pdf\\n\\nLecture 6.pdf\\n\\n0 @ 0 ®\\n\\nMail\\n\\n@ Cool Guides\\n\\ngoogle.com\\n\\nOnline Bewerbung QGIS API Docume...\\n\\nOo\\n\\nTools Window Help\\n\\nStartpage Search Results Kurs: Evolutionsgenetik de § NotebookLM\\n\\nom {fl v\\n\\nqgis - Trying to pe... New Script - Earth...\\n\\nChat @ Refresh\\n\\nexplain more about the following as per the sources\\n- 1. The change in allele frequency due to selection\\n2. The probability of fixation for an allele with\\nselection advantage and the time to fixation 3. The\\nequilibrium frequencies for both alleles in\\noverdominance\\n\\nHere\\'s more information about allele frequency\\nchange due to selection, the probability and time to\\nfixation of an allele with a selective advantage, and\\nequilibrium frequencies in overdominance, based on\\nthe provided sources:\\n1. The change in allele frequency due to selection\\n* General principle Natural selection causes\\nchanges in allele frequencies from one generation\\nto the next 1... . Fitter individuals produce more\\nviable offspring, influencing future generations 3.\\n- Fisher\\'s theorem The fitness of the population is\\n\\nmaximized 4\\n\\n14 sources\\n\\nStart typing...\\n\\n°©@\\n\\nDescribe Wright-Fisher model assumptions abou >\\n\\nPastebin.com - #1...\\n\\nSo\\nte)\\n\\n$vertr? 6 8B &@ SB FQ\\n\\n® EvolG all pdfs - Notebook BAKSMMaTi ela dale most rel is\\n\\nQ: Search Startpage v @\\nTargetP 2.0-DTU... https://www.mood... OnePlus12R revie... Whois “Indian\"in.. vA\\n«~ Share 3 Settings CO\\nStudio > Note a\\n\\nGenomic Signatures of Natural Selection\\n\\n(Saved responses are view only)\\n\\na reduction in variation [9].\\n2. Detecting Selection: Neutrality Tests\\n\\n- Neutrality tests compare observed data to theoretical\\nexpectations under a neutral model (where alleles are not under\\nselection) to detect if selection has occurred [10].\\n\\n* Comparing multiple loci across the genome: Because selection\\nacts on few genes, loci under selection should be outliers when\\ncompared to other parts of the genome [10, 11].\\n\\n* Tajima\\'s D: This test compares two estimators of the population\\nmutation rate, 6 (theta). Tajima\\'s D will be:\\n\\n* Negative when there is an excess of rare alleles, suggesting a\\nrecent selective sweep or population expansion [5, 12].\\n\\n+ Positive when there is an excess of intermediate-frequency\\nalleles, suggesting balancing selection, population structure,\\nor a recent bottleneck [8, 9, 12].\\n\\n* Zero when a population is in equilibrium, and evolving\\nneutrally [12].\\n\\n* McDonald-Kreitman (MK) test: Compares the ratio of non-\\nsynonymous (amino acid changing) to synonymous (silent)\\nsubstitutions within a species and between species. A higher ratio\\nbetween species compared to within species suggests positive\\nselection, and a lower ratio suggests purifying selection [13, 14].\\n\\n* Site Frequency Spectrum (SFS): The distribution of allele\\nfrequencies in a sample. Selection leaves specific sianatures in the\\n\\nG) Convert to source\\n\\nNotebookLM can be inaccurate; please double check its responses.\\n\\n+\\nQ\\n\\nQC) Co reset —O—— 100 %\\n\\nc\\n\\nMon Feb 10 16:00\\n\\naw\\n\\nv\\n\\nO © HD\\n\\nQD\\n\\nee\\n\\nQu qa\\n\\nOH O®e< GO 8W OW A\\n\\n&\\n\\n16:00\\n\\n”\\n',\n",
|
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" '100 MB\\n\\n200 MB\\n\\n300 MB\\n\\n100 MB\\n\\n200 MB\\n\\n300 MB\\n\\n',\n",
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" 'Building regulatory landscapes\\nreveals that an enhancer can recruit\\ncohesin to create contact domains,\\nengage CTCF sites and activate\\ndistant genes\\n\\nRinzema NJ, Sofiados k, [...], de Laat W\\n\\nNature Structural & Molecular Biology (2022)\\n\\n[| DOWNLOAD | 2022\\n\\nRobust detection of translocations in\\nlymphoma FFPE samples using\\ntargeted locus capture-based\\nsequencing\\n\\nAllahyar A, Pieterse M, [...], de Laat W\\n\\nNATURE COMMUNICATIONS: 12:3361\\n\\n[| DOWNLOAD | 2021\\n\\nReady-to-use public infrastructure\\nfor global SARS-CoV-2 monitoring\\nKrijger PHL, Hoek TA, [...], de Laat W, Tanenbaum M\\n\\nNature Biotechnology 39: 1178-1184\\n\\n[| DOWNLOAD | 2021\\n\\nNovel orthogonal methods to\\nuncover the complexity and diversity\\nof nuclear architecture\\n\\nTjalsma SJD, de Laat W\\n\\nCurrent Opinion in Genetics & Development: 67:10-17\\n\\n[| DOWNLOAD | 2021\\n\\nInterplay between CTCF boundaries\\nand a super enhancer controls\\ncohesin extrusion trajectories and\\ngene expression\\n\\nVos ESM, Valdes-Quezada C, Huang Y [...], de Laat\\nWw\\n\\nMol. Cell 81(15):3082-3095\\n\\n[| DOWNLOAD | 2021\\n\\nHow chromosome topologies get\\ntheir shape: views from proximity\\nligation and microscopy methods\\nHuang Y, Neijts R, de Laat W\\n\\nFEBS Letters: 594 3439-3449\\n\\n[| DOWNLOAD | 2020\\n',\n",
|
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" 'SPRINGER NATURE Link\\n\\nFind ajournal Publishwithus Track your research Q Search\\n\\nHome > Genome Biology > Article\\n\\nHiC-Pro: an optimized and flexible pipeline\\nfor Hi-C data processing\\n\\nSoftware | Openaccess | Published: 01 December 2015\\nVolume 16, articlenumber 259, (2015) Cite this article\\n\\nDownload PDF @ You have full access to this open access article\\n\\nNicolas Servant 4, Nelle Varoquaux, Bryan R. Lajoie, Eric Viara, Chong-Jian Chen, Jean-Philippe Vert,\\nEdith Heard, Job Dekker & Emmanuel Barillot\\n\\nS) 65k Accesses f) 1404 Citations & 19 Altmetric & 3 Mentions Exploreall metrics >\\n\\nAbstract\\n\\n',\n",
|
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" '#ALL vcf files\\nls -ltrh *xbowtie.vcf\\n\\n—rw-rw-r-—\\n—rw-rw-r-—\\n—rw-rw-r-—\\n—rw-rw-r-—\\n—rw-rw-r-—\\n\\n1 pst14 pst14 2.3M Jan\\n1 pst14 pst14 2.8M Jan\\n1 pst14 pst14 61K Jan\\n1 pst14 pst14 988K Jan\\n1 pst14 pst14 6.3M Jan\\n\\n25\\n25\\n25\\n25\\n25\\n\\n17:59\\n18:07\\n18:12\\n18:17\\n18:21\\n\\nsample1_bowtie.\\nsample2_bowtie.\\nsample3_bowtie.\\nsample4_bowtie.\\nsample5_bowtie.\\n\\nvcf\\nvcf\\nvcf\\nvcf\\nvcf\\n',\n",
|
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" '3. What sort of growth pattern in the epidermis would explain\\nthe kink formation?\\n\\n°\\n3.1. Is there any cellular evidence for PD growth signal in epidermis?\\n\\n',\n",
|
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" \"zcat FitHiC.spline_pass1.res20000.significances.txt.gz | awk '($1 ~ /*[1-9]$|*10$/ || $1 ~ /*B73V4_ctg[1-9]$|*B73V4_ctg10$/) && \\\\\\n($3 ~ /*[1-91$]*10$/ || $3 ~ /*B73V4_ctg[1-9]$|*B73V4_ctg10$/)' | head\\n\\n1 10000 61 30000 861 1.000000e+00 1.000000e+00 1. 000000e+00 1. 000000e+00 26.440884\\n\\n1 10000 61 2470000 1 3.096889e-01 1.000000e+00 1. 000000e+00 1. 000000e+00 @.370613\\n\\n1 10000 61 3070000 1 2.609742e-01 1.000000e+00 1. 000000e+00 1. 000000e+00 @.302423\\n\\n1 10000 61 3130000 1 2.569911e-01 1.000000e+00 1. 000000e+00 1. 000000e+00 Q@.297047\\n\\n1 10000 61 6270000 1 1.502626e-01 1.000000e+00 1. 000000e+00 1. 000000e+00 @.162828\\n\\n1 10000 61 7470000 1 1.350215e-01 1.000000e+00 1. 000000e+00 1. 000000e+00 @.145051\\n\\n1 10000 61 8790000 1 1.343277e-@1 1.000000e+00 1. 000000e+00 1. 000000e+00 @.144249\\n\\n1 10000 61 20830000 1 8.989326e-02 1. 000000e+00 1.000000e+00 1. 000000e+00 @.094193\\n1 10000 61 24010000 1 6.377743e-02 1. 000000e+00 1.000000e+00 1. 000000e+00 @.065902\\n1 10000 61 27510000 1 5.066134e-02 1. 000000e+00 1.000000e+00 1. 000000e+00 @.051990\\n\\nInter-chromosomal ones\\n\\n($3 ~ /A[1-91$|*10$|*B73V4_ctg[1-91$|*B73V4_ctg10$/) && \\\\\\n($1 != $3) && ($7 <= 0.05)* | we -L iG)\\n\\n485\\n\\n(F + @2@gq®\\nIntra-chromosomal ones ‘VA FE\\n\\n($3 ~ /*[1-9]$|*10$ |*B73V4_ctg[1-9]$|*B73V4_ctg10$/) && \\\\\\n($1 == $3) && ($7 <= 0.05)' | we -l °©\\n\\n89531\\n\",\n",
|
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" 'In [36]:\\n\\n%%sbash\\n\\nhead /mnt/storage3/aman/wdbasejuicer_new/hiccups_output/postprocessed_pixels_10000.bedpe\\n\\n#chr1— x1 x2 chr2\\nexpectedDonut expectedH\\ncentroid2 radius\\n\\n# juicer_tools version 2.20.00\\n10 6090000 6100000 10\\n6.738838 8.369542\\n\\n6098333 6208333 7454\\n\\n10 139920000 13993000\\n55,255 62.0 8.725843\\n455184E-15 9.31793E-40\\n\\n10 76000000 76010000\\n55,255 57.0 9.344456\\n203114E-17 2.29482E-25\\n\\n10 149390000 14940000\\n55,255 56.0 5.521386\\n702141E-16 2.387457E-16\\n\\n10 136480000 13649000\\n55,255 56.0 5 8624353\\n23398E-20 1.2297154E-24\\n\\n10 148200000 14821000\\n55,255 55.0 78222165\\n\\n19 8.71397@5E-12 2\\n\\n10 145390000 14540000\\n55,255 52.0 9.858375\\n\\n17 1.6487045E-21 2\\n\\n10 143300000 14331000\\n55,255 48.0 7.270913\\n923472E-15 8.827955E-12\\n\\nyl y2 name score strand1 strand2 color observed expectedBL\\nexpectedV fdrBL fdrDonut fdrH fdrv numCollapsed centroid1\\n6200000 6210000 . . : : @,255,255 69.0 7.9115663\\n13.515236 1.45373255E-30 5.202941E-36 3.1267008E-30 1.2960435E-19 3\\n0 10 139980000 139990000 : : : . 0,2\\n7.795326 15.521655 4.7749968 5.0803407E-25 6.842732E-30 1.4\\n3 139925000 139985000 10000\\n\\n10 76080000 76090000 : : : . 0,2\\n8.861963 11.599155 7.0608373 2.5698042E-21 1.734446E-21 4.3\\n6 76006666 76076666 14337\\n0 10 149450000 149460000 : : : . 0,2\\n7.006336 10.389031 11.967166 2.12049@5E-29 6.3991415E-25 1.6\\n4 149390000 149450000 7071\\n0 10 136880000 136890000 : : : . 0,2\\n4.0235314 9.664011 6.9882493 2.1204905E-29 8.194439E-34 2.8\\n7 136483571 136879285 16659\\n0 10 148260000 148270000 : : : . 0,2\\n9.238162 9.26983 14.654494 6.932115E-24 3.9216012E-20 1.4314703E-\\n148205000 148260000 5000\\n0 10 145440000 145450000 : : : . 0,2\\n6.957423 8.590018 6.5711 5.5672264E-14 6.6677316E-22 1.1138844E-\\n145395000 145450000 5000\\n0 10 143360000 143370000 : : : . 0,2\\n55802155 8.395383 12.302593 1.2983397E-18 1.498726E-22 4.4\\n2 143310000 143365000 5000\\n',\n",
|
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" 'a\\n\\nI\\nI\\n.* a\\n\\nLIEN TIE uc\\n\\nolathe id- \"4 ut ]\\nFigure 2 | Haplotype pattern in a region defined by SNPs that are at high\\nfrequency in Tibetans and at low frequency in Han Chinese. Each column is\\na polymorphic genomic location (95 in total), each row is a phased haplotype\\n(80 Han and 80 Tibetan haplotypes), and the coloured column on the left\\ndenotes the population identity of the individuals. Haplotypes of the Denisovan\\nindividual are shown in the top two rows (green). The black cells represent the\\npresence of the derived allele and the grey space represents the presence of\\nthe ancestral allele (see Methods). The first and last columns correspond to the\\nfirst and last positions in Supplementary Table 3, respectively. The red and\\nblue arrows indicate the 32 sites in Supplementary Table 3. The blue arrows\\nrepresent a five-SNP haplotype block defined by the first five SNPs in the\\n32.7-kb region. Asterisks indicate sites at which Tibetans share a derived allele\\nwith the Denisovan individual.\\n\\n',\n",
|
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" \"© Pupiisn\\n\\n10-\\n\\ngroup\\n° 1G\\n\\nBOUBUBA %LZ 'ZOd\\n\\n\",\n",
|
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" '(a:\\n\\nimport pandas as pd\\nimport matplotlib.pyplot as plt\\n\\n# Load the BEDPE file\\nfile_path = \"/mnt/storage3/aman/wdbasejuicer_new/hiccups_output/merged_loops. bedpe\"\\ncolumns = [\\n\"chromosome\", \"x1\", \"x2\", \"chromosome2\",\\n“name\", \"score\", “strand1\", \"strand2\",\\n\"color\", \"observed\", \"expectedBL\", \"expectedDonut\",\\n“expectedH\", “expectedV\", \"fdrBL\", \"fdrDonut\"\\n\"fdr\", “fdrv\", “number_collapsed\", “centroidi\", “centroid2\", “radiu\\n\\n\"ya\",\\n\\ndata = pd.read_csv(file_path, sep=\"\\\\t\", comment=\"#\", names=columns)\\n\\n# Convert relevant columns to numeric\\nnumeric_cols = [\"x1\", \"x2\", \"yl\", \"y2\", “observed\"]\\ndata{numeric_cols] = data(numeric_cols].apply(pd.to_numeric, errors=\"coerce\")\\n\\n# Calculate genomic distance\\n\\ndata{\"midpoint_upstream\")] = (data[\"x1\"] + data[\"x2\"]) / 2\\n\\ndata{\"midpoint_downstream\"] = (datal\"\"y1\") + datal\"y2\"]) / 2\\n\\ndata{\"genomic_distance\"] = abs(data|\"midpoint_downstream\"] - data|\"\\'midpoint_upstream\"])\\n\\n# Drop rows with missing values in observed or genomic distance\\ndata = data.dropna(subset=(\"genomic_distance\", \"observed\"])\\n\\n# Convert columns to numeric for plotting\\ndata[\"genomic_distance\"] = data[\"genomic_distance\"].astype( float)\\ndata[\"observed\"] = data|\"observed\").astype( float)\\n\\n# Plot the relationship between genomic distance and observed counts\\n\\nplt. figure(figsize=(10, 6))\\n\\nplt.scatter(data[\"genomic_distance\"], data(\"observed\"], alpha=0.5, s=10)\\nplt.xscale(\"Log\"\\n\\nplt.yscale(\"Log\")\\n\\nplt.title(\"Genomic Distance vs Observed Counts\")|\\n\\nplt.xlabel(\"Genomic Distance (bp)\")\\n\\nplt.ylabel(\"Observed Contact Count\")\\n\\nplt.grid(True, which=\"both\", linestyle=\"——\", Linewidth=0.5)\\n\\nplt.\\n\\nshow()\\n\\notVvVSa PE\\n\\nObserved Contact Count\\n\\nGenomic Distance vs Observed Counts\\n\\n10?\\n\\n10\\n\\n108 108\\nGenomic Distance (bp)\\n\\n',\n",
|
||
" 'In [14]:\\n\\nprint(gt_h[:5,:5])\\n\\nTraceback (most recent call last)\\nInput In [14], in <cell line: 1>()\\n-———> 1\\n\\nFile ~/.conda/envs/test_allel_env/1lib/python3.9/site-packages/allel/abc.py:285, in DisplayableArray.__str__(self)\\n284 def _str_(self):\\n——> 285 return\\n\\nFile ~/.conda/envs/test_allel_env/1lib/python3.9/site-packages/allel/abc.py:392, in DisplayAs2D.to_str(self, row_thr\\neshold, col_threshold, row_edgeitems, col_edgeitems)\\n\\nfor row in items:\\n\\nFile ~/.conda/envs/test_allel_env/lib/python3.9/site-packages/allel/abc.py:372, in DisplayAs2D.get_display_items(se\\nlf, row_threshold, col_threshold, row_edgeitems, col_edgeitems)\\n\\n370 else:\\n371 row_indices = list(range(self.shape[@]))\\n--> 372 items =\\n\\n374 # determine col indices of items to show\\n375 if self.shape[1] > col_threshold:\\n\\nFile ~/.conda/envs/test_allel_env/1lib/python3.9/site-packages/allel/model/ndarray.py:2151, in HaplotypeArray.str_it\\nems (self)\\n\\n2149 max_allele = 1\\n\\n2150 n = int(np.floor(np.log10(max_allele))) + 1\\n-> 2151 t = values astype ((ipaStRIngE n))\\n\\n2152 # recode missing alleles\\n\\n2153 t[lvalues < 0] = ‘\\n\\nFile ~/.conda/envs/test_allel_env/lib/python3.9/site-packages/numpy/__init__.py:397, in __getattr__(attr)\\n\\n394 raise AttributeError(__former_attrs__[attr])\\n396 if attr in _expired_attributes_:\\n-—> 397 raise AttributeError(\\n398 f\"*np.{attr}*> was removed in the NumPy 2.@ release. \"\\n399 f\"{__expired_attributes_ [attr]}\"\\n400 )\\n402 if attr \"chararray\":\\n403 warnings.warn(\\n404 \"“np.chararray’ is deprecated and will be removed from \"\\n405 “the main namespace in the future. Use an array with a string \"\\n406 \"or bytes dtype instead.\", DeprecationWarning, stacklevel=2)\\n\\nAttributeError: ‘np.string_* was removed in the NumPy 2.0 release. Use ‘np.bytes_* instead.\\n\\n',\n",
|
||
" 'Qualimap Report: BAM QC\\n\\nCONTENTS\\n\\nInput data and parameters\\n\\nInput data & parameters\\n\\nQualiMap command line\\nqualimap bamqc -bam sample1_sorted.bam -nw 400 -hm 3 Summary\\nAlignment Coverage across reference\\nbwa mem q\\nCoverage Histogram\\nCommand /data/proj2/home/students/pst14/ref_gen/sample1/ncbi_dataset/data/GCA_011696235. GCA Olavsvess.a_norissvsvesva_yonunmennia\\n\\nline: /data/proj2/home/students/pst14/illegal_logging_trees/fastqc_raw/trimmomatic/wood_sample_1/Wood sample 1 forward paired.fa.az\\n/data/proj2/home/students/pst14/illegal_logging_trees/fastqc_raw/trimmomatic/wood_sample_1/v Coverage Histogram (0-50X)\\nDraw . .\\nGenome Fraction Coverage\\nchromosome no\\nlimits: °\\nDuplication Rate Histogram r\\nAnalyze\\noverlapping\\n. no Mapped Reads Nucleotide Content\\npaired-end\\nreads: .\\nMapped Reads GC-content Distribution —\\nProgram: bwa (0.7.17-r1198-dirty)\\nAnalysis date: Wed Jan 22 07:39:07 CET 2025 Mapped Reads Clipping Profile\\nSize of a °\\n3 Homopolymer Indels\\nhomopolymer:\\nSkip duplicate | Mapping Quality Across Reference\\nalignments:\\nNumber of 400 Mapping Quality Histogram\\nwindows: .\\nBAM file: samplet_sorted.bam Insert Size Across Reference\\nInsert Size Histogram\\nSummary\\nGlobals\\nReference size 809,993,317\\nNumber of reads 377,652\\n\\nMapped reads\\n\\n371,463 / 98.36%\\n\\nUnmapped reads 6,189 / 1.64%\\n\\nMapped paired reads 371,463 / 98.36%\\n\\n',\n",
|
||
" '',\n",
|
||
" '@ Mainwindow\\n\\nOmeoerork oe oO =\\n\\n[Juicebox 2.17.00] Hi-C Map <9>: inter.hic\\n\\nView Bookmarks\\nChromosomes\\n\\nAssembly Dev\\n\\nNormalization (Obs | Ctrl) Resolution (BP) Color Range\\n\\nF Q SBS MonNov4 20:49\\n\\n2.5MB 500KB 100KB 25KB 5KB\\n\\n156,000 KB 155,000 KB 154,000 KB 153,000 KB 152,000 KB 151,000 KB\\n\\n157,000 KB\\n\\n1:159,230,001-159,240,000\\n1:153,390,001-153,400,000\\nobserved value (O) = 0.0\\nlexpected value (E) = 0.032\\nO/E =0\\n\\nLayerO <> | & |\\n\\nShow Annotation Panel\\n\\n',\n",
|
||
" 'Measuring DNA methylomes\\n\\n5 Input\\nx Mutant 2 mutant 21 i ee\\nparmazy|— 4H Uh lu ina\\ni\" ot saan Jil wi\\n& Mutant’ wide (tL Et i\\n5\\nSteps: Methy|Star pipeline\\n\\n1. WGBS of selected plant material\\n2. Align to reference genome (.fasta)\\n3. Determine per-cytosine 5mC levels\\n\\nStandard BS-Seq\\nWorkFlow\\n\\n',\n",
|
||
" 'In [21]: |# Retained - Blue, Removed - Red\\ndf <- FetchData(patient3, vars = c(\"nFeature_RNA\", “percent.mt\"))\\ndf$kept <- rownames(patient3) %in% colnames(patient3_transform)\\n\\nggplot(df, aes(x = nFeature_RNA, y = percent.mt, color = kept)) +\\ngeom_point(size = 0.5) +\\nscale_color_manual(values = c(\"red\", \"blue\")) +\\ntheme_minimal() +\\nlabs(title = \"QC: Kept (blue) vs Removed (red)\")\\n\\nError in ‘$<-.data. frame’ (**xtmp**, kept, value = c(FALSE, FALSE, FALSE, : replacement has 23691 rows, data has 5755\\nTraceback:\\n\\n1. ‘$<-.data.frame*(**tmp*x*, kept, value = c(FALSE, FALSE, FALSE,\\n. FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE,\\n. FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE,\\n',\n",
|
||
" 'Reference genome $$ eee eC CCC OOOO OCT OO a\\n\\n= Sa « al\\n’ ’\\nnn | Pullup (or grastso dactang\\n\\n>\\n-\\n\\n*\\n\\n—\\n- : =\\n. -\\n*\\nee\\n',\n",
|
||
" 'Sample\\n\\nwood_sample_1\\nwood_sample_2\\nwood_sample_3\\nwood_sample_4\\nwood_sample_5\\n\\nComplete BUSCOs |\\n\\n14\\n14\\n14\\n15\\n14\\n\\nMissing BUSCOs\\n\\n99\\n100\\n99\\n99\\n100\\n\\nTotal BUSCO Groups\\n\\n116\\n116\\n116\\n116\\n116\\n',\n",
|
||
" '®) ToruOkadaOi\\n\\nGD) nalakath.org\\n\\n',\n",
|
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" '195\\n196\\n197\\n198\\n199\\n200\\n201\\n202\\n202:1\\n\\nConsole\\n\\n# Standard steps in the Seurat workflow for visualization and clustering\\nzehn_s <- RunPCACzehn_s, npcs = 10, verbose = FALSE)\\nzehn_s <- RunUMAP(zehn_s, dims = 1:10, verbose = FALSE)\\n\\nzehn_s <- FindNeighbors(zehn_s, dims = 1:10, verbose = FALSE)\\nzehn_s <- FindClusters(zehn_s, verbose = FALSE)\\nDimPlot(zehn_s, label = TRUE)\\n\\n(Top Level) +\\n\\nTerminal Background Jobs\\n\\nR~R4.4.2 - ~/\\n\\na Leenieic Danediedheded edeeineiaaiiiaeshabadiedhsehehiieniedian iaehameaaainedseeimmmmneedsimimaemneneheeeeheneineehesebeetidmmertaaatmdhenetsameedimmenae\\n\\n> zehn_s <- RunPCA(zehn_s, npcs = 15, verbose = FALSE)\\nWarning: Number of dimensions changing from 2@ to 15\\n\\nVVVVVVv\\n\\nzehn_s <- RunUMAP(zehn_s, dims = 1:15, verbose = FALSE)\\n\\nzehn_s <- FindNeighbors(zehn_s, dims = 1:15, verbose = FALSE)\\n\\nzehn_s <- FindClusters(zehn_s, verbose = FALSE)\\n\\nDimPlot(zehn_s, label = TRUE)\\n\\n# Standard steps in the Seurat workflow for visualization and clustering\\nzehn_s <- RunPCAC(zehn_s, npcs = 10, verbose = FALSE)\\n\\nWarning: Number of dimensions changing from 15 to 10\\n\\nVv\\n\\nzehn_s <- RunUMAP(zehn_s, dims = 1:10, verbose = FALSE)\\nzehn_s <- FindNeighbors(zehn_s, dims = 1:10, verbose = FALSE)\\nzehn_s <- FindClusters(zehn_s, verbose = FALSE)\\nDimPlot(zehn_s, label = TRUE)\\n\\nR Script +\\n\\n1a\\n\\n\\\\& toplo 390 obs. of ¢ variables\\n\\n© zehn Large Seurat ( 10.9 MB)\\n\\n© zehn_o Large Seurat ( 3.2 MB)\\n\\n© zehn_s Large Seurat ( 13.6 MB)\\n\\nFiles Plots Packages Help Viewer Presentation elo\\n« P Zoom -XJExpot~ O % Publish ~\\n\\n',\n",
|
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" '[1]\\n\\n(4)\\n\\ntv)\\n\\n(4)\\n\\nprint(hic.getGenomeID())\\nprint(hic.getResolutions())\\n\\nhg19\\n[2500000, 1000000, 500000, 250000, 100000, 50000, 25000, 10000, 5000, 1000]\\n\\nnow print out the chromosomes in this file.\\n\\nfor chrom in hic.getChromosomes():\\nprint(chrom.name, chrom. length)\\n\\nAll 3098789\\n249250621\\n243199373\\n198022430\\n191154276\\n180915260\\n171115067\\n159138663\\n146364022\\n141213431\\n10 135534747\\n11 135006516\\n12 133851895\\n13 115169878\\n14 107349540\\n15 102531392\\n16 90354753\\n17 81195210\\n18 78077248\\n19 59128983\\n20 63025520\\n21 48129895\\n22 51304566\\nX 155270560\\nY 59373566\\nMT 16569\\n\\nCOIYAHAWNE\\n',\n",
|
||
" 'tn (oon: [aeaneig Lise 2\\n\\n15 (002) poly contig seta, roe)\\nUbbijcanea- tie; wo)\\n\\n=\\n=\\n=\\n=\\n=\\n=\\n\\n15 oes wget TR & comune\\n‘Hetiptsene Teaneig seta, patter = contig ise. 2),\\nI\\n\\nTrae Tee\\n\\nacon ig sn\\nyee tent tengtet), fant\\nobagT es titeaats tee\\n\\n(fine oer\\n\\nras, eae\\n\\n',\n",
|
||
" 'In [120]: combined_seurat <- combineExpression(\\ncombined_TCR,\\ncombined_seurat,\\ncloneCall = \"strict\",\\nproportion = FALSE\\n)\\n\\nError in combineExpression(combined_TCR, combined_seurat, cloneCall = \"strict\",\\nthere are groupings < 1\\nTraceback:\\n\\n: Adjust the cloneSize parameter —\\n\\n1. stop(\"Adjust the cloneSize parameter - there are groupings < 1\")\\n2. .handleSimpleError(function (cnd)\\n-f\\n\\nwatcher$capture_plot_and_output()\\n\\ncnd <- sanitize_call(cnd)\\n\\nwatcher$push(cnd)\\n\\nswitch(on_error, continue = invokeRestart(\"eval_continue\"),\\n\\nstop = invokeRestart(\"eval_stop\"), error = NULL)\\n. }, \"Adjust the cloneSize parameter - there are groupings < 1\",\\nbase: : quote(combineExpression(combined_TCR, combined_seurat,\\ncloneCall = \"strict\", proportion = FALSE)))\\n',\n",
|
||
" \"This slide is included to illustrate the complexities of analyzing genetic variation under bottleneck\\nscenarios and their impact on Tajima's D. It highlights two cases of bottlenecks: one where only a\\nsingle ancestral lineage survives (Case A) and another where multiple lineages persist through the\\nbottleneck (Case B). These scenarios lead to different patterns of genetic diversity and allele\\nfrequency distributions. In Case A, the severe reduction in population size results in a loss of\\ngenetic diversity, typically leading to 6, < Ow and D < 0, as low-frequency alleles dominate. In\\nCase B, where multiple lineages survive, the patterns can reverse, leading to 0, > Oy and D > 0\\n, as intermediate-frequency alleles become more common. The slide emphasizes the challenge of\\ninterpreting Tajima's D in bottleneck models due to these variable outcomes and underscores the\\n\\nneed for careful consideration of demographic history in genetic analyses.\\n\",\n",
|
||
" 'This slide illustrates the impact of manganese (Mn) deficiency on plants using chlorophyll a\\nfluorescence as a diagnostic tool. The top panel shows that Mn deficiency (-Mn) leads to visibly\\nstunted and less vibrant plant growth compared to control and other nutrient deficiencies (-Fe, -\\nMg). The corresponding F,y/ Fn values, which measure the maximum quantum efficiency of\\nphotosystem Il, are significantly lower in Mn-deficient plants (0.62) compared to control (0.82),\\nindicating impaired photosynthetic efficiency. The graph on the right tracks F’, /Fin over time,\\nhighlighting a dramatic decline in Mn-deficient plants around 28-35 days after planting, further\\n\\nemphasizing the critical role of Mn in photosynthesis and plant health.\\n',\n",
|
||
" 'Progeny genotypes\\n\\np2\\nPH\\n\\n=\\n—\\neo\\n\\n(1/4)H2 (1/2)H2 (1/4)H2\\nHQ HQ\\n\\n2(P+(1/2)H)\\n(P rads (Q +(1/2)H) (Q oo\\n\\n',\n",
|
||
" 'ose:\\n\\nJone] one Mylew peresqreoey\\n\\n3 4\\n\\none] uoyefigew peresare\\n\\n(asbo\\n\\n(ena)\\n\\nTsien]\\n\\n(asbo\\n\\n(Ena)\\n\\n(sen)\\n\\n(1600)\\n\\n(Ena\\n\\n(800)\\n\\n076\\n\\n5 3 B é\\n\\nona] uonefgiow peresqieooy\\n\\nsamples — methyome 16 sus TE — methylome merged WT_AL_TE — mothylome mett_TE samples — methyome 16 sus TE — methylome merged WT_AL_TE — mothylome mett_TE\\n\\nsamples — motyome16_suw5 TE — motrylome merged WT_AILTE — mthyome matt TE\\n',\n",
|
||
" '> # Rename columns to match scRepertoire expectations\\n> colnames(S1) <- cC\\n+ \"“cell_id\", \"total_read_count\", \"total_molecule_count\",\\n\"v_call\", \"j_call\", \"c_gene\", \"cdr3_nt\", \"cdr3\",\\n\"alpha_gamma_read_count\", \"alpha_gamma_molecule_count\",\\n\"beta_v_gene\", \"d_call\", \"beta_j_gene\", \"beta_c_gene\",\\n\"beta_cdr3_nt\", \"beta_cdr3\", \"beta_read_count\", \"beta_molecule_count\",\\n+ \"paired_chains\", \"cell_type\", \"high_quality\"\\n+)\\nError in names(x) <- value :\\n\\n\"names\\' attribute [21] must be the same length as the vector [1]\\n\\n++ t+\\n',\n",
|
||
" 'Observed Contact Count\\n\\nGenomic Distance vs Observed Counts\\n\\n—® Median Contact Count\\n\\n10:4\\n\\nuu —.—.—+ — - - —, - - —____—\\n105 10°\\nGenomic Distance (bp)\\n\\n102\\n\\n10!\\n\\n10°\\n\\nLog Density\\n',\n",
|
||
" 'sampledists <- dist(t(assay(vsd)))\\n\\nlibrary(\"Rcolorsr ewer”)\\nsampleDistMatrix <- as.matrix(sampleDists)\\nrownames(samplebistwatrix) <- vsdScondition I\\ncolnames(sampledistmatrix) <- NULL\\ncolors <- colorramppalette( rev(brewer.pal(9, “Blues”)) )(255)\\npheatmap(sampleDistmatrix,\\n\\nCluster ing_distance_rows=sampleDists,\\n\\ncluster ing_distance_cols=sampleDists,\\n\\ncol=colors)\\n\\nplotPca(vsd, intgroup-c(“condition”))\\n',\n",
|
||
" 'Broad range of functions\\n\\nMorrison 2009\\n\\n| Targeting of histone\\nmodification and variants\\n\\nRecruitment by\\n\\ntranscription factor\\n\\nTransient ass On\\nwith activating factors |\\n\\nUnits |\\n\\nSwapping of subt\\n\\nSubunit p: ranslational\\n\\nmodification\\n\\n',\n",
|
||
" 'STUDY INFORMATION SYSTEM Quick links” Give feedback! @ ENGIEST Q Search for study programme or course\\n\\nAman Shamil Nalakath Uni-to\\nTAL General information v My study information v ag | TEHNIKAULIKOOL @)\\nTECH 245633LV v\\nPERSONAL MESSAGES (NEW)\\n261 Esita avaldus 29. novembriks: saa rahvusvaheline 6pikogemus kodust lahkumata!Apply by November 29: take part in an international learning experience in the comfort of\\n\\nyour home... view\\n\\n29.10 Esita avaldus 29. novembriks: saa rahvusvaheline 6pikogemus kodust lahkumata!/Apply by November 29: take part in an international learning experience in the comfort\\nof your home... view\\n\\n09.09 Opingukava muutus / Study plan changed... view\\n\\n05.09 Opingukava muutus / Study plan changed... view\\n\\nSTUDY PLAN ( SUBMITTED)\\n\\nTitle (course code) ECTS credits\\nBioinformatics II (LKGOO50) 6.0\\n\\nCourses in total: 6.0 ECTS credits\\n\\nLATEST GRADES / PASS-FAIL ASSESSMENTS\\n\\n2312.2024 5 Bioinformatics Il (LKGOO50) Airi Rump 6.0 ECTS credits\\n\\nGrade point average: 5.000\\n',\n",
|
||
" 'Article | Published: 18 November 2019\\n\\nWidespread long-range cis-regulatory elements in the\\nmaize genome\\n\\nWilliam A. Ricci, Zefu Lu, Lexiang Ji, Alexandre P. Marand, Christina L. Ethridge, Nathalie G. Murphy,\\n\\nJaclyn M. Noshay, Mary Galli, Maria Katherine Mejia-Guerra, Maria Colomé-Tatché, Frank Johannes, M.\\n\\nJordan Rowley, Victor G. Corces, Jixian Zhai, Michael J. Scanlon, Edward S. Buckler, Andrea Gallavotti,\\n\\nNathan M. Springer, Robert J. Schmitz 4 & Xiaoyu Zhang 4\\n\\nNature Plants 5, 1237-1249 (2019) | Cite this article\\n\\n19k Accesses | 136 Altmetric | Metrics\\n',\n",
|
||
" 'Assigning cell type identity to clusters\\n\\nFortunately in the case of this dataset, we can use canonical markers to easily match the unbiased clustering to known cell types:\\n\\nCluster ID Markers Cell Type\\n0 IL7R, CCR7 Naive CD4+T\\n\\n1 cD14, LYZ CD14+ Mono\\n\\n2 IL7R,S100A4 Memory CD4+\\n3 MS4a1 B\\n\\n4 cD8A CD8+T\\n\\n5 FCGR3A,MS4A7_ FCGR3A+ Mono\\n6 GNLY, NKG7 NK\\n\\n7 FCERIA,CST3. DC\\n\\n8 PPBP Platelet\\n\\nnew.cluster.ids <- c(\"Naive CD4 T\"\\n\"NK\", \"DC\", \"Platelet\")\\n\\nnames (new. cluster. ids) <- levels (pbmc)\\n\\npbmc <- RenameIdents(pbmc, new.cluster. ids)\\n\\nDimPlot(pbmc, reduction = \"umap\", label = TRUE, pt.size = 0.5) + NoLegend()\\n\\n\"CD14+ Mono\", “Memory CD4 T\", \"B\", \"CD8 T\", \"FCGR3A+ Mono\",\\n\\n10\\n\\numap_1\\n\\nLibrary (ggplot2)\\nplot <- DimPlot(pbmc, reduction = \"umap\", label = TRUE, label.size = 4.5) + xlab(\"UMAP 1\") + ylab\\n(“UMAP 2\") +\\n\\ntheme(axis.title = element_text(size = 18), legend.text = element_text(size = 18)) + guides(c\\nolour = guide_legend(override.aes = list(size = 10)))\\nggsave( filename = \"../output/images/pbmc3k_umap.jpg\", height\\ny = 50)\\n\\n7, width = 12, plot = plot, qualit\\n\\nsaveRDS(pbmc, file = \"../output/pbmc3k_final. rds\")\\n',\n",
|
||
" \"Effects of Population Size on Genetic Diversity Metrics\\n\\n“To understand how population growth, dactne, and stability influence genetic diversity, we consider\\nther effects on tres kay mats:\\n\\n+x (Theta n): The average numberof pairwise cifferences between sequences.\\n+ 8W (Theta Watterson}: A measure based on the number af segregating sites.\\n‘+ Tojima's D: A statistical test that compares 8 and 6W to detect deviations from neutral\\n\\nevolution,\\n\\n“These simplified scenarios illustrate how population size changes impact genetic variation.\\n\\nScenario 1: Population Growth\\n\\nDescription:\\n‘When @ population expands rapidly, many rare alleles appear due to the racent increase in inevicuals.\\n\\n‘Assumptions (Hypothetical Values):\\n\\n+ on\\n\\n(Pairwise citferences are low since most sequences are very similar due tothe\\nexpansion)\\n\\n+ 8W-=4 (More segregating sites appear due to expansion)\\n+ Tajima’sb Calculation:\\n\\nO, = Ow 2-4\\n” Sandard deviation 1\\n\\nSince 6r < 8W, Tajma’s Dis negative.\\n\\n‘conclusion:\\nPopulation growth results in 6x < OW and Tajima’s D <0, indicating an excoss of rare variants\\n\\nScenario 2: Population Deciit\\n\\nDescription:\\n\\ne (Bottleneck)\\n\\n‘A population experiences a drastic reduction in size, leacing to the loss of rare alleles and an\\n‘overrpresentation of comman ones.\\n\\n‘Assumptions (Hypothetical Values):\\n+ on\\n\\n(Pairwise citferonces are higher because the remaining sequences are more divergent)\\n+ eW=4 (Fewer segregating sites due tothe bottleneck)\\n+ Tajima’sb Calculation:\\n\\n0, = Ow. on4\\n~ Wandard deviation ~ 1\\n\\nSince 6x > BW, Tajma’s Dis postive\\n\\nD =2\\n\\n‘conclusion:\\n\\nPopulation dectne results in @x > BW and Taima's D > 0, suggesting a loss of rae aloes.\\n\\nScenario 3: Constant Population Size\\n\\nDescription:\\n[A population remains stable ver time, with alle frequencies evolving neutral\\n\\n‘Assumptions (Hypothetical Values):\\n+ on\\n\\n(Pairwise citferances match the expected diversity level)\\n+ @W=5 (Segregating sites align with a stable population)\\n+ Tajima’sb Calculation:\\n\\n0, — Ow\\nRandard deviation 1\\n\\nSince 6x = BW, Tajima’s Dis zor,\\n\\nD\\n\\n‘conclusion:\\n\\n[A stable population results in 8x = 8W and Taima’s D = 0, indicating neutral evelution.\\n\\nSummary\\n\\n‘Changes in population size affect genetic variation in distinct ways:\\n+ Population Growth > More rave alleles > Negative Tajima’sD.\\n+ Population Decline > Fewer rare alleles > Positive Tajima's .\\n\\n+ Stable Population > Balanced allele frequencies > Tajima’s D= 0.\\n\\n‘These tends help researchers infer historical der raphic changes in populations from genetic data,\\n\",\n",
|
||
" 'COURSEWORK 4 6\\nDL: check the Moodle athe\\n\\n7 >\\n\\nTowards complete and error-free genome assemblies of\\n\\nall vertebrate species\\n1.Pick one of main themes . t Pp\\n\\nMORE VIDEOS\\n[BREE TA\\n\\nTECH\\nPm i) 35:57/37:42 © @ & Voulube ++\\n\\n',\n",
|
||
" 'Fore\\n\\n(600)\\n\\n(30)\\n\\n(3800)\\n\\n(300)\\n\\n(doo)\\n\\ntena\\n\\nisa)\\n\\n(-800)\\n\\n‘samples — metylome 16 suvS TE — metiybme merged WTALTE — atylome matt TE ‘samples — metifome 16 su TE — matylome merges WT ALTE — metifome mett_TE\\n\\nsamples — metyiome. 16 sar TE — thyme. merged WT_AILTE — matyome, matt TE\\n',\n",
|
||
" 'chemistry of radicals — peroxidase / hemoproteins\\n\\nHO\\n\\nmyoglobin ty Heme group (protoporphyrine IX)\\nae\\nig Fe3* (peroxidase) Fe*+O, — Fe3+ +0,\"\\n& le) fry 2+ ; : \" n\\\\\\nNRA Fe?* (hemoglobin, myoglobin) 0 AKA)\\n/ horseradish 2AH + H,0, SPP 2H, +2A° >>)\\nperoxidase\\nmw 17 kdal H,0, H, O AH Ae + H, O\\nhemoglobin\\nnative “native Compl. Comp.ll._~—SSsésattves Comp. II native\\nHam(Fe3+) Ham®(Fe=0)4* Ham(Fe=0)4*+ Ham(Fe3*)\\n\\nmw 68 kdal\\n\\nXS mw 42 kdal\\n\\ny\\n\\n',\n",
|
||
" 'Ranges from 0 to 1\\n',\n",
|
||
" '156,000 KB 155,000 KB 154,000 KB 153,000 KB 152,000 KB 151,000 KE\\n\\n157,000 KB\\n\\n148 MB\\n\\n149 MB\\n\\n150 MB.\\n\\n152 MB\\n\\n153 MB\\n\\n154 MB\\n\\n155 MB\\n\\n156 MB\\n\\n157 MB\\n\\n158 MB\\n\\n159 MB.\\n\\n',\n",
|
||
" \"Epidermal layer\\n\\nCell Distance\\n\\nArea\\n\\nElongation\\nratios (max/min)\\n\\n0.75-1.0\\n\\n0.5-0.75\\n\\n0.25-0.5\\n\\n0-0.25\\n\\n| A\\n2V_COMPLETE\\n\\n|AREA\\n\\nVOLUME\\n\\nIMAXMIN\\n\\nIm\\n\\nl21V_COMPLETE\\nAREA\\n\\nOLUME\\n\\nIM\\n\\nIM\\n\\ni201\\n\\n[AREA\\nVOLUME\\n\\nIM\\n\\nIMAXMID\\n\\nfos | oc | io E\\n00.25 0.25-0.50 0500.75 _0.75-1.0\\n'33.15772607 38,27197343 23.64799146 10.92426767\\n31,35001999 39, 15457599 24.53088161 10.96503692\\n(0.706271566 0,995108725 1.192089571 1.181832515\\n(0.732093524 0.931383858 1266275197 1.096748819\\n\\n0-0.25 0.25-0.50 0500.75 _0.75-1.0\\n\\n19.24792115 23,49681932 10.94572609 8.722510114\\n18.18032267 23.80134919 11.80305564 8.888929006\\n(0.893448068 1,017750253 1.0180320991.131369444\\n0.902037793 0.954909091 0.966313675 1.244435577\\n\\n0-0.25 0.25-0.50 0500.75 _ 0.75-1.0\\n\\n16.28634765 12.20109527 10.94945835 2.736923127\\n16.16750129 12.75007749 10.70985075 2.372292061\\n(0.918495544 0,946698623 1.136496971.195292929\\n(0.920755369 0.961650794 1.1124675321.198502646\\n\",\n",
|
||
" 'Recalibrated methylation level\\n\\n0.263\\n\\n0.198\\n\\n0.132\\n\\n0.066\\n\\n(-900) [Start] [End] (1000)\\n\\nsamples —— methylome_16_suvr5_genes ——- methylome_merged_WT_All_genes —— methylome_met1_genes\\n\\n',\n",
|
||
" 'Warning: Expected at\\nWarning: Expected at\\nWarning: Expected at\\nWarning: Expected at\\nWarning: Expected at\\nWarning: Expected at\\nWarning: Expected at\\nle, in the same order\\nWarning: Expected at\\nle, in the same order\\nWarning: Expected at\\n\\neast 2 parts\\neast 2 parts\\neast 2 parts\\neast 2 parts\\neast 2 parts\\neast 2 parts\\n\\neast 2 parts\\nas listed\">\\neast 2 parts\\nas listed\">\\neast 2 parts\\n\\nele, in the same order as listed\">\\n\\nWarning: Expected at\\n\\neast 2 parts\\n\\nele, in the same order as listed\">\\n\\nAfter filtering, kept\\nWriting PLINK PED and\\n\\nUnrecognized values used for CHROM:\\n\\nUnrecognized values used for CHROM:\\nUnrecognized values used for CHROM:\\n\\nUnrecognized values used for CHROM:\\n\\nDone.\\n\\n6@ out of 60\\nMAP files\\n\\nin\\nin\\nin\\nin\\nin\\nin\\nin\\n\\nin\\n\\nin\\n\\nin\\n\\nFORMAT entry: ID=PL,Number=G, Type=Integer, Description:\\n\\nFORMAT entry: ID=RGQ,Num\\n\\nINFO\\nINFO\\nINFO\\nINFO\\nINFO\\n\\nINFO\\n\\nINFO\\n\\nINFO\\n\\nentry:\\nentry:\\nentry:\\nentry:\\nentry:\\n\\nentry:\\nentry:\\n\\nentry:\\n\\nIndividuals\\n\\nchromosome_1\\nPLINK: Only outputting biallelic loci.\\n\\nchromosome_2\\n\\nchromosome_3\\n\\nchromosome_4\\n\\nID=AC, Number=A, Type=Integer, Description\\nID=AC, Number=A, Type=Integer, Descriptio\\nID=AF , Number=A, Type=Float, Description=\\nID=AF , Number=A, Type=Float, Description\\n\\nID=MLEAC, Num\\nID=MLEAC, Num\\nID=MLEAF, Num\\n\\nID=MLEAF, Num\\n\\nReplacing w\\n\\nReplacing w\\nReplacing w\\n\\nReplacing w\\n\\nAfter filtering, kept 477227 out of a possible 477227 Sites\\n\\nRun Time = 11.0@ seconds\\n\\nber=1, Type=Integer, Descriptio\\n\\nAllele Frequency, for each ALT allele, in the\\nAllele Frequency, for each ALT allele, in the\\nber=A, Type=Integer, Description=\"Maximum likelihood expectation (MLE) for\\n\\nber=A, Type=Integer, Description=\"Maximum likelihood expectation (MLE) for\\n\\nith @.\\n\\nith @.\\n\\nith @.\\n\\nith @.\\n\\nAllele count in genotypes, for each ALT allele, in the\\nAllele count in genotypes, for each ALT allele, in the\\n\\nsame order\\nsame order\\nthe allele\\n\\nthe allele\\n\\n\"Normalized, Phred-scaled likelihoods for genotypes as defined in the VCF specification\">\\n=\"Unconditional reference genotype confidence, encoded as a phred quality -10*log1@ p(genotype call is wrong)\">\\n\\nsame order as listed\">\\nsame order as listed\">\\n\\nas listed\">\\nas listed\">\\ncounts (not\\n\\ncounts (not\\n\\nnecessarily the same as the AC), for each ALT alle\\n\\nnecessarily the same as the AC), for each ALT alle\\n\\nber=A, Type=Float,Description=\"Maximum likelihood expectation (MLE) for the allele frequency (not necessarily the same as the AF), for each ALT all\\n\\nber=A, Type=Float, Description=\"Maximum likelihood expectation (MLE) for the allele frequency (not necessarily the same as the AF), for each ALT all\\n',\n",
|
||
" '@ Vivaldi File Edit View Bookmarks Mail Tools Window Help @ Oo gwg@oer+¥rzto@oee ©) FS Q ®S SunNov10 18:15\\n\\n©. taltech moodle fi pannzer2 li Pannzer2 [ ekhidna2.biocenter.helsir li ekhidna2.biocenter.helsi li ekhidna2.biocenter.helsi =) aman_assignment - Goo\\n\\n(a) — > YD YO @NotSecure ekhidna2.biocenter.helsinki.fi/barcosel/tmp//S2Z0dIJVpH4/PANZ_1.html Nv @ |_ a ee e > © FF @ @\\n\\nY Speed Dial ¥Y Imported From... Y Imported From... Online Bewerbung QGIS API Docume... qgis - Trying to pe... New Script - Earth... Pastebin.com - #1... TargetP 2.0- DTU... https://www.mood... OnePlus 12R revie... Whois “Indian”in.. v A »\\n\\nPannzer2 Home\\n\\nQueries 1 to 1000\\n\\ngene Description Biological process Molecular function\\nname Estimated PPV, description Estimated PPV, GO-id, description Estimated PPV, GO-i d, deg\\n\\nGO:0006813 potassium ion transport\\n\\nQuery header\\n\\nmonoatomic cation tra\\n\\nIKAOHOFJ_00001 00 G0:0098655 monoatomic cation 0.62 GO:0008324 activity\\n\\nK(+)/H(+) antiporter NhaP2 0.57 Cell volume regulation protein A . transmembrane transport 0.60 GO:0050660 flavin adenine dinucled\\nSearch i i i .\\na 0.49 GO-0098662 Morsanic cation 0.56 GO:0015297 antiporter activity\\n\\ntransmembrane transport\\n\\nIKAOHOFJ_00002\\n\\nhypothetical protein 0.0 Uncharacterized protein\\nSearch\\nIKAOHOFJ_00003\\nAlanine racemase GO Uncharacterized protein\\nSearch\\n0:76 GO:0006522 alanine metabolic process\\nIKAOHOFJ_00004 O74 G0:0046437 D-amino acid biosynthetic 0:79 GO:0008784 alanine racemase activ|\\nAlanine racemase, catabolic DADX 0.54 Alanine racemase , process 0.67 GO:0030170 pyridoxal phosphate b\\nSearch i i : i imeri\\npearcn 0.36 G0:0009080 pyruvate family amino 0.36 GO:0042803 protein homodimerizat\\nacid catabolic process\\n. D-alanine catabolic ”\\nTKAOHOFI_00005 : . 09 —GO:0055130 process 0184 GO:0008718 D-amino-acid dehydro\\nD-amino acid dehydrogenase DADA 0.64 D-amino acid dehydrogenase : tae : so\\n. L-alanine oxidation to 0335 GO:0042803 protein homodimerizat\\nSearch 039 GO:0019480 : :\\npyruvate via D-alanine\\nIKAOHOFJ_00006\\nD-amino acid dehydrogenase 035 D-amino acid dehydrogenase small subunit 0.56 GO:0016491 oxidoreductase activit\\nSearch\\nIKAOHOFJ_00007\\nhypothetical protein YCGB 0192 Sporulation protein SpoVR 0.63 GO:0006974 DNA damage response\\n\\nSearch\\n\\nregulation of fatty acid\\nmetabolic process\\n\\nfatty acid metabolic &\\n\\noO Q C) as a 100% = 18:15\\n\\nGO:0019217\\n\\n',\n",
|
||
" 'pbmc <- FindVariableFeatures(pbmc, selection.method = \"vst\", nfeatures = 2000)\\n\\n# Identify the 1@ most highly variable genes\\ntop1@ <- head(VariableFeatures(pbmc), 10)\\n\\n# plot variable features with and without labels\\n\\nplot1 <- VariableFeatureP lot (pbmc)\\n\\nplot2 <- LabelPoints(plot = plot1, points = top1@, repel = TRUE)\\nplot1 + plot2\\n\\nPPBP\\n.\\n\\nS100A9\\n\\nigus\\\\b?\\n\\noO\\n\\nPF4 GNLY\"\\n\\nFTH1e\\n\\noa\\n\\n¢ Non-variable count: 11714\\n¢ Variable count: 2000\\n\\nGNG11S100A8 = _-- Non-variable count: 11714\\n\\n¢ Variable count: 2000\\n\\nStandardized Variance\\nStandardized Variance\\n\\nle-02 1e+001e+02 le-02 1e+001e+02\\nAverage Expression Average Expression\\n',\n",
|
||
" '40\\n\\n30\\n\\n20\\n\\nValue\\n\\n10\\n\\nMetrics for 2V_COMPLETE\\n\\n16\\n\\n0-0.25\\n\\n71\\n\\n65\\nMetric\\n\\n(0) area\\n\\n{7 MAxmin\\n\\n.92\\n\\n19 18\\n\\n0.25-0.50 0.50-0.75 0.75-1.0\\n\\nInterval\\n',\n",
|
||
" 'In [54]: import fanc\\nimport fanc.peaks\\nimport fanc.plotting as fancplot\\n\\nimport logging\\nlogging. basicConfig(level=logging. INFO, format=\"%(asctime)s %(levelname)s %(message)s\")\\n\\nhic_data = fanc. load(\\'/mnt/storage3/aman/wdbasejuicer_new/aligned/inter_3@.hic\\')\\nloop_caller = fanc.RaoPeakCaller()\\n\\n/home/aman/. lLocal/lib/python3.10/site-packages/fanc/compatibility/juicer.py:330: UserWarning: No resolution chosen\\nfor Juicer Hic - using 2500000bp. Specify a custom resolution using <.hic file>@<resolution>\\nwarnings.warn(\"No resolution chosen for Juicer Hic - using {}bp. \"\\n/home/aman/. lLocal/lib/python3.10/site-packages/fanc/compatibility/juicer.py:353: UserWarning: Support for Juicer .h\\nic v9 is still in beta. Please report any issues to https://github.com/vaquerizas lab/fanc/issues/92\\nwarnings.warn(f\"Support for Juicer .hic v{self.version} is still in beta. \"\\n',\n",
|
||
" 'Parallel Computing Hi-C Fragment\\n\\n——\\n\\n—_—_—\\n—\\n—4 Sequencing ——\\n\\nSingleton\\nLow MAPQ\\n\\nDumped Pairs\\n\\n',\n",
|
||
" '14-4441 2100 2-24 24IV0 2 3-1 3-ll 3-lll 3-IV 3-V 3-Vl\\n',\n",
|
||
" 'es EXTENSIONS: MARKETPLAI\\n\\n@id:ms-python.python\\n\\nPython\\n\\ngo al Python language support with extensio...\\n\\n@ Microsoft\\n\\nreAny\\n\\n>=) a power out\\n\\nS Enter the URL of the running Jupyter Server\\nEnter the url of the running Jupyter Server ‘ython &\\nPress \\'Enter\\' to confirm your input or \\'Escape\\' to cancel touts - ‘O Python 3.9.6\\n> b& B- fi\\n 267ms >\\nconda activate cooler_aman\\n(0) (3] ® 0.0s Python\\nValueError Traceback (most recent call last)\\n\\n<ipython-input-3-83b@c48be5@b> in <module>\\n----> 1 get_ipython().run_line_magic(\\'conda\\', ‘activate cooler_aman\\')\\n\\n~/. local/lib/python3.6/site-packages/IPython/core/interactiveshell.py in run_lin\\n\\n2324 kwargs[\\'local_ns\\'] = sys._getframe(stack_depth) .f_locals\\n2325 with self.builtin_trap:\\n-> 2326 result = fn(x*args, *kkwargs)\\n2327 return result\\n2328\\n\\n~/.local/Lib/python3.6/site-packages/decorator.py in fun(xargs, *kkw)\\n\\n230 if not kwsyntax:\\n231 args, kw = fix(args, kw, sig)\\n--> 232 return caller(func, *(extras + args), *kkw)\\n233 fun.__name__ = func.__name.\\n234 fun.__doc__ = func.__doc\\n\\n~/. local/lib/python3.6/site-packages/IPython/core/magic.py in <lambda>(f, *a, ++\\n\\n185 # but it\\'s overkill for just that one bit of state.\\n186 def magic_deco(arg):\\n--> 187 call = lambda f, *a, **k: f(xa, *xk)\\n188\\n189 if callable(arg):\\n---> 79 raise ValueError(\"The python kernel does not appear to be a\\n80 \"Please use \\\\‘%Spip install’. instead.\")\\n81\\n\\nValueError: The python kernel does not appear to be a conda environment. Please\\n\\nmn re rT \"y\\n',\n",
|
||
" '> BMC Genomics. 2021 Jan 6;22:23. doi: 10.1186/s12864-020-07324-0 %\\n\\nChromatin loop anchors contain core structural components of the gene\\nexpression machinery in maize\\n\\nGina Zastrow-Hayes *, Gregory D May +\\n',\n",
|
||
" 'Comparison PS-SSD-DH\\n\\nPedal oe\\n\\na\\n\\nohn\\n\\nLSD (5%)=0.40\\noa\\n\\n--DH -> SSD «PS\\n® Seri 82» Attila\\n\\n7 ——\\n\\n—* DH =. 88D = Fa |\\n| ® Kauz ™ Weaver\\nInagaki et al. TAG 1998 *o + @ @ 4 5 67 8\\n\\nLine number by order of grain yield\\n\\nGrain yield (t/ha),\\nLSD (5%)=0.52\\n\\nProf. Chris-Carolin Schén (TUM) | Plant Breeding\\n\\n',\n",
|
||
" '',\n",
|
||
" \"16\\n\\n6 samtools view -b -F 4 file.bam > mapped.bam\\n\\nDoes it really get all mapped reads because using the above gives me less reads than:\\n\\nsamtools view -b -F 4 -f 8 file.bam > onlyThisEndMapped.bam\\n\\nsamtools view -b -F 8 -f 4 file.bam > onlyThatEndMapped.bam\\n\\nsamtools view -b -F12 file.bam > bothEndsMapped.bam\\n\\nsamtools merge merged.bam onlyThisEndMapped.bam onlyThatEndMapped.bam bothEndsMapped.k\\n\\nADD REPLY * link updated 5.0 years ago by Ram ¥ 44k - written 9.7 years ago by Sheikki ¥ 11k\\n\\n1@ From my understanding your first command extracts all mapped reads, but does not extract mates that were\\n6 unmapped. The command in your second set:\\n\\nsamtools view -b -F 8 -f 4 file.bam > onlyThatEndMapped.bam\\n\\nExtracts UNMAPPED reads who's mate is mapped. Thus, you will have more reads with the second set of\\ncommands because you are including unmapped mates of mapped reads.\\n\\nADD REPLY = link updated 6.2 years ago by Ram ¥ 44k + written 9.4 years ago by alolex » 960\\n\",\n",
|
||
" ', isolate, treatment, time, repeat,Library Name,LibraryLayout,LibrarySelection, LibrarySource, Orga\\n6000,Franklin,under control,@h,biological repeat 1,F—CK-1,PAIRED, PCR, TRA\\n\\nity, Illumina\\nity, Illumina\\nity, Illumina\\nity, Illumina\\nity, Illumina\\nity, Illumina\\nity, Illumina\\nity, Illumina\\n\\nity, Illumina\\nity, Illumina\\nity, Illumina\\nity, Illumina\\nity, Illumina\\nity, Illumina\\nity, Illumina\\nity, Illumina\\nity, Illumina\\n\\nNovaSeq\\nNovaSeq\\nNovaSeq\\nNovaSeq\\nNovaSeq\\nNovaSeq\\nNovaSeq\\nNovaSeq\\n\\nNovaSeq\\nNovaSeq\\nNovaSeq\\nNovaSeq\\nNovaSeq\\nNovaSeq\\nNovaSeq\\nNovaSeq\\nNovaSeq\\n\\n6000,TX9425, under\\n6000,TX9425, under\\n6000,TX9425, under\\n6000,TX9425, under\\n6000,TX9425, under\\n6000,TX9425, under\\n6000,TX9425, under\\n\\n6000, Franklin, under\\n6000, Franklin, under\\n6000, Franklin, under\\n6000, Franklin, under\\n6000, Franklin, under\\n6000, Franklin, under\\n6000, Franklin, under\\n\\nwaterlogging\\nwaterlogging\\nwaterlogging\\nwaterlogging\\nwaterlogging\\nwaterlogging\\n\\nstress,72h,biological\\nstress,72h,biological\\nstress,72h,biological\\nstress,24h, biological\\nstress,24h, biological\\nstress,24h, biological\\n\\nrepeat\\nrepeat\\nrepeat\\nrepeat\\nrepeat\\nrepeat\\n\\n3,T-72-3, PAI\\n2,T-72-2, PAI\\n1,T-72-1,PAI\\n3,T-24-3, PAI\\n2,T1-24-2, PAI\\n1,T-24-1, PAI\\n\\ncontrol,0h,biological repeat 3,T-CK-3, PAIRED, PCR, TRANS\\nCity, Illumina NovaSeq 6000,Franklin,under waterlogging stress,72h,biological repeat 3,F-72-3,\\n\\nwater logging\\nwater logging\\nwater logging\\nwater logging\\nwater logging\\n\\nstress,72h,biological\\nstress,72h,biological\\nstress,24h,biological\\nstress,24h,biological\\nstress,24h,biological\\ncontrol,@h,biological repeat 3,F-CK-3,PAIRED,PCR, TRA\\ncontrol,@h,biological repeat 2,F-CK-2,PAIRED,PCR, TRA\\n\\nrepeat 2,F-72-2,F\\nrepeat 1,F-72-1,F\\nrepeat 3,F-24-3,\\nrepeat 2,F-24-2,\\nrepeat 1,F-24-1,F\\n\\n6000,TX9425,under control,@h,biological repeat 2,T-CK-2, PAIRED, PCR, TRANS\\n6000,TX9425,under control,@h,biological repeat 1,T-CK-1, PAIRED, PCR, TRANS\\n',\n",
|
||
" \"@ = Safari File Edit View History Bookmarks Develop Window Help @ vw 6 8 $x 6© €& @ F Q ® Fri21.Nov 14:17\\n\\né ~*~\\neco < Co Wes pax-db.org @ © A + O\\n&% Yes. The paper states t... geckopy/geckopy/expe... geckopy — geckopy O.... i PaxDb - Help i= https://pax-db.org/dow... it PaxDb - WhatsNew meringlab/paxdb-issue... FragPipe workflows | Fr...\\n\\nINOVV UALA \\\\OVITIVAGIOU LU VU .U)\\n\\ne 392 species (from 170)\\ne 1'639 datasets (from 971)\\n\\n© 1'429 primary datasets\\n\\n© 210 species/tissue integrated datasets\\n1'376'031 unique proteins (from 743'963)\\n9'343'011 unique peptides (from 5'440'259)\\n\\nYou can contribute to PaxDl\\n\\ne Suggestions on database improvement can be submitted to issues repo meringlab/paxdb-issues.\\ne From Compute page (see below), user-uploaded peptide abundance data can be included in PaxDb.\\ne Requests to re-process projects catalogued on ProteomeXchange can be submitted at Request page.\\n\\nLarge-scale mass spectrometry raw data processing\\n\\nIn v6.0, we have introduced an end-to-end workflow from raw data files in proteomics repositories (cataloged at ProteomExchange) to absolute abundance in ppm. The projects are selected via a continuously\\ndeveloping LLM-based classifier which filters projects suitable for PaxDb scope. These projects were automatically downloaded and processed with a final step of manual evaluation to be included in the PaxDb.\\n\\nThe sample sdrf files for the processed ProteomeXchange projects are generated and made available for download.\\n\\nDatabase update\\nUpdate schedule\\n\\nThe semi-automated system has enabled the systematic screening, re-processing to be efficiently run and the data source to be regularly updated. The minor version updates incorporate new datasets twice a year,\\nwhile the major updates follow a two-year update corresponding to STRING's major version updates. The current corresponding STRING version is v12.0.\\n\\nChanges across major updates\\n\\nPaxDb species is a subset of what is available in STRING to leverage the protein interaction information for quality control as well as streamline the species genome, protein information integration. Between major\\nupdates, the NCBI taxonomy ID may change for the same PaxDb species - usually to denote a different strain and use its genome for protein annotation. That is, when searching for species name, the page will most\\nlikely remain, while the url changes /speeies/etdia /species/new_id.\\n\\nIn principle, as genomes become more complete/accurate with updates, the dataset quality improves if the original data is based on peptide quantity. The species ID mapping table is available for download. All previous\\nversions were frozen and available for download for comparison.\\n\\nDataset removal cases\\n\\nSeveral datasets included in the earlier versions of PaxDb were manually mapped by protein ID. These datasets reply on mapping of the protein name/IDs in the new genome version, which result in ID loss and\\nreduction in coverage. With the new workflow, we can re-process the same experiments with full control of the genome searched against, recovering the coverage.For these datasets, the old versions are removed. The\\nspecies mapping can be found at unmapped_datasets_v5_v6.txt and datasets affected are recorded at species_map_v5_v6.yml.\\n\\nAnother possibility is the species cannot be mapped, then the associated datasets will not appear in the new version. They are recorded in another archive which can re-surface when the species is available again.\\n\\nns\\n\\n\",\n",
|
||
" '@FastQC Report\\n\\nSummary\\n\\nQeasic Statistics\\nOre base sequence quality\\n\\nOber sequence quality scores\\n\\nOber base sequence content\\nQeer sequence GC content\\nOeer base N content\\n\\nQ sequence Length Distribution\\nQseauence Duplication Levels\\nQoverrepresented sequences\\nQadapter Content\\n\\nQrxmmer Content\\n\\nQbasic Statistics\\n\\na\\n\\nFilename\\n\\nFile type\\n\\nEncoding\\n\\nTotal Sequences\\n\\nSequences flagged as poor quality\\nSequence length\\n\\n%GC\\n\\nwood_sample_3_forward_paired. fq.gz\\nConventional base calls\\n\\nSanger / Illumina 1.9\\n\\n185642\\n\\n)\\n\\n30-150\\n\\n36\\n\\n@per base sequence quality\\n\\nQuality scores across all bases (Sanger / Illumina 1.9 encoding)\\n\\n40\\n\\n16\\n\\n14\\n12\\n10\\n\\noN B&O\\n\\n12345 67 8 9 1519\\n\\n30-34 45-49 60-64 75-79 90-94 105-109 120-124 135-139 150\\n',\n",
|
||
" 'zcat FitHiC.spline_pass1.res20000.significances.txt.gz | cut -f1,3 | sort | uniq\\n\\nSs\\n\\nOANODUBWNPPR\\n\\nB73V4_ctg1\\nB73V4_ctg10\\nB73V4_ctg100\\nB73V4_ctg101\\nB73V4_ctg102\\nB73V4_ctg103\\nB73V4_ctg104\\nB73V4_ctg105\\n\\nPRPRPRPPRPRPRPRPRPRPRPPRPRRPR\\n',\n",
|
||
" \"In [52]: from allel.stats.decomposition import GenotypePCA\\n\\n# Initialize PCA model (use ‘standard’ scaler since Patterson might not work well for haploids)\\nmodel = GenotypePCA(n_components=10, scaler=None)\\n\\n# Fit and transform haplotype data\\nmodel. fit (x)\\ncoords = model.transform(X)\\n\\nIn [54]: import matplotlib.pyplot as plt\\n\\n# Compute variance explained\\n#explained_variance = model.eigenvalues_ / model.eigenvalues_.sum()\\n\\n# Scatter plot of first two PCs\\n\\nplt.scatter(coords[:, @], coords[:, 1], alpha=0.7)\\n#plt.xlabel(f'PC1 ({explained_variance [0 ]*100: .2f}%)')\\n#plt.ylabel(f'PC2 ({explained_variance[1]*100:.2f}%)')\\nplt.title('PCA of Haploid Data’)\\n\\nplt.show()\\n\\nPCA of Haploid Data\\n\\n100\\n\\n15\\n\\n50\\n\\n25\\n\\n-50\\n\\n-75] o@&\\n\\n100-75 9-50-25 0 25 50 15\\n\",\n",
|
||
" \"(mustache_aman) [papantonis1@gwdu101 aman]$ awk '$1 == $4 {print $1}' GEO2457_5kb_mustache_loops.bedpe | sort | unig -c && wc -1 GE02457_5kb_mustache_loops.bedpe\\n88@ chri\\n457 chr1e\\n536 chri1\\n542 chri2\\n297 chr13\\n306 chri4\\n278 chri5\\n173 chr16\\n253 chr17\\n244 chri8\\n\\n92 chri9\\n942 chr2\\n216 chr2e\\n\\n88 chr21\\n\\n65 chr22\\n804 chr3\\n686 chr4\\n663 chrd\\n731 chré\\n552 chr7\\n574 chr8&\\n402 chr9\\n205 chrx\\n\\n9987 GEO2457_5kb_mustache_loops.bedpe\\n\\n(mustache_aman) [papantonis1@gwdu1@1 aman]$ awk '$1 == $4 {print $1}' GE02459_5kb_mustache_loops.bedpe | sort | unig -c && wc -1 GE02459_5kb_mustache_loops.bedpe\\n673 chri\\n341 chr1e\\n394 chri1\\n433 chri2\\n233 chr13\\n254 chri4\\n207 chri5\\n108 chr16\\n147 chr1i7\\n234 chri8\\n\\n29 chri9\\n626 chr2\\n173 chr2e\\n\\n83 chr21\\n\\n30 chr22\\n60@ chr3\\n534 chr4\\n484 chrd5\\n536 chré\\n425 chr7\\n481 chr8\\n286 chr9\\n158 chrx\\n\\n7478 GEO2459_5kb_mustache_loops.bedpe\\n\",\n",
|
||
" 'UMAP_2\\n\\nMS4A1\\n\\n-10-5 0 5 10\\nUMAP_1\\n\\nMS4A1_CD79A\\n15\\n\\nCD79A\\n\\n-10-5 0 5 10\\nUMAP_1\\n\\n10.0\\n\\n7.5\\n\\n5.0\\n\\n2.5\\n\\nColor threshok\\n\\n2 4 6 8 10\\nMS4A1\\n',\n",
|
||
" 'Epigenetic basis of complex Spontaneous epimutations Epigenetic clocks Machine learning of 3D\\n\\ntraits chromatin contacts\\n\\nGenomic and epigenomic basis\\n\\nof high-alpine adaptation\\n',\n",
|
||
" \"® Other High-Impact Internship Combos to Consider (besides ML + Proteomics)\\n\\nCombo\\n\\nML + Spatial Transcriptomics\\n\\nSingle-Cell Multi-Omics + Deep\\n\\nLearning\\n\\nMachine Learning + Functional\\n\\nGenomics (CRISPR screens)\\n\\nML + Imaging / Bioimage Analysis\\n\\nWhy It's Worth Considering\\n\\nMatches your interest in 3D genome and spatial\\nregulation. Cutting-edge field.\\n\\nExplodes in 2025. Everyone's moving toward this.\\n\\nGood for publication chances.\\n\\nGets you into industry very easily (e.g., biotech,\\n\\npharma).\\n\\nIf you like confocal/morphogenesis from Schneitz lab.\\n\\nAlso complements Micro-C beautifully.\\n\\nGood Labs\\n\\nGerlich (IMBA), Ale\\nBuecker (LMU)\\n\\nLinnarsson Lab (KI\\n\\n(Zurich), Aviv Rege\\n\\nETH Zurich, Broad:\\nMDC Berlin\\n\\nEMBL, CZI-linked |\\nDresden\\n\",\n",
|
||
" 'Db DEGs <- list. files(pattern = (\"/mnt/volume/data/group8/funcourse/workf lows/scripts/deseq2_results.csv\"))\\nprint (DEGs)\\n\\n[fas] ¥ 0.0s\\n\\ncharacter(@)\\n',\n",
|
||
" '706883\\n706884\\n706886\\n706885\\n706887\\n706888\\n706890\\n706891\\n706892\\n706889\\n706875\\n706873\\n706876\\n706874\\n1\\n\\n1321\\n\\nroot\\nroot\\nroot\\nroot\\nroot\\nroot\\nroot\\nroot\\nroot\\nroot\\nroot\\nroot\\nroot\\nroot\\nroot\\n\\nmessagebu\\n\\n20\\n20\\n20\\n20\\n20\\n20\\n20\\n20\\n20\\n20\\n20\\n20\\n20\\n20\\n20\\n20\\n\\n2866M\\n2866M\\n2866M\\n2866M\\n2866M\\n2866M\\n2866M\\n2866M\\n2866M\\n2866M\\n23440\\n23448\\n19992\\n19992\\n\\n164M\\n\\n9456\\n\\n2391M\\n2393M\\n2391M\\n2393M\\n2393M\\n2391M\\n2391M\\n2393M\\n2391M\\n2393M\\n7344\\n7344\\n2252\\n2136\\n11788\\n3364\\n\\n2412\\n2324\\n2412\\n2324\\n2324\\n2412\\n2412\\n2324\\n2412\\n2324\\n2372\\n2372\\n1720\\n1604\\n6216\\n1908\\n\\nNANNNNHNDDDDDDDDANN\\n\\n400.\\n400.\\n10@.\\n10@.\\n10@.\\n10@.\\n10@.\\n10@.\\n10@.\\n\\nDOAAD\\n\\nPLCTCTDPSORPRRPRRPRREBRBR\\nPOSCTCT®VVDDVD0D0090 0\\n\\nVPVTVTAAAD\\n\\n45h22:\\n45h22:\\n11h20:\\n11h20:\\n11h20:\\n11h20:\\n11h20:\\n11h20:\\n11h20:\\n11h20:\\nQ4.\\n13.\\n40.\\n44.\\n:1@.\\n18.\\n\\n51:\\n50:\\n39:\\n39:\\n\\n10:\\n\\n46\\n32\\n32\\n32\\n32\\n32\\n31\\n31\\n32\\n81\\n12\\n59\\n96\\n44\\n96\\n\\n/usr/\\n/usr/\\n/usr/\\n/usr/\\n/usr/\\n/usr/\\n/usr/\\n/usr/\\n/usr/\\n/usr/\\n\\noca\\noca\\noca\\noca\\noca\\noca\\noca\\noca\\noca\\noca\\n\\n/anaconda/envs/HiC-Pro_v3.\\n/anaconda/envs/HiC-Pro_v3.\\n/anaconda/envs/HiC-Pro_v3.\\n/anaconda/envs/HiC-Pro_v3.\\n/anaconda/envs/HiC-Pro_v3.\\n/anaconda/envs/HiC-Pro_v3.\\n/anaconda/envs/HiC-Pro_v3.\\n/anaconda/envs/HiC-Pro_v3.\\n/anaconda/envs/HiC-Pro_v3.\\n/anaconda/envs/HiC-Pro_v3.\\nperl /usr/local/anaconda/envs/HiC-Pro_\\n\\nVPV®VVVVVVOVO\\n\\n-0/\\n-0/\\n-0/\\n-0/\\n-0/\\n-0/\\n-0/\\n-0/\\n-0/\\n-0/\\n\\nv3.\\n\\nperl /usr/local/anaconda/envs/HiC-Pro_v3.\\n/anaconda/envs/HiC-Pro_v3.0.0/\\n/anaconda/envs/HiC-Pro_v3.0.0/\\n/lib/systemd/systemd --system --deserialize 33\\n@dbus—daemon --system —-address=systemd:\\n\\n/usr/\\n/usr/\\n\\noca\\noca\\n\\nbin/bowtie2-align-s --wrapper\\nbin/bowtie2-align-s --wrapper\\nbin/bowtie2-align-s --wrapper\\nbin/bowtie2-align-s --wrapper\\nbin/bowtie2-align-s --wrapper\\nbin/bowtie2-align-s --wrapper\\nbin/bowtie2-align-s --wrapper\\nbin/bowtie2-align-s --wrapper\\nbin/bowtie2-align-s --wrapper\\nbin/bowtie2-align-s --wrapper\\n\\nbasic-®\\nbasic-®\\nbasic-®\\nbasic-®\\nbasic-®\\nbasic-®\\nbasic-®\\nbasic-®\\nbasic-®\\nbasic-®\\n\\n--very-sensitive\\n—-very-sensitive\\n--very-sensitive\\n--very-sensitive\\n--very-sensitive\\n--very-sensitive\\n--very-sensitive\\n--very-sensitive\\n—-very-sensitive\\n—-very-sensitive\\n\\n30\\n30\\n30\\n30\\n30\\n30\\n30\\n30\\n30\\n30\\n\\n--score-min\\n--score-min\\n--score-min\\n--score-min\\n--score-min\\n--score-min\\n--score-min\\n--score-min\\n--score-min\\n—-score-min\\n\\nDAARBAAADH\\nNNNNNNNNN\\n\\nPere rere\\nSoooKoKOOOO\\n\\n--end-to-end\\n--end-to-end\\n--end-to-end\\n—-end-to-end\\n--end-to-end\\n--end-to-end\\n--end-to-end\\n--end-to-end\\n--end-to-end\\n--end-to-end\\n\\n—-reo\\n—-reo\\n—-reo\\n—-reo\\n—-reo\\n—-reo\\n—-reo\\n—-reo\\n—-reo\\n—-reo\\n\\n@.0/bin/bowtie2 --very-sensitive -L 3@ --score-min L,-@.6,-@.2 --end-to- end --reorder --un bowtie_resu\\n@.0/bin/bowtie2 --very-sensitive -L 3@ --score-min L,-@.6,-@.2 --end-to-end --reorder --un bowtie_resu\\n\\nbin/samtools view -F 4 -bS —\\nbin/samtools view -F 4 -bS —\\n\\n--nofork --nopidfile --systemd-activation --syslog-only\\n',\n",
|
||
" 'Index of /pub/plants/release-60/gff3/hordeum_vulgare\\n\\no Parent Directory\\n[7] CHECKSUMS\\n\\n@P README\\n\\nrh Hordeum _vulgare.MorexV3\\nrh Hordeum _vulgare.MorexV3\\nrh Hordeum _vulgare.MorexV3\\nrh Hordeum _vulgare.MorexV3\\nrh Hordeum _vulgare.MorexV3\\nrh Hordeum _vulgare.MorexV3\\nrh Hordeum _vulgare.MorexV3\\nrh Hordeum _vulgare.MorexV3\\nrh Hordeum _vulgare.MorexV3\\n\\nName Last modified Size Description\\n\\npseudomolecules\\npseudomolecules\\npseudomolecules\\npseudomolecules\\npseudomolecules\\npseudomolecules\\npseudomolecules\\npseudomolecules\\n\\npseudomolecules\\n\\n2024-10-01 20:00 2.5K\\n\\nassembly.60.chr,gff3.9z 2024-09-23 05:32 5.8M\\nassembly.60.gff3.2z 2024-09-23 05:32 5.8M\\nassembly.60.primary_assembly.1H.gff3.gz 2024-09-23 05:32 748K\\n\\nassembly.60.primary_assembly.2H.gff3.gz 2024-09-23 05:32 1.0M\\nassembly.60.primary_assembly.3H.gff3.gz 2024-09-23 05:32 932K\\nassembly.60.primary_assembly.4H.gff3.gz 2024-09-23 05:32 726K\\nassembly.60.primary_assembly.5H.gff3.gz 2024-09-23 05:32 927K\\nassembly.60.primary_assembly.6H.gff3.gz 2024-09-23 05:32 716K\\nassembly.60.primary_assembly.7H.gff3.gz 2024-09-23 05:32 886K\\n\\n2024-09-23 05:18 6.2K\\n',\n",
|
||
" \"About Library _ Statistics\\n\\nSequencing\\n\\nSequenced Reads: 547812856\\n\\nDuplication and Complexity (% Sequenced Reads)\\n\\nAnalysis of Unique Reads (% Sequenced Reads / % Unique Reads)\\n\\nIntra-fragment Reads: 34,307,613\\n\\nBelow MAPQ Threshold: 355,354,506 (64.87% / 73.27%)\\n\\nHi-C Contacts: 95,311,375 (17.40% / 19.65%)\\n3' Bias (Long Range): 97% - 3%\\n\\nPair Type % (L-I-O-R): 25% - 25% - 25% - 25%\\n\\nAnalysis of Hi-C Contacts (% Sequenced Reads / % Unique Reads)\\n\\nInter-chromosomal: 22,194,956 (4.05% / 4.58%)\\nIntra-chromosomal: 73,116,419 (13.35% / 15.08%)\\nLong Range (>20Kb): 35,425,178 (6.47% / 7.30%)\\n\",\n",
|
||
" 'HiC - Pro Juicer\\n\\nParailel Computing Hi-C Fragment\\nA Sequenced Alignment and Duplicate Map creation\\nHi-C Reads Chimera Handling Merge Sort removal\\na on\\n==\" a —— RI R2\\nSequencing © ———— SSS EES ESS\\nEy SSS SSS . > .\\n\\nae a ee : -.\\n\\n',\n",
|
||
" '> R.version$pLatform\\n\\n[1] \"x86_64-pc-lLinux-gnu\"\\n\\n> R.version$version. string\\n\\n[1] \"R version 4.4.2 (2024-10-31)\"\\n',\n",
|
||
" 'library(\"Rcolorsr ewer”)\\nsampleDistMatrix <- as.matrix(sampleDists)\\nrownames(samplebistwatrix) <- vsdScondition I\\ncolnames(sampledistmatrix) <- NULL\\ncolors <- colorramppalette( rev(brewer.pal(9, “Slues\")) )(255)\\npheatmap(sampleDistmatrix,\\n\\nCluster ing_distance_rows=sampleDists,\\n\\ncluster ing_distance_cols=sampleDists,\\n\\ncol=colors)\\n',\n",
|
||
" 'In [201]: combined_TCR_filtered <- combined_TCR[sapply(combined_TCR, function(df) {\\nany(df$barcode %in% colnames(combined_seurat) )\\n\\n+)]\\n\\nIn [202]: names(combined_TCR_filtered) <- \"patient3\"\\ncombined_TCR_filtered[[1]]$sample <- \"patient3\"\\n\\nIn [203]: combined_seurat <- combineExpression(\\ncombined_TCR_filtered,\\ncombined_seurat,\\ncloneCall = \"strict\",\\nproportion = FALSE\\n\\n)\\n\\nError in combineExpression(combined_TCR_filtered, combined_seurat, cloneCall = \"strict\", : Adjust the cloneSize par\\nameter - there are groupings < 1\\nTraceback:\\n\\n1. stop(\"Adjust the cloneSize parameter - there are groupings < 1\")\\n2. .handleSimpleError(function (cnd)\\n-{\\no watcher$capture_plot_and_output()\\ncnd <- sanitize_call(cnd)\\nwatcher$push(cnd)\\nswitch(on_error, continue = invokeRestart(\"eval_continue\"),\\n5 stop = invokeRestart(\"eval_stop\"), error = NULL)\\n. }, \"Adjust the cloneSize parameter - there are groupings < 1\",\\nbase: : quote(combineExpression(combined_TCR_filtered, combined_seurat,\\ncloneCall = \"strict\", proportion = FALSE) ))\\n\\nIn [204]: length(intersect(\\ncolnames(combined_seurat) ,\\ncombined_TCR_filtered[[1]]$barcode\\n\\n))\\n\\n274\\n',\n",
|
||
" 'Plant to plant communication as means to monitor the\\n(stress) environment and induce disease resistance\\n\\nMock (M)/SAR (S)\\n\\nSender Receiver\\n\\nZero air\\n\\ngenerator\\n\\nAmbient air in\\n\\nWenig et al. Nature Comm. 2019\\n\\nMFC 200\\nml/min\\n\\nfom\\n\\nPsttiter (log, cfu om~*)\\n\\na b a a\\n°\\nM Ss M Ss\\n\\nZ£\\n\\nPst\\n“i\\na\\n\\nPsttiter (log, cfu cnr)\\n\\n7\\n\\n3\\n\\nTu\\n\\na bb a bd\\nM s M s\\nCol-0 —_-ggpps12\\n\\nG(G)PPS12; Geranyl(gerany)) diphosphate synthase 12 involved in GPP and monoterpene biosynthesis 34\\n',\n",
|
||
" '@ | scRep.R*\\n\\na YA\\n\\n111840\\n111840\\n146164\\n166368\\n166368\\n205244\\n289640\\n289640\\n480269\\n480269\\n679531\\n679531\\n731374\\n755182\\n768329\\n\\nT_CD8_memory\\nT_CD8_memory\\nT_CD8_memory\\nT_CD8_memory\\nT_CD8_memory\\nT_CD8_memory\\nT_CD8_memory\\nT_CD8_memory\\nT_CD8_memory\\nT_CD8_memory\\n\\nWOONDAUBPWNPR\\n\\nPRPPRPRPRREB\\nNOURWNPS\\n\\ncell_id cell_type_experimental\\n\\nTRA\\nTRB\\nTRB\\nTRA\\nTRB\\nTRB\\nTRA\\nTRB\\nTRA\\nTRB\\n\\nTrue\\nTrue\\nTrue\\nTrue\\nTrue\\nFalse\\nTrue\\nTrue\\nTrue\\nTrue\\n\\n__) run071-nsclc-4_VDJ_Dominant_Co...\\n\\na\\n\\nShow whitespace\\n\\nhigh_quality_cell Locus sequence_id consensus_count umi_count sec\\n111840@_TRA_1\\n111840@_TRB_1\\n146164_TRB_1\\n166368_TRA_1\\n166368_TRB_1\\n205244_TRB_2\\n289640_TRA_1\\n289640_TRB_1\\n\\n480269_1\\n480269_1\\n\\nTRA_2\\nTRB_1\\n\\nTRA_1\\nTRB_1\\nTRA_1\\n\\n6529 5 GCAGTGCCAGGCTCATAGGGGCACATGAAGTGTCTACCTTCT¢\\n113@ 4 CCCTGTGTCTCCTGGGGGCAGATCATGCAGATACTGGAGTCTC\\n13953 6 CCTGCCTGATGCTTATAGGGGGAAGAGGTGGAGACGTTACAGA\\n2183 4 GGGTTTTCGTGATTATAGGGGAAAGCAGATTCTTTTTATGATT\\n495 4 CCCTGCCATGGGCACCGTGACCCTGATTGGGCAAAGCTCCCATCC\\n4500 2 GGTCCCTTGGGATTATAGGGGACTCTGCCATGGGCTGCAGGCT\\n2068 3 GGGGAGCATTTCCTGCCCTGAAGGAGAATTCTCACCAAGCACA\\n9639 9 CCTCTGTCGTGATTATAGGGACAGTGACACTGATCTGGTAAAC\\n3422 7 TTAAGAGGGCTGTTCTGGTATAGGCAAGATCCTGGGAAAGGC(\\n10682 9 GCCCAGTCGTGGTTAGAGGGGGAGATCCTGCCATGGGCTT CAG\\n\\nT_CD8_naive True TRA 679531_TRA_1 1485 2 ATAAAGTATCTCTTATAGGGGCTTTTTTCTAATTGGTAGGACAGA\\nT_CD8_naive True TRB 679531_TRB_1 20342 17 GGCCATCACGGATTATAGGGGTCCTCGCTGGTGAATGGAGGCA\\nT_CD8_memory False TRA 731374_1\\nT_CD8_memory True TRB 755182_1\\nT_CD8_memory True TRA 768329_T\\n1002101 T_CD8_naive True TRA 1002101_TRA_1 1434 1 TGGGCAGTATGGCCATTTTCACAATATTGATTCTTCCTATCCATC\\n1002101 T_CD8_naive True TRB 1002101_TRB_1 8774 5 CCTGCCATGGGCACCTAGGGGGCTTTTGCTCACAGTGACCCTGAT\\n\\n4770 2 TGCTCCTAGTGATTATAGGCGGCCTTTCTGTTTTGGAAACTTT\\n6937 6 TTCTCTGTCTGATTATAGGGGAGAGGCCCCATCTCAGACCCGA\\n5083 7 TTCTGGCAGTGATTATAGGGCTCATTCTGAGTTCAAAGCAACT\\n\\nFalse TRA 1136417 TRA 1 1464 2? AGGCTCATGGGATTATAGGGGATGAAGTGTCTACCTTCTGCAC\\n\\n18\\n19 1136412 T CDR memorw\\n1:33\\nConsole’ Terminal Background Jobs\\n\\nR~ R4.4.2 - ~/\\n\\nText file =\\n\\n=O\\n\\nF.True.False. True. TRBV3.1.01.1.7099999999999997e. 125 . 86S283M192S4N. 87.369. TRBD2.@2.@.000267 .370S13M178S3\\nN.371.383.TRBJ2.1.01.5.252e.20.385S6N44M132S . 386.429. TRBC2. GACACAGCTGTTTCCCAGACT CCAAAATACCTGGTCACACAGATG\\nGGAAACGACAAGTCCATTAAATGTGAACAAAAT . DTAVSQTPKYLVTQMGNDKSIKCEQN . ATGTATTGGTATAAACAGGACTCTAAGAAATTTCTGAAGATAA\\nTGTTTAGC .MYWYKQDSKKFLKIMFS . ATTATAAATGAAACAGTTCCAAATCGCTTCTCACCTAAATCTCCAGACAAAGCTCACTTAAATCTTCACATCAATTC\\nCCTGGAGCTTGGTGACTCTGCTGTGTATTTCTGT . TINETVPNRFSPKSPDKAHLNLHINSLELGDSAVYFC . TTCGGGCCAGGGACACGGCTCACCGTGCTA.\\nFGPGTRLTVL . CTGGGCCATGATACT . LGHDT . TACAATAATAAGGAGCTC . YNNKEL . GCCAGCACCCTGGGACTAGCGGGATTCAATGAGCAGTTC ..ASTLG\\n\\nLAGFNEQE\"\\n\\n> #contig.output <- cC\"/home/rstudio/\")\\n\\n> contig_list <- lList(S1)\\n\\n> contig.list <- loadContigsCinput = S1,\\nformat = \"AIRR\")\\n\\n+\\n\\nError in “[.data.frame*Cdf[[iJ], , cC\"cell_id\", \"locus\", \"consensus_count\",\\n\\nundefined columns selected\\n\\nEnvironment\\n\\n@ a\\n\\nR~\\nData\\n\\nO contig_list\\n\\nTutorial\\n\\n¥\\n\\nConnections\\n\\n3 893 MiB ~\\n\\nHistory\\n\\n+? Import Dataset ~\\n\\n© Global Environment ~\\n\\nLarge list ( 3.1 MB)\\n\\ncontig. list List of 0\\nOs1 1098 obs. of 1 variable\\nValues\\ncontig.output \"/home/rstudio\"\\nFiles Plots Packages Help Viewer Presentation\\n@ New Folder © NewFile ~ ©] Upload © Delete 3)R\\n/ > home >» rstudio\\n4 Name\\nt..\\n@ data\\n| Dockerfile\\n®} lib_test.R\\na run071_RSEC_MolsPerCell_MEX\\nrun071_Sample_Tag_Calls.csv run071_RSEC_MolsP\\nrun071_VDJ_perCell.csv\\n__} run071-nsclc-4_VDJ_Dominant_Contigs_AIRR.tsv\\nrun071-nsclc-4_VDJ_perCell.csv\\n®] scRep.R\\n®] seurat_vig1.R\\n|_| test\\n|_| test.txt\\nA variable_features_plot_with_labels.pdf\\n®) verify_packages.R\\n\\n',\n",
|
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" 'Find in Page: | IKAOHOFJ_00007 © itera Match Case\\nPannzer2 Home\\ne\\nQueries 1 to 1000\\nCnesy hens gene Description Biological process Molecular functio1\\nty name Estimated PPV, description Estimated PPV, GO-id, description Estimated PPV, GO-i d, de\\n0170 GO:0006813 potassium ion transport monoatomic cation tr:\\nIKAOHOFJ_00001 060 GO-0098655 Ponoatomicleation 0162 GO:0008324 activity\\none antiporter NhaP2 057 Cell volume regulation protein A emigrant transport 0160 GO:0050660 _ flavin adenine dinucle\\npearen . inorganic cation . . i\\n049 GO:0098662 transmembrane transport 056 GO:0015297 antiporter activity\\nIKAOHOFJ_00002\\nhypothetical protein 0.0 Uncharacterized protein\\nSearch\\nIKAOHOFJ_00003\\nAlanine racemase GO Uncharacterized protein\\nSearch\\n0176 GO:0006522 alanine metabolic process\\nIKAOHOFJ_00004 0.74 G0:0046437 D-amino acid biosynthetic 0.79 GO:0008784 alanine racemase acti\\\\\\nAlanine racemase, catabolic DADX 0.54 Alanine racemase , process 0.67 GO:0030170 pyridoxal phosphate b\\nSearch 036 GO:0009080 pyruvate family amino 0336 GO:0042803 protein homodimeriza\\n, acid catabolic process\\nD-alanine catabolic\\nIKAOHOFJ_00005 075 GO:0055130 , . .\\nD-amino acid dehydrogenase DADA D-amino acid dehydrogenase Process _ 0.84 G0:0008718 D cout acid yes\\nGandh 039 G0:0019480 L-alanine oxidation to 0135 GO:0042803 protein homodimeriza\\npyruvate via D-alanine\\nIKAOHOFJ_00006\\nD-amino acid dehydrogenase D-amino acid dehydrogenase small subunit 056 GO:0016491 oxidoreductase activit\\nSearch\\nhypothetical protein YCGB 0192 Sporulation protein SpoVR 0163 GO:0006974 DNA damage response\\n\\nSearch\\n\\n',\n",
|
||
" 'Genome-wide expected (log)\\n\\n10000 - |\\n\\n1000 - |\\n\\n100 +}\\n\\n105}\\n\\nExpected at BP_500000 norm SCALE\\n\\n3°¢64¢5 10 20 30 100\\nDistance between reads (log)\\n\\n200\\n',\n",
|
||
" '@ Vivaldi File Edit View Bookmarks Mail Tools Window Help Of sa - es © € ©) Ff Q ® SatMar 22 21:30\\n\\nee (ab) — > QO VY localhost HN @& |_ a @ OQ * ®@ @\\n\\nSpeed Dial Y Imported From... Y Imported From... Y Online Bewerbung QGIS API Docume... qgis - Trying to pe... New Script- Earth... Pastebin.com - #1... TargetP 2.0 .. https://www.mood... OnePlus 12R revie... Whois “Indian\"in.. A v\\n\\nIdentity\\n\\nIdentity\\n\\nB cell\\nB cell\\n6\\nT_cells T_cells\\nSs\\nNK_cell NK_cell e\\n0.0 0.5 1.0 1.5 2.0 2.5 0 1 2 3 4\\nExpression Level Expression Level\\nCDK2AP2 EFNB2 ea\\n= ry\\n5 5\\nx} xs}\\nm\\n©\\nB cell B cell\\nG\\nT_cells T_cells\\nNK_cell NK_cell\\n0 1 2 0.0 0.5 1.0 1.5 2.0\\n\\nExpression Level Expression Level\\n\\nS seurat_in3_vign... v Analysis, visualization, and Analysis, visualization, and *K Troubleshooting Single-Ce! [f# Pastebin.com - #1 paste to R_ RStudio Server 2) Plot Zoom > + @ Ww\\n\\nSe\\n\\nGS 0 @ 8B co) ee —O = 100% + 21:30\\n',\n",
|
||
" 'Examples\\n\\n>>> import allel\\n\\n>>> g = allel.GenotypeArray([[[0, 0], [@, 11],\\n[f@, 2], [1, 11],\\nO00 [{2, 2], [-1, -1]]])\\n>>> g.to_allele_counts()\\n<GenotypeAlleleCountsArray shape=(3, 2, 3) dtype=uint8>\\n2:0:0 1:1:0\\n\\n1:0:1 0:2:0\\nQ:0:2 0:0:0\\n>>> v=gl:, 0]\\n\\n>>> Vv\\n\\n<GenotypeVector shape=(3, 2) dtype=int64>\\n\\n@/0 0/2 2/2\\n\\n>>> v.to_allele_counts()\\n\\n<GenotypeAlleleCountsVector shape=(3, 3) dtype=uint8>\\n2:0:0 1:0:1 0:0:2\\n\\n>>>\\n',\n",
|
||
" 'sapere patos on\\n\\nsomraing soresiips wre ctr yay \\\\\\n\\nart 9\\n\\n‘part Satore ae ne\\n\\nPeeriguetageszests, 5)\\neetniean ark apni, matt, A, et bs pa)\\n\\nPileup Matix Heatmap\\n\\n: >\\na” 4\\n: 3\\na\\n',\n",
|
||
" '70\\n60\\n50\\n40\\n30\\n\\n20\\n\\nThe heatmaps like this give a good idea about clustering. Similar samples should cluster close\\nto each other. But in this case, one 1G is not clustering properly, which could be due to\\nbiological variability or even some technical issues.\\n',\n",
|
||
" 'Fragment\\n\\n= ————\\n\\n=. —,\\n> sequencing *——— a\\n\\n',\n",
|
||
" \"In [2]: .libPaths()\\n\\n‘/opt/conda/envs/scanpy_v1.10.4_r/lib/R/library'\\n\",\n",
|
||
" '‘Mean Methylation Levels - CG Context ‘Mean Methylation Levels - CHG Context ‘Mean Methylation Levels - CHH Context\\n\\nMean Methylation Level\\n\\nMean Methylation Level\\n\\nMean Methylation Level\\n\\nFile\\n\\nFile\\n\\nFile\\n',\n",
|
||
" 'Chromatin Loops Connect Gene-distal ACRs with Genes\\n\\nThe locations of dACRs raised the question of how they might regulate target genes over\\nlarge intergenic distances. To determine if dACRs directly interacted with their target genes\\nthrough the formation of chromatin loops, we first performed Hi-C2° on young maize leaves.\\nWe focused on the characterization of chromatin loops involving dACRs and genes\\n(Supplementary Data Tables $3 and S4). Due to technical constraints, we did not search for\\nchromatin loops less than 20 kb in length. Therefore, this was not an exhaustive\\ncharacterization of all d(ACR-gene loops. However, 39.2% of dACRs—a sufficiently\\nrepresentative sample of the dACR population—were greater than 20 kb from their nearest\\ngenes (Fig. 1d). Although dACRs comprised less than ~0.2% of the intergenic space, more\\nthan half (614/1,177) of the identified intergenic-gene loops contained at least one dACR at\\ntheir intergenic edges (Fig. 3b). Analysis of the Hi-C reads from self-ligated contact pairs\\ndemonstrated that the loop enrichment at dACRs was not an artifact arising from chromatin\\naccessibility or mapping biases (Supplementary Fig. 6a and b). Therefore, (ACR-gene loops\\nspanning = 20 kb were a common feature in the maize genome. These loops included\\ninteractions between the target genes tb/, ZmRap2.7, and BX/ and their genetically-mapped\\ncontrolling regions that have been hypothesized to contain long-range CREs (Fig. 3a,\\nSupplementary Fig. 6c-e).\\n',\n",
|
||
" 'corrected Hi-C counts\\n\\n10!\\n\\n10°\\n\\n107?\\n\\n10°\\ngenomic distance\\n\\n—— data_mcool.h5\\n\\n> Decay curve\\n\\n> First converted into .h5\\nformat\\n\\n> HiCExplorer—-\\n\\nhicPlotDistVsCounts()\\n> Data quality and\\n\\ncomparison\\n',\n",
|
||
" 'Go to a\\n\\nSign in -\\n\\namanshamil@protonmail. com\\n\\nvCJp65KgDZQcczKnkk$t\\n\\nOpen Dashboard\\n\\nBw My Sites\\n\\noo\\n88\\n\\nMAN. N\\n\\nNALAKATH\\n\\nmain_page aman_personal\\n\\nGo to main page and edit or ?start fresh\\nLook at my page(aman_personal) for aman.nalakath.org . Try making one to your taste\\n\\nWhen main page is ready save/publish to nalakath.org\\n\\npersonal page publishing - #TODO\\n',\n",
|
||
" 'S Plant Epigenome\\nBrowser\\n\\ny Local tracks\\n\\n©) BigWig methylome 16 suvr5 CG\\n\\nBigWig methylome 16 suvr5 CHG\\n\\n©) BigWig methylome 16 suvr5 CHH\\n(Bigwig methylome merged WT All CG\\nBigWig methylome merged WT All CHG\\n© BigWig methylome merged WT All CHH\\n© BigWig methylome met1 CG\\n\\nBigWig methylome mett CHG\\n\\n© BigWig methylome met1 CHH\\n\\n+ Reference sequence\\n\\n© Reference sequence\\n\\n¥ Annotation\\n\\nGenes\\nTransposons\\n\\ny pENCODE\\nStrain: Col-O\\n ATAC-seq\\n\\n© Leaf ATAC (rep 1)\\nCO Leaf ATAC (rep 2)\\nO Leaf ATAC Input\\n\\n> MethylC-seq\\n\\nLeaf Methylation\\n\\n+ ChIP-seq\\n\\nO Leaf H2A.Z\\n\\n© Leaf H3K4me1\\n© Leaf H3K4me3\\nCO) Leaf H3K27me3\\n©) Leaf H3K36me3\\nOC Leaf H3k56ac\\nO Leaf 3\\n\\n© Leaf ChIP Input\\nH3K9me2\\n\\n mRNA-seq\\n\\n© Leaf mRNA (rep 1)\\n© Leaf mRNA (rep 2)\\n\\n15\\n\\nArabidopsis thaliana (TAIR10) + File View Help co Share\\n\\nSlee ee Urata et\\n\\na 5,000,000 10,000,000 15,000,000 20,000,000\\n70 4\\n\\nGenes (feature density)\\n\\nTransposons (feature density)\\n\\nLeaf Methylation\\n\\nH3K9me2\\n\\nAR La Lu a Lait alu n dt al\\n\\n0.6\\n\\nBigWig methylome 16 suvr5 CHG 54\\n\\nBigWig methylome merged WT All CHG\\n\\nos\\nBigWig methylome met1 CHG\\n\\n',\n",
|
||
" 'Figure 3 Inference of population size from whole- —— YRI (Nigeria) —— CHB (China)\\n\\n. : . — MKK (Kenya) — JPT (Japan)\\ngenome sequences. (a) Population size estimates —— LWK (Kenya) — GIH (N. India\\nindivi — CEU (N.Europe) —— MXL (Mexico — CEU (N. Europe)\\n\\nfrom four haplotypes (two phased individuals) a — fsiaayy pe) — we re Rletive American) b ~ TSI (aly)\\nfrom each of nine populations. The dashed line — CHB (China)\\nwas generated from a reduced data set of only the © g 10° — JPT (Japan)\\nNative American components of the MXL genomes. 3 ae — GIH (N. India)\\n\\n. < 5 10 — YRI (Nigeria)\\nEstimates from two haplotypes for CEU and YRI g FS — LWK (Kenya)\\nare shown for comparison as dotted lines. 2 2 108\\nN, Northern. (b) Population size estimates from a a\\neight haplotypes (four phased individuals) from the g 2 108\\nsame populations as in a but excluding MXL and 3 E 10!\\n\\nWw\\n\\nMKK. In contrast to estimates with four haplotypes,\\nestimates are more recent. For comparison, we\\nshow the result from four haplotypes for CEU,\\n\\n10° 104 10°\\nCHB and YRI as dotted lines. Time (years ago) Time (years ago)\\n\\n',\n",
|
||
" 'Genome Tracks ¥ Sample Info v Session v Share Bookmark Save Image Circular View v Help v\\n\\nIGV asm.cont...me.fasta tig00000001:3,254,475-3,258,061 Q 3,587 bp ( Select Tracks }(_ Crosshairs }( Center Line )( Track Labels } (—) auu==® +)\\n\\nC I )\\n\\n3,255 kb 3,256 kb 3,257 kb 3,258 kb\\nL 1 1 1 1\\n\\nec cc\\n\\nC—O a x\\nssb\\n\\nsoxR soxS pdeC KBOCNLJJ_03121\\n',\n",
|
||
" 'Patch A\\nepidermis\\n\\nPatch B\\nepidermis\\n\\ndistance along PD axis (um)\\n\\nfor patch B of epidermis\\n\\nKKK\\n\\n50 ns |\\n\\n40\\n\\n30 ele\\n\\n2-Ill 2-IV 2-V\\n',\n",
|
||
" \"= An official website of the United States government Here's how you know V\\n\\nNational Library of Medicine\\n\\nNational Center for Biotechnology Information\\n\\nBLAST ® » blastn suite » results for RID-PVJCWU35016\\n\\nHome Recent Results\\n\\nSaved Strategies Help\\n\\n< Edit Search Save Search Search Summary ¥\\n\\nJob Title 2411:91.t1\\n\\nRID PVJCWU35016 Search expires on 12-28 04:24am Download All ¥\\nResults for 1:lel|Query_5316247 2411:g1.t1(947bp) v\\nProgram BLASTIN@ Citation v\\n\\nDatabase core_nt See details v\\n\\nQuery ID Icl|Query_5316247\\n\\nDescription 2411:g1.t1\\nMolecule type dna\\nQuery Length 947\\n\\nOther reports Distance tree of results MSA viewer @\\n\\nDescriptions Graphic Summary Alignments Taxonomy\\n\\nSequences producing significant alignments\\n\\nselect all 100 sequences selected\\n\\nDescription\\nv\\n\\nDiospyros sutchuensis chloroplast, complete genome\\n\\nDiospyros cathayensis chloroplast, complete genome\\n\\nDiospyros rhombifolia chloroplast, complete genome\\n\\nHeritiera elata chloroplast, complete genome\\n\\nDiospyros eriantha chloroplast, complete genome\\n\\nDiospyros mespiliformis voucher BR<BEL>:Meerts & Hasson 509 chloroplast, complete ge...\\n\\n@ How to read this report?\\n\\nFilter Results\\n\\n@ BLAST Help Videos\\n\\n‘Back to Traditional Results Page\\n\\nOrganism only top 20 will appear C] exclude\\nType common name, binomial, taxid or group name\\n+ Add organism\\nPercent Identity E value Query Coverage\\nto to to\\nDownload v Select columns v Show | 100Y | @\\nGenBank Graphics Distance tree of results © MSA Viewer\\nMax Total Query cE Per. Acc.\\nScientific N: Fi\\ncrenang Name Score Score Cover value dent — Len pccesslon\\nv v v v ’ v\\nDiospyros sutchuensis 1749 3499 100% 0.0 100.00% 157917 NC 067511.1\\nDiospyros cathayensis 1749 3499 100% 0.0 100.00% 157689 NC 039554.1\\nDiospyros rhombifolia 1749 3499 100% 0.0 100.00% 157368 NC 039556.1\\nHeritiera elata 1738 3477 100% 0.0 99.79% 157295 NC 043925.1\\nDiospyros eriantha 1738 3477 100% 0.0 99.79% 157432 NC 081462.1\\nDiospyros mespiliformis 1738 3477 100% 0.0 99.79% 157246 MZ274088.1\\n\\n38Sac0ggaqgqg\\n\\n— ob aanRoonn ..)\\n\\n“740\\n\\naDAIT\\n\\nannoys\\n\\nnn\\n\\nAn 7n0/\\n\\nACIECNO\\n\\nMIR noreraA a\\n\",\n",
|
||
" \"© multiqe\\n\\nv1.25.2\\n\\nGeneral Stats\\n\\nFastQC\\n\\nSequence Counts\\nSequence Quality Histograms\\n\\nPer Sequence Quality Scores\\n\\nPer Base Sequence Content\\n\\nPer Sequence GC Content\\n\\nPer Base N Content\\n\\nSequence Length Distribution\\nSequence Duplication Levels\\nOverrepresented sequences by sample\\nTop overrepresented sequences\\nAdapter Content\\n\\nStatus Checks\\n\\nSoftware Versions\\n\\n€~ multiqe\\n\\nA modular tool to aggregate results from bioinformatics analyses across many samples into a single report.\\n\\nReport generated on 2024-12-18, 19:35 CET based on data in: /data/proj2/home/students/pst14/illegal_logging_trees/fastqc_raw/trimmomatic/fastqc_results\\n\\n© Welcome Not sure where to start? «09\\n\\nGeneral Statistics\\n\\nSW Copy table HE Configure columns\\n\\nSample Name\\nwooa_sample_z Torwara_unpairea\\n\\nwood_sample_2_reverse_paired\\nwood_sample_2_reverse_unpaired\\nwood_sample_3 forward_paired\\nwood_sample_3_forward_unpaired\\nwood_sample_3 reverse_paired\\nwood_sample_3_reverse_unpaired\\nwood_sample_4_forward_paired\\nwood_sample_4_forward_unpaired\\nwood_sample_4_reverse_paired\\nwood_sample_4_reverse_unpaired\\nwood_sample_5_forward_paired\\nwood_sample_5_forward_unpaired\\nwood_sample_5_reverse_paired\\n\\nwood_sample_5_reverse_unpaired\\n\\nFastQC version: 0.11.4\\n\\nQuality control tool for high throughput sequencing data. URL: htto://www..bioinformatics. babraham.ac.uk/projects/fastqc\\n\\nSequence Counts\\n\\n+i: Scatter plot\\n\\n= Violin plot\\n\\n‘Showing °/29 rows and °/s columns.\\n\\nSequence counts for each sample. Duplicate read counts are an estimate only.\\n\\nPercentages\\n\\nFastQC: Sequence Counts\\n\\nGc\\n\\n38. %\\n38.0%\\n37.0%\\n36.0%\\n36.0%\\n36.0%\\n36.0%\\n37.0%\\n37.0%\\n37.0%\\n37.0%\\n37.0%\\n37.0%\\n38.0%\\n\\n37.0%\\n\\ndon't show again x\\n\\nExport as CSV\\n\\nuM\\n\\n9 EF & A ® ieotvox\\n\\n01M\\n\\n01M\\n\\n01M\\n\\n01M\\n\\n01M\\n\\n01M\\n\\n01M\\n\\n@Help\\n\\nExport Plot\\n\\nrm samp A fore ie LLL LLL LLL MM Unique Reads\\n\\nI Duplicate Reads\\n\\nwood_sample_1_reverse_unpaired\\n\\n‘wood_sample_2_forward_paired\\n\\nwood_sample_2_reverse_paired\\n\\n\",\n",
|
||
" '@ Vivaldi File Edit View Bookmarks Tools Window’ Help BSBeweerok@eoeetkww@es es Mon Sep 9 22:29\\n\\nLECTURE TASK - WHO ARE YOU? Bi)\\n\\nGetting to know you: your names, where you are from, your major or specialisation area, questions you\\nmight have, etc., and above all: your expectations and aims regarding this course.\\n\\nDeliver your answer, ~200 words’ in Moodle via task \"Students’ introductions and aims\"\\n\\nWhich topics are How does your\\n; ; What grade are you\\nWhat kind of especially schedule look like aiming at?\\nbackground do you interesting for you this autumn? ,\\n>\\nhave? Are all these SNE What kind of\\n\\ntopics new to you? bic are the best expectations do\\nDo you have study days of the ou have?\\nconcerns? week for you? Y\\n\\nWO] SAVIO fF you like, you are allowed to be creative and deliver your introduction e.g., in a 5 min vlog,\\n\\nvideo, or any such format. Just submit the link to the lecturer via Moodle.\\n\\nPp Md) 34:19/37:11 © @ «© Youtube 5\\n',\n",
|
||
" 'js tibrary,scnepertolre)\\n#S1 <— read.delim(\"/home/rstudio/run@71-nsclc—4_VDJ_Dominant_Contigs_AIRR.tsv\", sep = \"\\\\t\", header = TRUE, :\\n\\n#contig_list <- list(S1)\\n#contig.list <— loadContigs(input = S1,\\n# format = \"AIRR\")\\n\\n]: $2 <- read.delim(\"/home/rstudio/rund71-nsclc-4_VDJ_Dominant_Contigs_AIRR.tsv\", sep = \"\\\\t\", header = TRUE, st\\ncontig_list <- loadContigs(input = $2, format = \"AIRR\")\\n\\n#converting columns in TCR data to character (string).\\ncontig_list <- lapply(contig_list, function(df) {\\nfor (col in c(\"cdr3_nt\", “cdr3\", \"chain\", \"barcode\", \"v_gene\", \"j_gene\", \"d_gene\", \"c_gene\")) {\\nif (col %in% names(df)) df[[col]] <- as.character(df[[col]])\\n+\\n\\ndf\\n})\\n\\n]: combined. TCR_p4 <- combineTCR(\\ncontig_list,\\nsamples = c(\"P4_S1\"),\\nremoveNA = FALSE,\\nremoveMulti = FALSE,\\nfilterMulti = FALSE\\n)\\n\\n# output = a list of contig data frames that wilcox.testl be reduced to the reads associated with a single «\\nhead (combined. TCR[[1]])\\n\\nA data.frame: 6 x 11\\n\\nbarcode TCR1 cdr3_aa1 edr3_nt1 7\\n<chr> <chr> <chr> <chr> <\\n1 1002101 TRAV19\"01.TRAJ27*01.TRAC CAPTPMQANQP TGTGCCCCAACACCAATGCAGGCAAATCAACCTIT TRBV11-2 ana\\nTR\\n3 10279593 TRAV13-2\"01.TRAJ26°01.TRAC CAENTRGRRSEFCL =TGTGCAGAGAATACGAGGGGTAGGAGGTCAGAATTTTGTCTIT 2°01.TRBD1*01.TF\\n4°01.TF\\n\\n5 10300542 TRAV21*02.TRAJ34*01.TRAC CAAYNTDKLIF TGTGCTGCTTATAACACCGACAAGCTCATCTIT\\nTRE\\n6 10311423 TRAV2101.TRAJ15*01.TRAC CAVVNQAGTALIF TGTGCTGTAGTTAACCAGGCAGGAACTGCTCTGATCTIT 2°01.TRBD2*02.TF\\n2°01.71\\n8 10627379 TRAV29/DV5*01.TRAJ47°01.TRAC CAASRYGNKLVF TGTGCAGCAAGCAGATATGGAAACAAACTGGTCTIT TRBV27°01. TREO\\n\\nTRE\\n\\n',\n",
|
||
" '@ Vivaldi File Edit View Bookmarks Mail Tools Window Help O @ 6B +0 3 © OB o S SunNov 17 12:11\\n\\nS Workspaces v higlass/higlass: Fast, flex higlass/higlass-docker: B li Data Preparation — HiGla Jupyter! Bagatelle No. 25inA! HiGlass + ry Ww\\n\\nQ Search Google ’ a ¢ fo °¢ # © @ a\\n\\nhttps://www.mood... OnePlus 12R revie... Whois “Indian”in.. vA\\n\\nQ\\n&\\n7\\n&\\n‘ a\\n6\\n&\\n\\n@ = > DO. OU VY docalhost:5001/app Ae\\n\\nv\\n\\nY Speed Dial yY Imported From... Y Imported From... Online Bewerbung QGIS API Docume... qgis - Trying to pe... New Script - Earth... Pastebin.com - #1.. TargetP 2.0 - DTU...\\n\\nHiGlass About Blog Examples Plugins Docs ©\\n\\noot x\\notx\\n\\nva\\nf\\nL\\n“\\n<9 08009\\n\\n®\\n\\n* &\\n00 @ © @& SOO LR rset —O—$—$—$—$—= 100 % 11\\n',\n",
|
||
" '.\\n\\n@ Vivaldi File Edit View Bookmarks Mail Tools Window Help ®o @O6O+0 8 © B S SatFeb1 16:35\\n\\n5 tum_ngs v < @® papantonis2 - NotebookL! Program Description VBC Summer School Date DNA Fiber Fluorography O (GS) DNA fiber fluorography - ¢ Mediator recruits the cohe + ry Ww\\n\\na) €- > OQ VBA google.com W yv @ Search Google ’ | Restart Required [_) oe S&P ee *# G C &\\n\\nY Speed Dial yY Imported From... Y Imported From... Online Bewerbung QGIS API Docume... qgis - Trying to pe... New Script- Earth... Pastebin.com-#1.. TargetP 2.0- DTU... https://www.mood... OnePlus 12R revie... Whois “Indian”in.. v A »\\n\\nAN) papantonis2 « Share $3 Settings CO\\n\\n®O® © HD\\n\\nSources at Chat @© Refresh Studio oO\\nVWVISUIALY! ANU CULICOSIT HE Yous\\n72_ZhangEtAl_NatGenet2023 (1).pdf expression and chromatin Audio Overview fo)\\narchitecture 41 e\\n\\n#2 Source guide a o Mediator and cohesin in sister a2\\n\\niappmy quansy \\\\wrey 1eaus anu ren uupueaiwe were nmcicu vus. chromatid cohesion 44 papantonis2 if) on ch : ww\\n\\nNext, ICE-balanced .cool files and KR-balanced .hic files were *Micro-C and data analysis: This 6\\n\\ngenerated and visualized via HiGlass ver. 1.11.7, cooltools section describes the Micro-C > e -\\n\\n(https://cooltools.readthedocs.io/en/ procedure, including library 00:00 / 22:52 +\\n\\nlatest/notebooks/contacts_vs_distance.html) used to generate decay preparation, sequencing, and data Se\\n\\nplots via, and sub-compartment analysis was performed using CAL- processing using the Dovetail Interactive mode\\n\\nDER60 considered 50 kbp-resolution Micro-C data. For loop calling, Genomics pipeline 42 . It mentions\\n\\nwe used a multi-tool (HiCCUPS ver. 1.19.01, cooltools ver. 0.5.4, and mapping reads to the hg38 genome,\\n\\nmustache ver. 1.0.1) and a multi-resolution (5- and 10-kbp) filtering low-quality reads and PCR Notes 3 iB}\\n\\napproach33,34. Loop lists derived from each tool were merged using duplicates, generating .cool and .hic\\n\\npgltools ver. 1.2.1 (ref. 61) as follows: dots from both 10- and 5-kbp files, and loop calling using multiple -\\n\\nresolution are retained if they are supported by >10 read counts, and tools 42 . It also describes how loops + Add note\\n\\nkept at native resolution. To further annotate loops as CTCF- or were annotated based on CTCF,\\n\\ntranscription-anchored, using CTCF, H3K27ac CUT&Tag peaks (from H3K27ac, and RNAPII peaks 42 . & Study guide Ei Briefing doc @\\n\\nthis work), as well as RNAPII peaks and nascent RNA-seq signal «Chromatin fractionation and oo\\n\\n(RPKM > 10; from ref. 39). All intersections were performed using western blotting: This section details FAQ ~ Timeline\\n\\ng\\n\\npgltools intersect1D without any distance tolerance for CTCF the method used to assess protein . . . oo, w\\n\\nanchors, and with a 10-kbp tolerance for enhancers and promoter abundance in different subcellular =) koh hone a eevenay of cach none: wean Ie\\n\\nanchors (annotated TSS + 2 kbp) identitied using elizseskenven 3.16 fractions, including cell lysis, summaries of the subsections, as requested: Paper 1... ©\\n\\n(ref. 62). Note that promoters of all gene isoforms were considered, rantrifiinatian and nrntain datantian\\n\\nand ‘superenhancers’ were called using our H3K27ac CUT&Tag data\\n\\nand the BOSE; algorithm63. Finally, aggregate plots for loops and Start typing... 6 sources >)\\n\\nboundaries were generated using the coolpup.py tool64. All custom [+] ‘G)\\n\\ncode used is available at:\\n\\nhttps://github.com/shuzhangcourage/Micro-C-CUT-tag/ tree/v1.0.0. What methods were used to analyz >\\n\\nCleavage under targets and tagmentation (CUT&Tag) Following lifting\\n\\n. : : : - ; NotebookLM can be inaccurate, please double check its responses. Q\\n',\n",
|
||
" 'nature plants\\n\\nExplore content y Aboutthejournal y Publish with us v Subscribe\\n\\nnature > nature plants > articles > article\\n\\nArticle | Published: 18 November 2019\\n\\nWidespread long-range cis-regulatory elements in the\\nmaize genome\\n\\nWilliam A. Ricci, Zefu Lu, Lexiang Ji, Alexandre P. Marand, Christina L. Ethridge, Nathalie G. Murphy,\\n\\nJaclyn M. Noshay, Mary Galli, Maria Katherine Mejia-Guerra, Maria Colomé-Tatché, Frank Johannes, M.\\n\\nJordan Rowley, Victor G. Corces, Jixian Zhai, Michael J. Scanlon, Edward S. Buckler, Andrea Gallavotti,\\n\\nNathan M. Springer, Robert J. Schmitz 4 & Xiaoyu Zhang 4\\n\\nNature Plants 5, 1237-1249 (2019) | Cite this article\\n\\n18k Accesses | 224 Citations | 139 Altmetric | Metrics\\n\\n@ An Author Correction to this article was published on 06 February 2020\\n\\n@ This article has been updated\\n\\n',\n",
|
||
" 'Data types before conversion:\\n\\nchr aintea\\nx1 antea\\nx2 object\\nchr2 object\\nyl object\\ny2 object\\nname object\\nscore floated\\nstrand floated\\nstrand2 floated\\ncolor floated\\nobserved floated\\nexpectedBL floated\\nexpectedDonut —_floate4\\nexpected floated\\nexpectedv floated\\nfrat. intea\\nfdrDonut antea\\nfart intea\\nfarv antea\\n\\ndtype: object\\nAfter converting to numeric, missing values per column:\\nchr @\\n\\nx1 @\\n\\nx2 7736\\n\\nchr2 @\\n\\nyl 1736\\n\\ny2 736\\n\\nname @\\n\\nstrand1\\nstrand2\\ncolor\\nobserved\\nexpectedBL\\nexpectedDonut\\nexpected\\nexpectedv\\nfrat.\\nfdrDonut\\nfart\\n\\nfarv\\n\\ndtype: intea\\nUnique values in chr1: [ 96500000 147225000 137165000\\nUnique values in chr2: [\\'.\"]\\n\\nNunber of valid distances: @\\n\\n14540000 28425000 131400000)\\n\\nDistribution of Loop Distances from BEDPE File\\n\\n0.04\\n\\n0.02\\n\\n0.00\\n\\nFrequency\\n\\n0.02\\n\\n0.04\\n\\n00 02 oa 06 os\\nDistance (bp)\\n\\n',\n",
|
||
" '@ Zoom Workplace\\n\\nox\\n\\nWw\\n\\n4\\n\\na\\n\\nClipboard\\n\\n11\\n\\nSlide 10 0f 14 4\\n\\nMeeting View Edit\\n\\n[==] Layout ¥\\n\\n‘© Reset\\nNew\\n\\nSlide v Section\\n\\nSlides\\n\\nEnglish (India)\\n\\nCy, Accessibility: Investigate\\n\\nW4\\n\\nx\\n\\nPre\\n\\n& 00 Ce PD Find i) A\\nD | 5 b S yy\\nALLS o5|¥ 82 Replace v\\nD Swen Arrange Create PDF Create PDF and Add-ins\\n° soe 2 I$ Select v and Share link Share via Outlook\\nFont Paragraph Drawing Editing Adobe Acrobat Add-ins\\n\\nGrowth : Tissue expansion\\n\\nAcross stages (from 2-III to 2-V) , interval [0.75-1.00] along PD axis sees the highest tissue expansion\\n\\nU\\n\\nWhat sort of tissue expansion is it (isotropic or anisotropic) ?\\n\\nMAXMIN Histogram MAXMID Histogram\\n(0.75. 1.00} 00} oe\\n= 2 7\\n} (0.25, 0.30) 4\\n- Po om st 0.00, 0.25)\\na =\\n\\nAverage of MAXMID\\n\\n> No evidence of purely isotropic cell growth in any stage at any interval\\n\\n> Cell growth on average is anisotropic for all intervals at each stage\\n\\n“ oo\\n= Notes QB comments oo\\n\\noO\\n\\nWindow Help Bevwvue@8Wrt+ ort.t@goe wy Se Wed Feb 12 22:24\\n\\n',\n",
|
||
" \"Last login: Wed Sep 18 15:46:07 on ttys@0ee\\naman@Laptop-von-Aman ~ % ssh amnala@base.hpc.taltech.ee\\namnala@base.hpc.taltech.ee's password:\\n\\nLast login: Wed Sep 18 16:49:35 2024 from 193.40.250.119\\n\\nWelcome to base.hpc.taltech.ee.\\nIt has been freshly upgraded to Rocky 8!\\n\\nThis is HPC Centre's main batch cluster.\\nIf you run into any trouble, let us know in Teams 'HPC Support Chat' or write to us: hpcsupport@taltech.ee\\n\\nUser guides: https://hpc.pages.taltech.ee/user-guides\\n\\nNEW MODULES:\\n\\nmodule load rocky8/all\\n\\nmodule load rocky8-spack/master\\n\\nmodule load openmpi/4.1.1-gcc-10.3.0-r8\\n\\nURGENT ==\\n\\n. The module system has changed so your job submission scripts need to be changed\\n\\n-— amp*, green* and gray* modules have been replaced by rocky8* modules.\\n\\n-— most of the module names have changed, use module avail to see the available ones\\n\\n- Infiniband is not available currently, for MPI jobs use the openmpi/4.1.1-gcc-10.3.@-r8-tcp module\\n\\n2. We are missing some software currently, it will become available in the coming weeks\\n\\n3. The user-guide will be updated in the coming weeks and the example scripts and modules do not yet reflect the current module structure/naming\\n4. user-guides have been moved to https://docs.hpc.taltech.ee\\n\\nIf you run into any trouble, let us know in Teams 'HPC Support Chat' or e-mail us: hpcsupport@taltech.ee\\n\\n[amnala@base ~]$ ls\\n\\nfruitsalad.txt fruitsalad_cleaned.txt history_aman.txt\\n\\n[amnala@base ~]$ cat history_aman.txt\\n14 cd smbgroup/bioinf-students/\\n\\n15 s -ltr\\n16 clear\\n17 s -ltr\\n\\n18 echo $HOME\\n\\n19 cp fruitsalad.txt $HOME\\n\\n20 cd $HOME\\n\\n21 s\\n\\n22 cat fruitsalad.txt\\n\\n23 uniq fruitsalad.txt\\n\\n24 cat fruitsalad.txt | sort | uniq -u\\n25 s\\n26 cat fruitsalad.txt\\n\\n27 cat fruitsalad.txt | sort | uniq -u > fruitsalad_cleaned.txt\\n\\n28 s\\n29 cat fruitsalad_cleaned.txt\\n3@ we -h\\n\\n31 we --help\\n\\n32 we -l1 fruitsalad_cleaned.txt\\n\\n33 cat fruitsalad_cleaned.txt\\n\\n34 history | less\\n\\n35 history | tail\\n\\n36 history\\n\\n37 history | tail -n +14 > history_aman.txt\\n[amnala@base ~]$\\n\\n\",\n",
|
||
" 'In\\n\\n[189]:\\n\\ncombined_seurat <- combineExpression(\\ncombined_TCR,\\ncombined_seurat,\\n\\ncloneSize = c(Rare = 1le-4,Small = 0.001,Medium = 0.01,Large = 0.1,Hyperexpanded =\\n\\nproportion = FALSE\\n)\\n\\nError in combineExpression(combined_TCR, combined_seurat, cloneSize\\nter - there are groupings < 1\\n\\nTraceback:\\n1. stop(\"Adjust the cloneSize parameter - there are groupings < 1\")\\n2. .handleSimpleError(function (cnd)\\n\\nwatcher$capture_plot_and_output()\\ncnd <- sanitize_call(cnd)\\nwatcher$push(cnd)\\nswitch(on_error, continue = invokeRestart(\"eval_continue\"),\\n5 stop = invokeRestart(\"eval_stop\"), error = NULL)\\n. }, \"Adjust the cloneSize parameter - there are groupings < 1\",\\nbase: : quote(combineExpression(combined_TCR, combined_seurat,\\ncloneSize = c(Rare = 1e-04, Small = 0.001, Medium = 0.01,\\nLarge = 0.1, Hyperexpanded = 1), proportion = FALSE))\\n\\n)\\n\\nc(Rare\\n\\nle-04,\\n\\n1),\\n\\n: Adjust the cloneSize parame\\n',\n",
|
||
" 'PC2\\n\\n400\\n\\n300\\n\\n200\\n\\n100\\n\\n—100\\n\\n—200\\n\\n—300\\n\\n—400\\n\\nPCA of Genotype Data\\n\\nT T T T\\n—200 0 200 400 600\\nPcl\\n',\n",
|
||
" \"Hi Aman,\\n\\nOh that's a good match! She will move out on the 1st of May. It's a flat very close to UMG (where our office is) and\\nit's gonna be fully furnished.\\n\\nWhen would you be interested in moving in?\\n\",\n",
|
||
" 'There was a problem connecting\\nto the server “nas.ads.mwn.de\".\\n\\nThe server may not exist or it is\\nunavailable at this time. Check the\\nserver name or IP address, check your\\nnetwork connection, and then try again.\\n\\nOK\\n',\n",
|
||
" '-hic & .cooV.mcool:; Binary formats for Hi-C data\\n> Compressed contact matrices at multiple resolutions\\nGenomic intervals for binned data\\n\\n>\\n> Interaction frequencies between loci\\n> Supports multiple bin sizes & corrections in one file\\n\\n',\n",
|
||
" \"[20]: # Calculate allele frequencies\\nac = gt.to_allele_counts().sum(axis=1)\\nan = gt.to_n_alleles()\\naf = ac / an\\nprint(af)\\n\\n# Total alternate alleles per variant\\n# Total alleles (2 * n_samples)\\n# Alternate allele frequency\\n\\nn_alleles'\\n\\n\",\n",
|
||
" 'x No N\\n\\neo Q @ .\\nWw’ ao @° Expression\\n2.0\\nJADE2 15\\n1.0\\nDEK 08\\n0.0\\n-0.5\\nCDK2AP2\\nIdentity\\nEFNB2 © NK cell\\ne T_cells\\n\\ne B-cell\\n',\n",
|
||
" 'Outcrossing — Panmixia — Hardy-Weinberg-Law TM\\n\\nIn the absence of\\n\\n- selection\\n\\n- migration\\n\\n~ mutation\\n\\nwe have under panmixia\\n\\nno change in gene frequencies\\n\\nEE recone\\n\\n=P, =p, -...\\np=P+05H id mene\\n\\nequilibrium genotype\\nAA: Aa: aa=p?:2pq:q?\\n\\nafter one generation!\\n\\n',\n",
|
||
" 'Crosslinked —> Religated —» Sequencing —» Alignment to\\nchromatin fragments reference genome\\n\\n«« Ligation\\n} Restriction site tt Pair of mate reads\\n\\nFiltering & & Binning Paired read thes <_ Pairing\\n\\nfo Gt\\n\\nig ee pe\\n\\nWAY\\nWHY\\n\\nlo\\n3\\n=\\nlp\\n>\\n=\\n\\n',\n",
|
||
" \"4.524 HiC-Pro-master/doc/themes/paris/logos -> ../../_static/logos/\\n\\n4.575 Make sure internet connection works for your shell prompt under current user's privilege ...\\n\\n4.575 Starting HiC-Pro installation !\\n\\n4.976 Checking dependencies\\n\\n4.976 - Python libraries ...0K\\n\\n6.765 — R installation ...0K\\n\\n9.515 - Bowtie2 installation ...0K\\n\\n9.531 - Samtools installation ...0K\\n\\n9.590\\n\\n9.598 Checking HiC-Pro configuration\\n\\n9.758 - Configuration for TORQUE/PBS system ...0K\\n\\n9.758\\n\\n9.758 done !\\n\\n9.844 (g++ -Wall -02 -std=c++@x -o build_matrix /opt/HiC-Pro-master/scripts/src/build_matrix.cpp; mv build_matrix /opt/HiC-Pro-master/scripts)\\n16.47 (g++ -Wall -02 -std=c++@x -o cutsite_trimming /opt/HiC-Pro-master/scripts/src/cutsite_trimming.cpp; mv cutsite_trimming /opt/HiC-Pro-master/scripts)\\n19.24 realpath: /opt/hicpro/HiC-Pro_3.1.@: No such file or directory\\n\\n19.25 cp -Ri /opt/HiC-Pro-master /opt/hicpro/HiC-Pro_3.1.0\\n\\n19.26 cp: cannot create directory '/opt/hicpro/HiC-Pro_3.1.@': No such file or directory\\n\\n19.27 make: *** [Makefile:78: cp] Error 1\\n\\nDockerfile:42\\n\\n# Install HiC-Pro\\n\\n41 |\\n42 | >>> RUN cd /opt && \\\\\\n43 | >>> wget https://github.com/nservant/HiC-Pro/archive/master.zip -O hicpro_latest.zip && \\\\\\n44 | >>> unzip hicpro_latest.zip && \\\\\\n45 | >>> cd HiC-Pro-master && \\\\\\n46 | >>> bash scripts/install/install_dependencies.sh -c config-install.txt -p /opt/hicpro -o /opt/hicpro/HiC-Pro_3.1.0 -q && \\\\\\n47 | >>> make install && \\\\\\n48 | >>> 1n -s /opt/hicpro/bin/HiC-Pro /usr/local/bin/HiC-Pro && \\\\\\n49 | >>> rm -rf /opt/hicpro_latest.zip /opt/HiC-Pro-master\\n|\\n\\n5@\\n\\n\",\n",
|
||
" 'aman@Laptop-von-Aman docker_rstud % ls\\n\\nDockerfile\\n\\naman@Laptop-von-Aman docker_rstud % docker image ls\\n\\nREPOSITORY TAG IMAGE ID CREATED SIZE\\nrstud_aman_v@.2 atest #331421d46dd 23 minutes ago - 09GB\\ntoluene123/rstudio v@.2 £331421d46dd 23 minutes ago - 09GB\\nrstud_aman_v@.1 atest 006134F99654 5 hours ago . 27GB\\ntoluene123/rstudio v0.1 Q06134F99654 hours ago . 27GB\\nrstud_aman atest 73334d399a99 hours ago 27GB\\naman_jupyter.slim atest 34e8d349084b months ago .3GB\\ndocker-slim-empty—image atest 38a6aa7a96ad months ago 678B\\naman_jupyter atest elcbbb52f016 months ago 8.05GB\\ntoluene123/aman_jupyter atest elcbbb52f016 months ago 8.05GB\\nconda-—jupyter-—bioinformatics atest cf4883933257 months ago 8.05GB\\nghcr.io/apptainer/apptainer atest 1320d13fa26d months ago 515MB\\nhialass/hialass—docker atest L4086069ee7d vears aqgo 2.29GR\\n\\nPwWOwWwoRE\\n\\nWV WNNNNNAD\\n',\n",
|
||
" '@ HiGlass psa About Blog Examples Plugins Docs ©\\n\\nNow to add some data...\\n\\nShow a specific genomic\\n\\nlocation\\n= Upload a viewconf to the Adding data\\nserver\\n= Edit the view config online HiGlass supports a number of different data types.\\n< ViewcontStructure naan\\n= Views Use the ingest command to add new data. Generally data requires a filetype and a datatype.\\n= Tracks This can sometimes (i.e. in the case of cooler and bigwig files) be inferred from the file itself.\\n= Overlay Tracks\\nPlugin Track Development higlass-manage ingest /tmp/sample.mcool\\n\\n= Basic skeleton\\n\\n» Available tracks, libraries, In other, more ambiguous cases, it needs to be explicitly specified:\\n\\nand utils\\n\\n. higlass-manage ingest my_file.bed --filetype bedfile \\\\\\nPlugin Data Fetcher --datatype bedlike --assembly hg19\\nDevelopment\\n\\n= Basic skeleton Note that bedfiles don’t store chromosome sizes so they need to be passed in using either the\\n\\n= Available libraries and utils --assembly or --chromsizes-filename parameters.\\n',\n",
|
||
" 'File View Bookmarks Assembly Dev\\n\\nChromosomes Show Normalization (Obs | Ctrl) Resolution (BP) Color Range\\ny) 5\\nA A A EE\\nAll All Bp Observed None None 1o.”’o00 00D UD. a . has =\\n\\n25MB 500KB 100KB 25 KB 5KB\\n\\n',\n",
|
||
" 'Why is important to have an accurate demography?\\n\\n‘ol\\n\\nTY ey\\n\\n® o\\nposition along genome\\n\\n',\n",
|
||
" 'Dec. 2013 (GRCh38/hg38)\\n\\n= Genome sequence files and select annotations (2bit, GTF, GC-content, etc)\\nm= Standard genome sequence files and select annotations (2bit, GTF, GC-content, etc)\\n= Analysis set sequence files (See: What is the analysis set?)\\n\\n= Sequence data by chromosome\\n\\n@ Annotations\\n\\n= SNP-masked fasta files\\n\\n@ LiftOver files\\n\\n@ Pairwise alignments\\n\\n= Multiple alignments\\n\\n@ Patches\\n\\n= Data archive\\n',\n",
|
||
" '>FDR Threshold - 10% at all resolutions; Peak Width - 5kb: 4, 10kb: 2,\\n25kb:1; Window width - 5kb: 7, 10kb: 5, 25kb: 3\\n\\n>merging distance - 5kb & 10kb: 20kb, 25kb: 50kb\\n',\n",
|
||
" 'Comparison PS-SSD-DH\\n\\n1993\\n\\n1993\\n\\n1994\\n\\n1994\\n\\n1995\\n\\n1995\\n\\n1996\\n\\nSeason\\n(location)\\n\\nSummer\\n(El Batan)\\n\\nWinter/spring\\n(Cd, Obregon)\\n\\nSummer\\n(El Batan)\\n\\nWinter/spring\\n(Cd. Obregon)\\n\\nSummer\\n(El Batan)\\n\\nWinter/spring\\n(Cd, Obregon)\\n\\nWinter/spring\\n(Cd. Obregon)\\n\\nBreeding method\\n\\nDH\\n\\nWheat F, plants\\nmaize crossing\\n\\nDoubled haploid\\ngrains\\n\\nSeed\\nmultiplication\\n\\n65-97 lines\\nSelection\\n\\n10 lines\\nYield evaluation\\n\\n10 lines\\nYield evaluation\\n\\n110 Fy plants\\n110 F, plants\\n110 Fy plants\\n110 Fs lines\\n\\nSelection\\n\\n10 lines\\nYield evaluation\\n\\n10 lines\\nYield evaluation\\n\\n1500 F plants\\n\\nSelec!\\n\\nton\\n\\n150 F; families\\n\\nSelec\\n\\n‘tion\\n\\n60 Fy families\\n\\nSelec\\n\\n20F\\n\\nSelec\\n\\ntion\\n\\ns families\\ntion\\n\\n10 lines\\nYield evaluation\\n\\n10 lines\\nYield evaluation\\n\\nInagaki et al. TAG 1998\\n\\nProf. Chris-Carolin Schén (TUM) | Plant Breeding\\n\\n',\n",
|
||
" 'Dear Aman,\\n\\nHonestly, | had nothing to do with this data. | was not involved in generating, storing or analyzing it.\\n\\nThe data is stored on Ting’s cluster and thus only she can give or change permissions.\\n\\nIn this case | would suggest to contact her and ask her for this data, so she can give you the exact instructions and\\npermissions.\\n\\nGood luck and have a good day!\\n\\nBest regards,\\n\\nAdi\\n\\nAdi Mackay (Danieli)\\n\\nPhD student\\n\\nTranslational Epigenetics Lab\\n\\nDepartment of Pathology\\n\\nUniversity Medical Center Géttingen\\n\\nLinkedin: https://Awww.linkedin.com/in/adi-danieli-a4778912a\\n\\n',\n",
|
||
" 'icv ome v Tracks ¥ mple Info v Session v Share Bookmark Save Image Circular View v Help v\\n\\nIGV oxford_e...me.fasta tig00000002:1,752,510-1,825,110 Q 72 kb (Select Tracks ) (\"Crosshairs ) (Center Line ) (Track Labels) @ qumm@ +)\\n\\nC | D)\\n\\n1,760 kb 1,770 kb 1,780 kb 1,790 kb 1,800 kb 1,810 kb 1,820 kb\\nL 1 n L L 1 n\\n\\n11D 2 ee) ee es 2 ie\\n\\nIKAOHOFJ_01984yijE metFmetLrpmE cytR hsilU gipF gipX tpiA pfkAcpxR sodA_1 thaBrhaD IKAOHOFJ_02046fdhE_1 dtdcsqR yihTyihR yihQ yihP_1 ompL IKAOHOFJ_02079 glnA gl\\n\\npriA_2 menA_2 sbp_2 cpxA_1 rhaT_2rhaA_1 ysdC_2 fdoG_3 yinV yihP_2 GFM1\\n',\n",
|
||
" 'Large heatmap:\\n\\n| <> Submatrix\\n\\n',\n",
|
||
" '(1000)\\n\\n(Ena\\n\\n[stan]\\n\\n(-900)\\n\\n0.023\\n\\nBoor\\n\\non\\n\\n| uone| Ayo peyesqueooy\\n\\n(1000)\\n\\n(Ena\\n\\n[stan]\\n\\n(-900)\\n\\n0.042\\n\\nz 0.031\\n\\n| YORE KYOU PayesqiIedoy\\n\\n02\\n\\n0.263\\n\\nBot98\\n\\n492\\n\\n| uone| Ayo peyesqueooy\\n\\n(1000)\\n\\n[Eng]\\n\\n[star)\\n\\n(-900)\\n\\nsamples — mothylome_ 16 suviS_genes — methyiome.merged WT_All_ genes — methylome_matt_genes\\n\\nsamples — mathylome_16_suw5 genes — methylome merged WT_AlL genes — methylome_matt_gones\\n\\nsamples — mothylome_ 16 suviS_genes — methyiome.merged WT_All_ genes — methylome_matt_genes\\n',\n",
|
||
" \"Veeb” 61S” = MOODLE E-mail” Help.\\n\\nBioinformatics Il information 2024\\nGeneral introduction to Bioinformatics il- Ol\\nIntroduction to the course\\nCourse info (link to study information\\nTeacher's announcements\\nCourse participant's forum (ask questions f.\\nProject Work 1 - Genome project plan\\nBioinformatics group project example from\\n¥ Week 1\\n.ek 1 general discuss!\\nLecture 1/A- Introduction\\nVideo: Lecture 1 - Introduction\\nStudents introduction and aims (DL 12.09. 2.\\nHow to (seriously) read a scientific pape\\nAsticle 1\\nLecture 1B - Setting up HPC A\\nVideo: Lecture 18 - Setting up HPC a\\nfleet and greet\\nCoursework 1on Article 1: Aspects of geno.\\nPart of MIT ture: His put\\n¥ Week 2\\nek 2 general discussio\\nLecture 2 A - Introduction to Computing Clu\\nVideo: Lecture 2 A - Introduction to Comput.\\nShort introduction to HPC\\nTalTech HPC Centre\\nTalTech HPC User Guides\\nAsticle 1\\nCoursework 2 on Article 1: Sequencing data\\n‘Command Line Tutorial\\nLecture 2.8 - Command Line Tutorial\\nLecture Task (HPC exercise) instructions\\nLecture Task: HPC Exercise (OL 19.09 23:59)\\nWhat is High Performance Computing - HP.\\nThe Linux command line for beginners\\nRyans Tutorials Linux Tutorial\\n¥ Week 3\\n\\nVeek 3 Introduction\\n\\nColour Strips Puzzle instructio\\n\\nColor Strips Puzzle\\n\\nLecture Task: Colour Strip Pu:\\n\\nsults\\n\\nBioinformatics Il MOOC: View: User report\\n\\nCourse Participants Grades.\\n\\n[ User report ¥ ]\\n\\n© Aman Shamil Nalakath\\n\\nGrade tem\\n'¥_ Blolnformaties ll MOOC\\n\\nB sudents introduction and aims (OL 12.08, 2359)\\n\\nB coursework\\n\\nAuticle t: Aspects of genome i\\n\\n(01 12.09, 2359)\\n\\nBD coursework 2 on Article : Sequencing data (OL 1909 23:58)\\n\\nB Lecture Task: HPC Exercise (DL 1209 23:59)\\n\\nB Lecture Task: Colour Sip Puzzle results\\n\\nlogy (DL 26.09 23:58)\\n\\nBD Assignment 1- Data Quality Control (OL 0310. 23:59)\\n\\nBD project Work t- ntial description (DL 1010. 23:59)\\n\\nBA Assignment 2- Genome assembly and assembly quality (DL 3110 23:59)\\n\\nBD coursework 6 (OL 1411 23:59)\\n\\nBD Project Work 1 Ge 1 plan (Final DL 2111 23:59)\\n\\nBD Assignment 4 Some questions on RNASeq and Transcriptome measurement (OL 2111 23:58)\\n\\nB Assignment 5 - looking back at the course (OL 1912 23:59)\\n\\nBD project Work 2 submission (DL 1912 23:5\\n\\nB coursework 4: Article based analysis (OL 1010 235\\n\\nBD) week 12 assignment (OL\\n\\n112359)\\n\\n© couse wal\\n\\nCalculated weight\\n\\n0.00%\\n\\n0.00%\\n\\n0.00%\\n\\n0.00%\\n\\n0.00%\\n\\n0.00%\\n\\n10.00%\\n\\n0.00%\\n\\n0.00%\\n\\n0.00%\\n\\n10.00%\\n\\n10.00%\\n\\n0.00%\\n\\n25.00%\\n\\n10.00%\\n\\n10.00%\\n\\n25.00%\\n\\n0.00%\\n\\n0.00%\\n\\nGrade\\n\\nPass\\n\\nPass\\n\\nPass\\n\\nPass\\n\\n100.00\\n\\nPass\\n\\n10.00\\n\\n100.00\\n\\nPass\\n\\nPass\\n\\n10.00\\n\\n900\\n\\nPass\\n\\n10.00\\n\\n10.00\\n\\n23.00\\n\\nPass\\n\\nPass\\n\\n9700\\n\\nRange Feedback\\nFall-Pass\\nFall-Pass\\nFall-Pass\\nFall-Pass\\n0-100\\nFall-Pass\\n0-10\\n0-100\\nFall-Pass\\nFall-Pass\\n0-10\\n\\n0 | gave 2 out of 3 points for comparing the annotations in detall and discussing the possible causes for observed differences as there could have been a bit more of discussion,\\n\\nFall-Pass\\n\\nVery good reasons behind the project and thoroughly described,\\n\\n0-28\\n\\no-10\\n\\no-10\\n\\nThere were no comments on IGV visualization, some interpretations were missing from Differential expression analysis and Enrichment analysis part.\\n\\n0-28\\n\\nFall-Pass\\n\\nFall-Pass\\n\\n0-100\\n\\nContribution to course total\\n\\n0.00 %\\n\\n0.00 %\\n\\n0.00 %\\n\\n0.00 %\\n\\n0.00 %\\n\\n0.00 %\\n\\n10.00%\\n\\n0.00 %\\n\\n0.00 %\\n\\n0.00 %\\n\\n10.00%\\n\\n200%\\n\\n0.00 %\\n\\n25.00%\\n\\n10.00%\\n\\n10.00%\\n\\n23.00%\\n\\n0.00 %\\n\\n0.00 %\\n\\nTAL\\n\",\n",
|
||
" 'ean tytn rt\\n\\n‘Mon Metyaton Loves ~ CHG Context\\n\\nMean Methylation Levels - CHG Context\\n\\nMean Methylation Levels - CHG Context\\n\\n‘Mean Motion Level\\n\\nFe\\n\\n',\n",
|
||
" \"‘Sample Distance Matrix\\n\\ncd\\n\\nRS * foe\\n\\n'5RR21866476,\\n\\nssrno 1866474\\n\\nRR2 1866875,\\n\\nCo\\n\\nSRR21866475,\\n\\nsro 1866474\\n\\n‘sep21a60476\\n\\nsR21866483\\n\\nsRR2 1866482\\n\\nsR21866473\\n\\nR21866472\\n\\nsaRz1866471\\n\\nsrR21a66485\\n\\nsRR21866470\\n\\nsRRZ1B60487\\n\\nspre 1266486\\n\\nsRRz1266484\\n\\nsmR21866481\\n\\nsRR21866477\\n\\n[ setascans\\n\\nSRR21866478\\n\\nsRRZTBEO48O\\n\\n\",\n",
|
||
" '@ = Safari File Edit View History Bookmarks Develop Window Help @ ne) - s © €& @) F Q ®f Fri21.Nov 14:08\\n\\n4 ~*~\\neeo om&-< ‘on © (1| @ 2g pax-db.org Gad oO +\\n&% Yes. The paper states that the prot... iG} geckopy/geckopy/experimental/exp... FE] geckopy — geckopy 0.0.1 documen... S ENSP00000370010 protein info at PaxDb - Help i= https://pax-db.org/downloads/6.0/...\\n\\naes % F o 5 @ 8 ree\\n—ec . me = Oo\\n£ . go 7 Qe\\ng g wo\\na oO ro\\n— &\\n2 4 & 8 Bo\\nBZ -i% =— = =o\\nfo} 2 Ss\\nra} °\\nf= = Ps\\n2 D>\\n= ° ‘=\\nfo} - nm SO\\ncS -\\n0.01 1 100 10000 10 100 1000 10000 10 100 1000 10000\\nhuman brain (ppm) eukaryotic average (ppm) multi-cellular average (ppm)\\n\\nProtein family abundance conservation. A: between human and mouse brain, B: between eukaryotes and bacteria. C: between multi-cellular and single-cellular organisms.\\n\\nQ: Can | export an abundance table for a few proteins of interest?\\n\\nA: Within a target species, multiple proteins (separated by comma) can be searched. and you will land in the page to disambiguate these proteins,\\nafter this step, you can view and download the abundance info in a TSV (tab separated value) file.\\n\\nQ: What does protein abundance (ppm) in PaxDb mean?\\n\\nA: “ppm” is short for parts per million. Protein abundance datasets in PaxDb are re-scaled to ppm. Abundance in \"ppm\" describes each protein\\nrelative to all other proteins in a sample (i.e. proportionality to counts of complete, individual protein molecules, not to molecular weights, protein\\nvolumes, or digested peptides). For instance, protein X has 300 copies per cell and counts of all proteins is 3 O00 000 molecules, then the protein\\nX\\'s abundance is expressed as 100 ppm.\\n\\nQ: Where do the data come from? Can | use it freely?\\n\\nA: All PaxDb data come from open-access proteomics data from a published experiment / research project, either directly or after re-processing.\\nAll data is under Creative Commons Attribution 4.0 International (CC BY 4.0).\\n\\nQ: What is the scope of PaxDb?\\n\\nA: Quantification of the whole proteome—at the organism or tissue level—under healthy, normal conditions.\\n\\ne ls sub-cellular protein abundands available? No\\ne Is post-translational modification data available? No\\ne ls disease sample available? No\\n\\nSy Q: What is iBAQ processing?\\n',\n",
|
||
" 'OMB 100 MB 200 MB 300 MB\\n\\n100 MB\\n\\n200 MB\\n\\n300 MB\\n\\n',\n",
|
||
" '@ Safari File Edit View History Bookmarks Develop Window Help @ ne) - s © €& @&) =F Se Fri21.Nov 14:08\\n\\n\"a =~\\ny a } rr\\n@ece OH < f SW ov Click to open in Finder Find 9 C, oO +\\n/Users/aman/Pictures/ Ankefar\\n&% Yes. The paper states that the prot... iG} geckopy/geckopy/experimental/exp... FE] fel] SCR-20251121-mphe.png stein info at PaxDb - Help i= https://pax-db.org/downloads/6.0/...\\n— “ mo =—- oO\\n—oe . o * o~\\ng g ©\\na av] w\\n5 co + =\\nfe) 2 QB\\n= © o\\n2 >\\n_ ° —\\n8 ° a2\\n0.01 1 100 40000 10 100 1000 10000 10 100 1000 40000\\nhuman brain (ppm) eukaryotic average (ppm) multi-cellular average (ppm)\\n\\nProtein family abundance conservation. A: between human and mouse brain, B: between eukaryotes and bacteria. C: between multi-cellular and single-cellular organisms.\\n\\nQ: Can | export an abundance table for a few proteins of interest?\\n\\nA: Within a target species, multiple proteins (separated by comma) can be searched. and you will land in the page to disambiguate these proteins,\\nafter this step, you can view and download the abundance info in a TSV (tab separated value) file.\\n\\nQ: What does protein abundance (ppm) in PaxDb mean?\\n\\nA: “ppm” is short for parts per million. Protein abundance datasets in PaxDb are re-scaled to ppm. Abundance in \"ppm\" describes each protein\\nrelative to all other proteins in a sample (i.e. proportionality to counts of complete, individual protein molecules, not to molecular weights, protein\\nvolumes, or digested peptides). For instance, protein X has 300 copies per cell and counts of all proteins is 3 O00 000 molecules, then the protein\\nX\\'s abundance is expressed as 100 ppm.\\n\\nQ: Where do the data come from? Can | use it freely?\\n\\nA: All PaxDb data come from open-access proteomics data from a published experiment / research project, either directly or after re-processing.\\nAll data is under Creative Commons Attribution 4.0 International (CC BY 4.0).\\n\\nQ: What is the scope of PaxDb?\\n\\nA: Quantification of the whole proteome—at the organism or tissue level—under healthy, normal conditions.\\n\\ne ls sub-cellular protein abundance available? No\\ne Is post-translational modification data available? No\\ne ls disease sample available? No\\n\\nSy Q: What is iBAQ processing?\\n',\n",
|
||
" 'In [16]: print(hic.getGenomeID())\\nprint (hic.getResolutions())\\n\\n/home/aman/basejuicer/references/chrom.sizes\\n[2500000, 1000000, 500000, 250000, 100000, 50000, 25000, 10000, 5000, 2000, 1000, 500, 200, 100]\\n\\nIn [17]: for chrom in hic.getChromosomes():\\nprint(chrom.name, chrom. length)\\n\\nALL 2135083\\nNC_@24459.2 307041717\\nNC_@24460.2 244442276\\nNC_@24461.2 235667834\\nNC_@24462.2 246994605\\nNC_@24463.2 223902240\\nNC_@24464.2 174033170\\nNC_@24465.2 182381542\\nNC_@24466.2 181122637\\nNC_@24467.2 159769782\\nNC_@24468.2 150982314\\nNW_@17972002.1 50531\\nNW_@17972003.1 60109\\nNW_@17972004.1 59657\\nNW_@17972005.1 66261\\nNW_@17972006.1 83265\\nNW_@17972007.1 255484\\nNW_@17972008.1 71433\\nNW_@17972009.1 64470\\n\\nIn [18]: chromosomes = hic.getChromosomes()\\nprint(Len(chromosomes) )\\n\\n268\\n\\nIn [20]: matrix_object_chr4 = hic.getMatrixZoomData( \\'NW_@17972091.1\\', \\'NW_017972091.1\\', \"observed\", \"KR\", \"BP\", 20000)\\nFile did not contain KR normalization vectors for one or both chromosomes at 20000 BP\\n\\nMemoryError Traceback (most recent call last)\\nCell In[20], line 1\\n\\n-—--> 1 matrix_object_chr4 = hi¢.getMatrixZoomData( \\'NW_017972091.1\", \\'NW_017972091.1\", \"observed\", \"KR\", \"BP\", 2000\\nL))\\n\\nMemoryError: std::bad_alloc\\n',\n",
|
||
" 'Assembly Parameters\\n\\nParameters of the driver script, abyss-pe\\n\\na: maximum number of branches of a bubble [ 2 ]\\n\\nb : maximum length of a bubble (bp) [ \"\" ]\\n\\nB : Bloom filter size (e.g. \"100M\")\\n\\nc : minimum mean k-mer coverage of a unitig [ sqrt(median) ]\\n\\nd : allowable error of a distance estimate (bp) [ 6 ]\\n\\ne : minimum erosion k-mer coverage [ round(sqrt(median) ) ]\\n\\nE : minimum erosion k-mer coverage per strand [1if sqrt(median) > 2 else 0]\\n\\nG : genome size, used to calculate NG50\\n\\nH : number of Bloom filter hash functions [ 4 ]\\n\\nj : number of threads [ 2 ]\\n\\nk : size of k-mer (when K is not set) or the span of a k-mer pair (when K is set)\\n\\nke : minimum k-mer count threshold for Bloom filter assembly [ 2 ]\\n\\nK : the length of a single k-mer in a k-mer pair (bp)\\n\\n1: minimum alignment length of a read (bp) [ 40 ]\\n\\nm : minimum overlap of two unitigs (bp) [ @ (interpreted as k - 1) if mp is provided or if k<=5@ , otherwise\\n50]\\n\\nn: minimum number of pairs required for building contigs [ 10 ]\\n\\nN : minimum number of pairs required for building scaffolds [ 15-20 ]\\n\\nnp : number of MPI processes [ 1 ]\\n\\np : minimum sequence identity of a bubble [ 0.9 ]\\n\\nq: minimum base quality [ 3 ]\\n\\ns : minimum unitig size required for building contigs (bp) [ 1000 ]\\n\\nS : minimum contig size required for building scaffolds (bp) [ 100-5000 ]\\n+ : maximum length of blunt contigs to trim [ k ]\\n\\nv:use v=-v for verbose logging, v=-vv for extra verbose\\n\\nx : spaced seed (Bloom filter assembly only)\\n\\nLr_s : minimum contig size required for building scaffolds with linked reads (bp) [ S ]\\n\\nLr_n : minimum number of barcodes required for building scaffolds with linked reads [ 10 ]\\n',\n",
|
||
" \"@ Terminal Shell Edit View Window Help VW & *- Xx @O© & @) F Q S&S Wed12.Nov 11:52\\n\\naman — tmux — ssh -L 8783:localhost:8783 -L 8899:localhost:8899 biodata — 208x61\\n\\nR ~ tmux +\\n\\nwarnings.warn('Could not normalize this rule: ' + rule)\\n/home/biodata/miniconda3/envs/troppo_base/lib/python3.10/site-packages/cobamp/gpr/core.py:41: UserWarning: Could not normalize this rule: _ENSG@@000023839 or _ENSG@Q0000118777\\nwarnings.warn('Could not normalize this rule: ' + rule)\\n/home/biodata/miniconda3/envs/troppo_|\\nwarnings.warn('Could not normalize :\\n/home/biodata/miniconda3/envs/troppo_base/lib/python3.10/site-packages/cobamp/gpr/core.py:41: UserWarning: Could not normalize this rule: _ENSGQQ000183463\\n\\nb\\nt :\\nbase/lib/python3.10/site-packages/cobamp/gpr/core.py:41: UserWarning: Could not normalize this rule: _ENSGQQ000108846 or _ENSG@Q000125257\\nt 7 .\\n\\nb\\nwarnings.warn('Could not normalize this rule: ' + rule)\\nb\\nt\\nb\\nN\\n\\nhis rule: ' + rule)\\n\\n/home/biodata/miniconda3/envs/troppo_base/lib/python3.10/site-packages/cobamp/gpr/core.py:41: UserWarning: Could not normalize this rule: _ENSGQQ000010932\\nwarnings.warn('Could not normalize this rule: ' + rule)\\n/home/biodata/miniconda3/envs/troppo_base/lib/python3.10/site-packages/cobamp/gpr/core.py:41: UserWarning: Could not normalize this rule: _ENSGQQ000030066 and _ENSGQQ@000047410 and _ENSG@0000058804 and _ENSG@O\\n000069248 and _ENSGQ0000075188 and _ENSG@Q000085415 and _ENSG@0000093000 and _ENSGQ0000094914 and _ENSGQQ000095319 and _ENSGQ0000101146 and _ENSG@Q000102900 and _ENSG@0000108559 and _ENSGQ00@00110713 and _ENSG\\n@0000111581 and _ENSG@0000113569 and _ENSGQ0000119392 and _ENSG@0000120253 and _ENSGQQ0@00124789 and _ENSGQQ000125450 and _ENSGQ@Q000126883 and _ENSG@0000132182 and _ENSGQ@0000136243 and _ENSGQQ000138750 and _EN\\nSG00000139496 and _ENSGQ0000153201 and _ENSG@Q@000153207 and _ENSG@0000155561 and _ENSGQQ000157020 and _ENSGQQ000157349 and _ENSGQ0000163002 and _ENSGQ@Q000196313 and _ENSGQ@0000213024\\nwarnings.warn('Could not normalize this rule: ' + rule)\\n/home/biodata/miniconda3/envs/troppo_base/lib/python3.10/site-packages/cobamp/gpr/core.py:41: UserWarning: Could not normalize this rule: _ENSG@Q000102794\\nwarnings.warn('Could not normalize this rule: ' + rule)\\n/home/biodata/miniconda3/envs/troppo_base/lib/python3.10/site-packages/cobamp/gpr/core.py:41: UserWarning: Could not normalize this rule: _ENSGQQ000198099\\nwarnings.warn('Could not normalize this rule: ' + rule)\\n/home/biodata/miniconda3/envs/troppo_base/lib/python3.10/site-packages/cobamp/gpr/core.py:41: UserWarning: Could not normalize this rule: _ENSGQ@0000198099 or _ENSGQ0000197894\\nwarnings.warn('Could not normalize this rule: ' + rule)\\n/home/biodata/miniconda3/envs/troppo_base/lib/python3.10/site-packages/cobamp/gpr/core.py:41: UserWarning: Could not normalize this rule: _ENSG@@000184254\\nwarnings.warn('Could not normalize this rule: ' + rule)\\n/home/biodata/miniconda3/envs/troppo_base/lib/python3.10/site-packages/cobamp/gpr/core.py:41: UserWarning: Could not normalize this rule: _ENSGQ@000132746 or _ENSGQ@00@00132746 or _ENSGQQ@000006534 or _ENSGQQ000\\n072210\\n\\nwarnings.warn('Could not normalize this rule: ' + rule)\\n/home/biodata/miniconda3/envs/troppo_base/lib/python3.10/site-packages/cobamp/gpr/core.py:41: UserWarning: Could not normalize this rule: _ENSG@0000180011 or _ENSGQ0000180011\\nwarnings.warn('Could not normalize this rule: ' + rule)\\n/home/biodata/miniconda3/envs/troppo_base/lib/python3.10/site-packages/cobamp/gpr/core.py:41: UserWarning: Could not normalize this rule: _ENSG@0000006534\\nwarnings.warn('Could not normalize this rule: ' + rule)\\n/home/biodata/miniconda3/envs/troppo_base/lib/python3.10/site-packages/cobamp/gpr/core.py:41: UserWarning: Could not normalize this rule: _ENSGQQ000196616\\nwarnings.warn('Could not normalize this rule: ' + rule)\\n/home/biodata/miniconda3/envs/troppo_base/lib/python3.10/site-packages/cobamp/gpr/core.py:41: UserWarning: Could not normalize this rule: (_ENSG@00@0131828 and _ENSG@0000168291 and _ENSGQQ000150768) or (_ENSG\\n00000163114 and _ENSG@0000168291 and _ENSGQQQ00150768)\\n\\nhis rule: ' + rule)\\nase/lib/python3.10/site-packages/cobamp/gpr/core.py:41: UserWarning: Could not normalize this rule: _ENSG@0000134864\\nhis rule: ' + rule)\\nase/lib/python3.10/site-packages/cobamp/gpr/core.py:41: UserWarning: Could not normalize this rule: _ENSG@0000078124\\nhis rule: ' + rule)\\nase/lib/python3.10/site-packages/cobamp/gpr/core.py:41: UserWarning: Could not normalize this rule: _ENSG@0000006695\\nhis rule: ' + rule)\\nase/lib/python3.10/site-packages/cobamp/gpr/core.py:41: UserWarning: Could not normalize this rule: _ENSG@Q000138744\\nhis rule: ' + rule)\\nase/lib/python3.10/site-packages/cobamp/gpr/core.py:41: UserWarning: Could not normalize this rule: _ENSG@Q000172264 or _ENSGQ@0000133315 or _ENSGQQ000124596\\nhis rule: ' + rule)\\nase/lib/python3.10/site-packages/cobamp/gpr/core.py:41: UserWarning: Could not normalize this rule: _ENSG@0000133315\\nhis rule: ' + rule)\\nase/lib/python3.10/site-packages/cobamp/gpr/core.py:41: UserWarning: Could not normalize this rule: _ENSG@0000120942\\nhis rule: ' + rule)\\nase/lib/python3.10/site-packages/cobamp/gpr/core.py:41: UserWarning: Could not normalize this rule: _ENSG@Q000161533 or _ENSG@Q000168306 or _ENSGQ0000087008 or _ENSGQQG00\\n00007171 or _ENSG@0@000148832 or _ENSG@0000179761 or _ENSG@0000158125\\nhis rule: ' + rule)\\nase/lib/python3.10/site-packages/cobamp/gpr/core.py:41: UserWarning: Could not normalize this rule: _ENSG@0000114120\\nhis rule: ' + rule)\\nase/lib/python3.10/site-packages/cobamp/gpr/core.py:41: UserWarning: Could not normalize this rule: _ENSG@@000114120 or _ENSG@Q0000171612\\nhis rule: ' + rule)\\nase/lib/python3.10/site-packages/cobamp/gpr/core.py:41: UserWarning: Could not normalize this rule: _ENSG@0000144182\\nhis rule: ' + rule)\\n\\nwarnings.warn('Could not normalize\\n/home/biodata/miniconda3/envs/troppo_|\\nwarnings.warn('Could not normalize\\n/home/biodata/miniconda3/envs/troppo_|\\nwarnings.warn('Could not normalize\\n/home/biodata/miniconda3/envs/troppo_|\\nwarnings.warn('Could not normalize\\n/home/biodata/miniconda3/envs/troppo_|\\nwarnings.warn('Could not normalize\\n/home/biodata/miniconda3/envs/troppo_|\\nwarnings.warn('Could not normalize\\n/home/biodata/miniconda3/envs/troppo_|\\nwarnings.warn('Could not normalize\\n/home/biodata/miniconda3/envs/troppo_|\\nwarnings.warn('Could not normalize\\n/home/biodata/miniconda3/envs/troppo_|\\n110887 or _ENSG@0@000203797 or _ENSG@O\\nwarnings.warn('Could not normalize\\n/home/biodata/miniconda3/envs/troppo_|\\nwarnings.warn('Could not normalize\\n/home/biodata/miniconda3/envs/troppo_|\\nwarnings.warn('Could not normalize\\n/home/biodata/miniconda3/envs/troppo_|\\nwarnings.warn('Could not normalize :\\n/home/biodata/miniconda3/envs/troppo_base/lib/python3.10/site-packages/cobamp/gpr/core.py:41: UserWarning: Could not normalize this rule: _ENSGQ0000248098 and _ENSGQ@000083123 and _ENSG@0000137992\\nwarnings.warn('Could not normalize this rule: ' + rule)\\n\\ncen espyenone nmebtocatat 10252 22-Nov-25\\n\\nAOt Ot ot ot Oot#ot# ot ot oHte# oe oe orl\\n\",\n",
|
||
" \"This slide focuses on the effect of slow fluctuations in population size on the effective\\npopulation size (V.) and emphasizes the conditions under which the harmonic mean formula for\\nN- applies. Slow fluctuations occur when the time period of interest (Z’) is much shorter than the\\nminimum population size (min[N;]) across the fluctuation cycle. In such cases, the population\\n\\n-1\\nsize appears relatively stable, and the harmonic mean formula (N. = (4 wh x) ) may not\\n\\naccurately represent the effective population size over longer periods. The diagram illustrates that\\nduring slow fluctuations, the coalescent events occur more gradually, and population size changes\\nare less abrupt compared to rapid fluctuations. The key message is that for the harmonic mean\\ncalculation to be meaningful, the time scale of observation (Z') must be significantly smaller than\\nthe scale of population size changes, ensuring accurate modeling of genetic drift and coalescence\\n\\nprocesses over generations.\\n\",\n",
|
||
" '1\\nOmics Data (RNA-seq, Proteomics, Metabolomics) |\\nJ\\n\\n1\\nContextualization (RIPTiDe, CORDA, etc.) |\\nJ\\n\\n1\\nContext-specific GEMs (csGEMs) |\\n— same topology, different active reactions\\nJ\\n\\nGraph Construction |\\nNodes = Reactions |\\nEdges = Shared metabolites\\n\\nFeatures = expr., flux, reversibility, etc.\\nJ\\n\\nGNN Training |\\nLearn embeddings capturing |\\n\\ncontext-driven network rewirings |\\nJ\\n\\nDownstream Applications |\\n- Predict flux states\\n- Classify tissue/disease\\n\\n- Identify key pathways\\n- Compare GNN vs rule-based |\\n|\\n\\nVv\\n',\n",
|
||
" 'library(tximport)\\n\\n# Define paths to trimmed reads and extract sample names\\nfastq_files <- list.files(path = \"/mnt/volume/data/group8/studies/trimmed\", pattern = \"*_1.fastq.gz\", full.names = TRUE\\nsamples <- basename(fastq_files) %>% sub(\"_1\\\\\\\\.fastq\\\\\\\\.gz\", \"\", .)\\n\\n# Create paths to Kallisto abundance files\\nfiles <- file.path(\"/mnt/volume/data/group8/kallisto_output\", samples, \"abundance.tsv\")\\nnames(files) <- samples\\n\\n# Check that all files exist\\n\\nmissing_files <- files[!file.exists(files)]\\n\\nif (length(missing_files) > 0) {\\ncat(\"Warning: The following abundance.tsv files are missing:\\\\n\")\\nprint(missing_files)\\n\\n} else {\\ncat(\"All abundance.tsv files found.\\\\n\")\\n\\n}\\n\\n# Load the tx2gene mapping\\ntx2gene <- read.csv(\"/mnt/volume/data/group8/references/tx2gene.csv\")\\n\\n# Run tximport to summarize counts to gene level\\ntxi <- tximport(files, type = \"kallisto\", tx2gene = tx2gene\\n\\n# Check the structure of the imported object\\nstr(txi)\\n\\n# Save gene-level counts to a CSV file\\n\\nwrite.csv(txi$counts, file = \"/mnt/volume/data/group8/kallisto_output/gene_counts.csv\", row.names = TRUE)\\nwrite.csv(txig$abundance, file = \"/mnt/volume/data/group8/kallisto_output/gene_counts_abundance.csv\", row.names = TRUE\\ncat(\"Gene-level counts saved to /mnt/volume/data/group8/kallisto_output/gene_counts.csv\\\\n\")\\n\\n(1) @ 00s\\n\\nError in basename(fastq_files) %>% sub(\"_1\\\\\\\\.fastq\\\\\\\\.gz\", \"\", «): could not find function \"%>%\"\\nTraceback:\\n',\n",
|
||
" 'The MethylStar pipeline\\n\\nStandard BS-Seq MethyIStar Pipeline\\nWorkFlow ‘\\n\\nInput FastQ\\n~ single-cell (de-multiplexed)\\n\\n{ ~ bulk\\n\\nQuality\\nControl\\n\\nInput FastQ\\n\\nTrim F ; — Quality Reports\\nTrimomatic FASTAC\\nAdapters J l — Genome Coverage\\nT [= Sequencing Depth\\n\\n‘Align reads) (—_ Bismark\\nto genome Mapper\\n\\npre-processing\\n\\nInput BAM\\n\\nRemoval of PCR) (Bismark\\nDuplicates J {_De-duplicate\\n\\nv\\n\\nWaintign) Bismark\\ngel inpeinal = Methylation\\n\\n— Extractor\\nNormalization* C Methimpute*\\n\\nImputation**\\n\\nDropouts*\\n\\nDifferential\\n\\nmethylation” *for single-cell data\\n\\n“for bulk data\\n\\ndownstream analysis\\n\\nAnnotation*\\n\\n',\n",
|
||
" \"In [4]: import numpy as np\\nimport matplotlib.pyplot as plt\\n\\n# Load the Q matrix\\nq_matrix = np. loadtxt('inp_admix.3.Q')\\n\\n# Sort individuals by population assignment for better visualization\\nsorted_indices = np.argsort(q_matrix[:, @])\\nq_matrix = q_matrix[sorted_indices]\\n\\n# Create the plot\\nplt. figure(figsize=(10, 5))\\ncolors ['#1f77b4', ‘#ff7f0e', '#2ca@2c']\\nfor i in range(q_matrix.shape[1]):\\nplt.bar(range(q_matrix.shape[0]), q_matrix[:, i], bottom=np.sum(q_matrix[:, :i], axis=1))\\n\\nplt.title('Ancestry Proportions (K=3)')\\nplt.xlabel( ‘Individuals (sorted) ')\\nplt.ylabel('Ancestry Proportion')\\n\\nplt. show()\\n\\nAncestry Proportions (K=3)\\n\\n104\\n\\n0.87\\n\\n0.64\\n\\n0.44\\n\\nAncestry Proportion\\n\\n0.27\\n\\nie} 10 20 30 40 50\\nIndividuals (sorted)\\n\\n\",\n",
|
||
" 'trimmomatic PE \\\\\\n$R1 $R2 \\\\\\n$OUT_PAIRED_R1 $OUT_UNPAIRED_R1 \\\\\\n$OUT_PAIRED_R2 $OUT_UNPAIRED_R2 \\\\\\nILLUMINACLIP: TruSeq3-PE. fa:2:30:10:2:True \\\\\\nLEADING:5 \\\\\\nTRAILING:5 \\\\\\nSLIDINGWINDOW: 4:20 \\\\\\nMINLEN: 30\\n',\n",
|
||
" '@ ZoomWorkplace Meeting View Edit Window\\n\\nx [==] Layout v\\nER + ‘© Reset\\nNew\\n¥ Slide v Section v\\nClipboard 8 Slides Font\\na\\n7\\n3° 4\\n+7\\n8\\ni\\n9\\n10 oan\\nES\\n11 on v\\nSlide 6 of 14 4 — English (India) & Accessibility: Investigate\\n\\n= OOo O\\n\\nALLOYG|*\\n\\n5 5X Arrange\\n\\n@ \\\\ Los v\\nParagraph Drawing\\n\\n3.1.\\n\\n= Notes\\n\\nPL Find\\n\\nab Replace\\n[> Select »\\n\\nEditing\\n\\nly\\n\\n§3 comments =\\n\\nSe Wed Feb 12 22:15\\n\\nIs there any cellular evidence for PD growth signal in epidermis?\\n\\nCreate PDF Create PDF and Add-ins\\nand Share link Share via Outlook\\nAdobe Acrobat Add-ins ON\\na\\n¥\\n\\n',\n",
|
||
" '1. Cross-check Against the Paper\\'s AP2/ERF Genes\\nThe paper identified four key AP2/ERF transcription factors:\\n\\n¢ HORVU4Hr1G077310\\n\\ne¢ HORVU5Hr1G080790 (HvERF1)\\n\\n¢ HORVU1Hr1G058940\\n\\n¢ HORVUS5Hr1G062940\\n\\nYou found HORVU.MOREX.r3.1HG0062940, which likely corresponds to HORVU5Hr1G062940,\\none of the AP2/ERF genes identified in the paper.\\n\\n2. Check Its Expression in Your DESeq2 Results\\n\\nNow, check if HORVU.MOREX.r3.1HG0062940 is significantly differentially expressed in your\\nresults.\\n\\nUsing Linux/macOS Terminal\\n\\nbash Copy % Edit\\n\\ngrep \"HORVU.MOREX. r3.1HG@062940\" deseq2_results.csv\\n\\nUsing R\\n\\nr Copy % Edit\\n\\nresults_df <- read.csv(\"deseq2_results.csv\")\\n\\nresults_df [grep(\"HORVU.MOREX. r3.1HGQ062940\", results_df$GeneID), ]\\n\\nIf you find it, check:\\n',\n",
|
||
" 'Empirical CDF\\n\\nVCdled FeylOrn (loo WO 1 lo) Fim 1UUYU DP\\n\\nHS +4h\\n\\nHS +24 h\\n\\nNHS\\n\\n20\\n\\n40\\n\\n60 20 40 60 20\\n\\nCPM TSS+200_TSS+700\\n\\n40\\n\\n60\\n\\n',\n",
|
||
" 'Read Counts\\n\\nValid Pairs — duplicates and contact ranges\\n\\n0.050\\n\\n0.025\\n\\n0.000 NaN-%\\n\\n-0.025\\n\\n-0.050\\ndata\\n\\nNaN-%\\n\\nDuplicates (%)\\n\\nValid Interactions (%)\\n\\nCis long-range (>20kb) (%)\\n\\nCis short-range contacts (<20kb) (%)\\n\\nTrans Contacts (%)\\n',\n",
|
||
" 'TAG\\n\\n@ latest .\\ndocker pull toluene123/rstudio:latest Copy\\nLast pushed 2 minutes by toluene123\\n\\nDigest OS/ARCH Last pull Compressed size ©\\n\\ncbb21d4b533f linux/arm64 less than 1 day 1.9 GB\\n',\n",
|
||
" 'Error in checkFullRank(modelMatrix): the model matrix is not full rank, so the model cannot be fit as specified.\\nOne or more variables or interaction terms in the design formula are linear\\ncombinations of the others and must be removed.\\n\\nPlease read the vignette section ‘Model matrix not full rank\\':\\n\\nvignette( \\'DESeq2\\')\\n\\nTraceback:\\n\\n1. DESeqDataSet(se, design = design, ignoreRank)\\n\\n2. checkFullRank(modelMatrix)\\n\\n3. stop(\"the model matrix is not full rank, so the model cannot be fit as specified.\\\\n One or more variables or interaction terms in the\\n4. .handleSimpleError(function (cnd)\\n\\nft\\nwatcher$capture_plot_and_output()\\ncnd <- sanitize_call(cnd)\\nwatcher$push(cnd)\\nswitch(on_error, continue = invokeRestart(\"eval_continue\"),\\nstop = invokeRestart(\"eval_stop\"), error = NULL)\\n. }, “the model matrix is not full rank, so the model cannot be fit as specified.\\\\n One or more variables or interaction terms in the d\\nbase: : quote(checkFullRank(modelMatrix) ) )\\n\\n',\n",
|
||
" '[21]:\\n\\nac = gt.to_allele_counts()\\nnum_samples = gt.shape[1]\\nan = 2 * num_samples\\n\\naf = ac / an\\n\\nprint (af)\\n\\n[[e. @.49166667 0.\\n[o. @.49166667 0.\\n[0. 40833333 0.08333333 0.\\n[0.24166667 0.15 Q.\\n[0.25 @.15 Q.\\n\\n[0.24166667 0.09166667 Q.\\n\\n»sum(axis=1)\\n\\n# Assuming gt is a 2D array where rows are variants\\n\\nf+\\n\\n+\\n\\nT+)\\n\\n',\n",
|
||
" 'Genome vv Tracks ¥ Sample Info v Session v Share Save Image Circular View v Help v\\n\\nIGV oxford_e...me.fasta _ tig00000002:1,988,781-1,993,318 § Q. 4,538 bp (Select Tracks ) (Crosshairs )(_Center Line )(TrackLabels) @ +)\\n\\nC D)\\n\\n1,989 kb 1,990 kb 1,991 kb 1,992 kb 1,993 kb\\n1 L 1 L f\\n\\nmdtL\\n\\n',\n",
|
||
" 'Sample\\n\\nSample 1\\nSample 2\\nSample 3\\nSample 4\\n\\nSample 5\\n\\nVariants (N)\\n2510\\n\\n4923\\n\\n65\\n\\n1115\\n\\n3637\\n\\nTransitions (N_Ts)\\n1207\\n\\n1896\\n\\n34\\n\\n468\\n\\n1756\\n\\nTransversions (N_Tv)\\n499\\n\\n759\\n\\n20\\n\\n422\\n\\n651\\n\\nTs/Tv Ratio\\n2.42\\n\\n2.50\\n\\n1.70\\n\\n1.11\\n\\n2.70\\n',\n",
|
||
" 'Q FastQC Report Smet eee\\n\\nSummary @per base sequence content\\n\\nSequence content across all bases\\n\\nQbasic Statistics 100 eT\\n\\n. %C\\n\\nOber base sequence quality 60 soa\\nOber sequence quality scores %G\\nOber base sequence content 80\\nOber sequence GC content 70\\nOber base N content\\nOsequence Length Distribution 80\\n©} sequence Duplication Levels 50\\nQ overrepresented sequences\\nQoaaapter Content “0\\n\\n30\\n\\n20\\n\\n10\\n\\n123456789 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51 53 55 57 59 61 63 65\\nPosition in read (bp)\\n\\nProduced by FastQC (version 0.12.1)\\n',\n",
|
||
" 'Visualization\\n\\nIn [220]: # total or relative numbers of unique clones.\\nclonalquant(\\n\\ncloneCalt:\\nchain = \"both\",\\nscale = TRUE)\\n\\ntrict\",\\n\\nSamples\\n\\nPercent of Unique Clones\\n8\\neagaegge\\n\\ng\\n\\nor @ & Py Ba 6 7 Se\\n‘Samples\\n\\n',\n",
|
||
" \"Sequencing\\n\\nSequenced Reads: 547812856\\n\\nDuplication and Complexity (% Sequenced Reads)\\n\\nAnalysis of Unique Reads (% Sequenced Reads / % Unique Reads)\\n\\nIntra-fragment Reads: 34,307,600\\n\\nBelow MAPQ Threshold: 355,353,763 (64.87% / 73.27%)\\n\\nHi-C Contacts: 95,311,495 (17.40% / 19.65%)\\n3' Bias (Long Range): 97% - 3%\\n\\nPair Type % (L-I-O-R): 25% - 25% - 25% - 25%\\n\\nAnalysis of Hi-C Contacts (% Sequenced Reads / % Unique Reads)\\n\\nInter-chromosomal: 22,195,088 (4.05% / 4.58%)\\nIntra-chromosomal: 73,116,407 (13.35% / 15.08%)\\nLong Range (>20Kb): 35,425,148 (6.47% / 7.30%)\\n\",\n",
|
||
" \"w SLOP fe ON ENS VEU’\\nplt.show()\\n\\nTypeError Traceback (most recent call last)\\nInput In [5], in <cell line: 27>()\\n\\n26 # Plot ancestry proportions sorted by PCA clusters\\n\\n27 for i in range(q_matrix.shape[1]):\\n\\n28 plt.bar(range(q_matrix.shape[0]), q_matrix[:, il,\\n\\n29 bottom=np.sum(q_matrix[:, :il, axi\\n---> 30 color:\\n\\n32 plt.title('Ancestry Proportions by PCA Clusters (K=3)')\\n\\n33 plt.xlabel('Individuals (grouped by PCA clusters)')\\n\\n,\\n\\nTypeError: list indices must be integers or slices, not Series\\n\\n<Figure size 1200x500 with @ Axes>\\n\\n)\\n\",\n",
|
||
" 'chromosome1 x1 x2 chromosome2 yl y2 color observed (0\\nexpected_bottom_left expected_donut expected_horizontal expected_vertical\\nfdr_bottom_left fdr_donut fdr_horizontal fdr_vertical\\nnumber_collapsed centroid1 centroid2 radius\\n',\n",
|
||
" 'j\\n\\noz\\n\\nA10-08178.raw.gz\\n\\nj\\n\\noz\\n\\nA10-08189.raw.gz\\n\\nj\\n\\noz\\n\\nA10-08194.raw.gz\\n\\nj\\n\\noz\\n\\nA10-08199.raw.gz\\n\\nj\\n\\noz\\n\\nA10-08204.raw.gz\\n\\nj\\n\\noz\\n\\nA10-08209.raw.gz\\n\\nj\\n\\noz\\n\\nA10-08214.raw.gz\\n\\nj\\n\\noz\\n\\nA10-08219.raw.gz\\n\\nj\\n\\noz\\n\\nA10-08235.raw.gz\\n\\nj\\n\\noz\\n\\nA10-08241.raw.gz\\n\\nj\\n\\noz\\n\\nA10-08249.raw.gz\\n\\nj\\n\\noz\\n\\nA10-08254.raw.gz\\n\\nj\\n\\noz\\n\\nA10-08259.raw.gz\\n\\nj\\n\\noz\\n\\nA10-08265.raw.gz\\n\\nj\\n\\noz\\n\\nA10-08270.raw.gz\\n\\nj\\n\\noz\\n\\nA10-08275.raw.gz\\n\\nj\\n\\noz\\n\\nA10-08281.raw.gz\\n\\nj\\n\\noz\\n\\nA10-08289.raw.gz\\n\\ni\\n\\noz\\n\\nA10-08294.raw.gz\\n\\nj\\n\\noz\\n\\nA10-12053.raw.gz\\n\\nj\\n\\noz\\n\\nA10-12058.raw.gz\\n\\nj\\n\\noz\\n\\nA10-12063.raw.gz\\n\\nj\\n\\noz\\n\\nA10-12068.raw.gz\\n\\nj\\n\\noz\\n\\nA10-12076.raw.gz\\n\\n=\\n\\nRAW\\n\\nA14-07017.raw\\n\\nex za)\\n\\nRAW\\n\\nA14-07022.raw\\n\\n=\\n\\nRAW\\n\\nA14-07028.raw\\n\\nxr\\n\\nRAW\\n\\nA14-07033.raw\\n\\nex ra)\\n\\nRAW\\n\\nA14-07038.raw\\n\\nxs\\n\\nRAW\\n\\nA14-07044.raw\\n\\nxs\\n\\nRAW\\n\\nA14-07049.raw\\n\\n=\\n\\nRAW\\n\\nA14-07054.raw\\n\\n=\\n\\nRAW\\n\\nA14-07060.raw\\n\\nex za)\\n\\nRAW\\n\\nA14-07065.raw\\n\\nxs\\n\\nRAW\\n\\nA14-07070.raw\\n\\nxz)\\n\\nRAW\\n\\nA14-07076.raw\\n\\nxa\\n\\nRAW\\n\\nA14-07081.raw\\n\\n=\\n\\nRAW\\n\\nA14-07086.raw\\n\\n=\\n\\nRAW\\n\\nA14-07092.raw\\n\\nex za)\\n\\nRAW\\n\\nA14-07097.raw\\n\\n=\\n\\nRAW\\n\\nA14-07102.raw\\n\\nxr\\n\\nRAW\\n\\nA14-07109.raw\\n\\nxr\\n\\nRAW\\n\\nA14-07114.raw\\n\\nxs\\n\\nRAW\\n\\nA14-07119.raw\\n\\nex za)\\n\\nRAW\\n\\nA14-07125.raw\\n\\nj\\n\\noz\\n\\nF020549.mzid.gz\\n\\nj\\n\\noz\\n\\nA10-08179.raw.gz\\n\\nj\\n\\noz\\n\\nA10-08190.raw.gz\\n\\nj\\n\\noz\\n\\nA10-08195.raw.gz\\n\\nj\\n\\noz\\n\\nA10-08200.raw.gz\\n\\nj\\n\\noz\\n\\nA10-08205.raw.gz\\n\\nj\\n\\noz\\n\\nA10-08210.raw.gz\\n\\nj\\n\\noz\\n\\nA10-08215.raw.gz\\n\\nj\\n\\noz\\n\\nA10-08224.raw.gz\\n\\nj\\n\\noz\\n\\nA10-08236.raw.gz\\n\\nj\\n\\noz\\n\\nA10-08242.raw.gz\\n\\nj\\n\\noz\\n\\nA10-08250.raw.gz\\n\\nj\\n\\ncz\\n\\nA10-08255.raw.gz\\n\\nj\\n\\noz\\n\\nA10-08260.raw.gz\\n\\nj\\n\\noz\\n\\nA10-08266.raw.gz\\n\\nj\\n\\noz\\n\\nA10-08271.raw.gz\\n\\nj\\n\\noz\\n\\nA10-08276.raw.gz\\n\\ni\\n\\noz\\n\\nA10-08282.raw.gz\\n\\nj\\n\\noz\\n\\nA10-08290.raw.gz\\n\\nj\\n\\noz\\n\\nA10-08295.raw.gz\\n\\nj\\n\\ncz\\n\\nA10-12054.raw.gz\\n\\nj\\n\\noz\\n\\nA10-12059.raw.gz\\n\\nj\\n\\noz\\n\\nA10-12064.raw.gz\\n\\nj\\n\\noz\\n\\nA10-12069.raw.gz\\n\\nj\\n\\noz\\n\\nA10-12077.raw.gz\\n\\n=\\n\\nRAW\\n\\nA14-07018.raw\\n\\n3\\n\\nRAW\\n\\nA14-07024.raw\\n\\nSra)\\n\\nRAW\\n\\nA14-07029.raw\\n\\nea)\\n\\nRAW\\n\\nA14-07034.raw\\n\\nxs\\n\\nRAW\\n\\nA14-07040.raw\\n\\nss\\n\\nRAW\\n\\nA14-07045.raw\\n\\nxr\\n\\nRAW\\n\\nA14-07050.raw\\n\\nss\\n\\nRAW\\n\\nA14-07056.raw\\n\\n=\\n\\nRAW\\n\\nA14-07061.raw\\n\\n3\\n\\nRAW\\n\\nA14-07066.raw\\n\\n=\\n\\nRAW\\n\\nA14-07072.raw\\n\\nss\\n\\nRAW\\n\\nA14-07077.raw\\n\\nEra)\\n\\nRAW\\n\\nA14-07082.raw\\n\\nss\\n\\nRAW\\n\\nA14-07088.raw\\n\\n=\\n\\nRAW\\n\\nA14-07093.raw\\n\\n3\\n\\nRAW\\n\\nA14-07098.raw\\n\\n=\\nSra)\\n\\nRAW\\n\\nA14-07104.raw\\n\\nea)\\n\\nRAW\\n\\nA14-07110.raw\\n\\nea)\\n\\nRAW\\n\\nA14-07115.raw\\n\\nxr\\n\\nRAW\\n\\nA14-07121.raw\\n\\n=\\n3\\n\\nRAW\\n\\nA14-07126.raw\\n\\nj\\n\\noz\\n\\nF023569.mzid.gz\\n\\nj\\n\\noz\\n\\nA10-08181.raw.gz\\n\\nj\\n\\noz\\n\\nA10-08191.raw.gz\\n\\nj\\n\\noz\\n\\nA10-08196.raw.gz\\n\\nj\\n\\noz\\n\\nA10-08201.raw.gz\\n\\nj\\n\\noz\\n\\nA10-08206.raw.gz\\n\\nj\\n\\noz\\n\\nA10-08211.raw.gz\\n\\nj\\n\\noz\\n\\nA10-08216.raw.gz\\n\\nj\\n\\noz\\n\\nA10-08225.raw.gz\\n\\nj\\n\\noz\\n\\nA10-08237.raw.gz\\n\\nj\\n\\noz\\n\\nA10-08243.raw.gz\\n\\nj\\n\\noz\\n\\nA10-08251.raw.gz\\n\\nj\\n\\noz\\n\\nA10-08256.raw.gz\\n\\nj\\n\\noz\\n\\nA10-08262.raw.gz\\n\\nj\\n\\noz\\n\\nA10-08267.raw.gz\\n\\nj\\n\\noz\\n\\nA10-08272.raw.gz\\n\\nj\\n\\noz\\n\\nA10-08277.raw.gz\\n\\nj\\n\\noz\\n\\nA10-08286.raw.gz\\n\\nj\\n\\noz\\n\\nA10-08291.raw.gz\\n\\ni\\n\\noz\\n\\nA10-08296.raw.gz\\n\\nj\\n\\noz\\n\\nA10-12055.raw.gz\\n\\nj\\n\\noz\\n\\nA10-12060.raw.gz\\n\\nj\\n\\noz\\n\\nA10-12065.raw.gz\\n\\nj\\n\\noz\\n\\nA10-12070.raw.gz\\n\\nj\\n\\noz\\n\\nA10-12078.raw.gz\\n\\n=\\n\\nRAW\\n\\nA14-07020.raw\\n\\nex 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za)\\n\\nRAW\\n\\nA14-07127.raw\\n\\ngenerated\\n\\nj\\n\\noz\\n\\nA10-08182.raw.gz\\n\\nj\\n\\noz\\n\\nA10-08192.raw.gz\\n\\nj\\n\\noz\\n\\nA10-08197.raw.gz\\n\\nj\\n\\noz\\n\\nA10-08202.raw.gz\\n\\nj\\n\\noz\\n\\nA10-08207.raw.gz\\n\\nj\\n\\noz\\n\\nA10-08212.raw.gz\\n\\nj\\n\\noz\\n\\nA10-08217.raw.gz\\n\\nj\\n\\noz\\n\\nA10-08234.raw.gz\\n\\nj\\n\\noz\\n\\nA10-08240.raw.gz\\n\\nj\\n\\noz\\n\\nA10-08246.raw.gz\\n\\nj\\n\\noz\\n\\nA10-08253.raw.gz\\n\\nj\\n\\ncz\\n\\nA10-08258.raw.gz\\n\\nj\\n\\noz\\n\\nA10-08264.raw.gz\\n\\nj\\n\\noz\\n\\nA10-08269.raw.gz\\n\\nj\\n\\noz\\n\\nA10-08274.raw.gz\\n\\nj\\n\\noz\\n\\nA10-08280.raw.gz\\n\\ni\\n\\noz\\n\\nA10-08287.raw.gz\\n\\nj\\n\\noz\\n\\nA10-08292.raw.gz\\n\\nj\\n\\noz\\n\\nA10-08297.raw.gz\\n\\nj\\n\\ncz\\n\\nA10-12056.raw.gz\\n\\nj\\n\\noz\\n\\nA10-12061.raw.gz\\n\\nj\\n\\noz\\n\\nA10-12066.raw.gz\\n\\nj\\n\\noz\\n\\nA10-12071.raw.gz\\n\\nA14-07016.raw\\n\\n=\\n\\nRAW\\n\\nA14-07021.raw\\n\\nRAW\\n\\nA14-07026.raw\\n\\nRAW\\n\\nA14-07032.raw\\n\\nRAW\\n\\nA14-07037.raw\\n\\nRAW\\n\\nA14-07042.raw\\n\\nss\\n\\nRAW\\n\\nA14-07048.raw\\n\\nxr\\n\\nRAW\\n\\nA14-07053.raw\\n\\nss\\n\\nRAW\\n\\nA14-07058.raw\\n\\nA14-07064.raw\\n\\n3\\n\\nRAW\\n\\nA14-07069.raw\\n\\nA14-07074.raw\\n\\nRAW\\n\\nA14-07080.raw\\n\\nRAW\\n\\nA14-07085.raw\\n\\nss\\n\\nRAW\\n\\nA14-07090.raw\\n\\nRAW\\n\\nA14-07096.raw\\n\\nRAW\\n\\nA14-07101.raw\\n\\nRAW\\n\\nA14-07107.raw\\n\\nA14-07113.raw\\n\\nxa)\\n\\nRAW\\n\\nA14-07118.raw\\n\\nxr\\n\\nRAW\\n\\nA14-07123.raw\\n\\nj\\n\\ncz\\n\\nF020490.mzid.gz\\n\\nmascot_daemon_merge.mg\\nf\\n',\n",
|
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" 'EXPLORER on @ 06_Exercise_finding_datasets_data_download.ipynb @ 10_mapping_practical_Kallisto.ipynb @ 2 O\\n\\n\\\\v GROUPS [SSH: COMPUTE2] funcourse > workflows > scripts > i 10_mapping_practical_Kallisto.ipynb > ms Aligning to Transcriptome with Kallisto. > m# STAR Alignment > ms RNA-Seq Quantification > @ %%bash\\n> funcourse + Code -+ Markdown | [> RunAll ‘© Restart =; Clear All Outputs © GoTo | fm] Variables Outline --- &, FunCourse (Python 3.13.1)\\n> kallisto_output /*\\n> references\\n© studies 1 Python\\n> fastqc_reports_initial\\n> SRR21866476 Dp De on)\\n> SRR21866477 DY csbash\\n> SRR21866478\\n> SRR21866479 # Set variables\\n> SRR21866480 tune\\n> SRR21866481 # Align and quantify reads\\n> SRR21866482 for r1 in /mnt/volume/data/group8/studies/trimmed/*_1.fastq.gz; do\\n> SRR21866483 r2=\"${r1/_1.fastq.gz/_2.fastq.gz}\" # Fixing R2 filename pattern\\n> SRR21866484 sample=$(basename \"$r1\" \"_1.fastq.gz\")\\n\\n> SRR21866485 # Make an output directory\\n\\n> SRR21866486 mkdir -p /mnt/volume/data/group8/kallisto_output/\"$sample\"\\n> SRR21866487\\nv trimmed # Kallisto quantification\\n\\nkallisto quant -i /mnt/volume/data/group8/funcourse/workf lows/scripts/Hordeum_vulgare.MorexV3_pseudomolecules_assembly.cds.all.fa_kallisto_index.idx \\\\\\n\\n> fastqc_trimmed_reports\\n-o /mnt/volume/data/group8/kallisto_output/\"$sample\" \\\\\\n\\n= SRR21866470_1_trimmed.fastq.gz -t $threads \\\\\\nSRR21866470_2_trimmed.fastq.gz TAO Ty\\n= SRR21866471_1_trimmed.fastq.gz done\\n= SRR21866471_2_trimmed.fastq.gz\\n= SRR21866472_1_trimmed.fasta.gz [16] © 00s ® Python\\nSRR21866472_2_trimmed.fastq.gz ee ’\\nSRR21866473_1_trimmed.fastq.gz Error: file not found /mnt/volume/data/group8/studies/trimmed/*_1.fastq.gz ;\\nRR21866473_2_trimmed.fastg.gz Error: file not found /mnt/volume/data/group8/studies/trimmed/*_2.fastq.gz\\n= GREAT Tales ee Usage: kallisto quant [arguments] FASTQ-files .\\n= SRR21866474_2_trimmed.fastq.gz\\nSRR21866475_1_trimmed.fastq.gz Required arguments:\\nSRR21866475_2_trimmed.fastq.gz -i, --index=STRING Filename for the kallisto index to be used for \"\\n= SRR21866476_1_trimmed.fasta.gz quantification\\n-0, --output-dir=STRING Directory to write output to\\n= SRR21866476_2_trimmed.fastq.gz F\\n= SRR21866477_1_trimmed.fastq.gz Optional arguments: ]\\nSRR21866477_2_trimmed.fastq.gz -b, --bootstrap-samples=INT Number of bootstrap samples (default: @) :\\n= SRR21866478_1_trimmed fasta.gz —-seed=INT Seed for the bootstrap sampling (default: 42)\\n—-plaintext Output plaintext instead of HDF5\\n= SRR21866478_2_trimmed.fastg.gz are quant shy stniteand Gee\\n= SRR21866479_1_trimmed fastq.gz --single-overhang Include reads where unobserved rest of fragment is\\n= SRR21866479_2_trimmed.fastq.gz predicted to lie outside a transcript\\nSRR21866480_1_trimmed.fastq.gz\\nPROBLEMS ($9) OUTPUT DEBUGCONSOLE TERMINAL PORTS JUPYTER @ bash - references /\\\\ + v Wu A x\\n\\n= SRR21866480_2_trimmed.fastq.gz\\n\\n= SRR21866481_1_trimmed.fastq.gz Approx 10% complete for SRR21866483_2_trimmed. fastq.gz\\n\\n. Approx 15% complete for SRR21866483_2_trimmed. fastq.gz\\nRR21866481 2 trimmed.fasta.gz GRD Tess GEN PUSS WO BREZATS EL ee ert Seb EE aks\\n\\n',\n",
|
||
" 'E prediction (1).csv X\\n\\n|=: |\\nchr sl e1 chr s2 e2 prob interacted\\n2 22526446 22528946 2 22814566 22817066 @.07493626 0\\n1 266059450 266061950 1 268070606 268073106 0.00105671 20\\n7 147503328 147505828 8 128360720 128363220 @.11050791 0\\n6 83428606 83431106 6 83561424 83563924 @.@5532939 @\\n2 136764680 136767180 2 194169328 194171828 @.02850886 0\\n4 69078758 69081258 B73V4_ctg123 40538 43038 @.97231692 1\\n3 59710226 59712726 9 103061696 103064196 Q.79198885 1\\n2 238896326 238898826 7 180405930 180408430 @.91551584 1\\n6 43225786 43228286 8 58523256 58525756 @.41623008 0\\n2 112828 115328 9 8854860 8857360 0.48401883 0\\n1 56612510 56615010 2 123349824 123352324 @.91257286 1\\n1 4743676 4746176 2 29714932 29717432 @.00028278 0\\n2 183204299 183206792 10 29734998 29737498 @.16519122 0\\n5 94957556 94960056 8 106032644 106035144 0.01354055 0\\n1@ 101151746 101154246 1@ 117103810 117106310 0.04067870 0\\n7 86905866 86908366 7 87323180 87325680 @.96810138 1\\n9 157235660 157238160 9 157246142 157248642 @.00085247 0\\n9 97811166 97813666 9 97873470 97875970 @.28599653 @\\n7 36804356 36806856 7 37915560 37918060 @.00084740 0\\n2 201394662 201397162 2 201951932 201954432 @.42819530 0\\n5 146959838 146962338 5 147308546 147311046 @.01540282 0\\n1 138982386 138984886 1 139298800 139301300 0.00286249 0\\n2 228932330 228934830 2 242882804 242885304 @.00006837 20\\n3 169303632 169306132 3 170629794 170632294 @.00313403 20\\n5 173224780 173227280 9 14831228 14833728 @.50576568 1\\n10 103275036 103277536 10 103935806 103938306 0.76628095 1\\n3 60123814 60126314 3 61538358 61540858 @.90330076 1\\n6 92918356 92920856 6 92926284 92928784 @.00082037 0\\n2 164106720 164109220 4 189484316 189486816 0.41017136 0\\n5 180621622 180624122 5 180767554 180770054 @.00002511 20\\n3 94155890 94158390 4 71318030 71320530 @.02578691 0\\n10 106788698 106791198 1@ 123073314 123075814 0@.31578454 @\\n6 22939020 22941520 6 22992588 22995088 @.00065063 @\\n9 24786776 24789276 9 24842666 24845166 @.00202687 20\\n3 45261222 45263722 3 45953012 45955512 @.00176121 @\\n1 121210690 121213199 1 126284536 126287036 0.99455833 1\\n3 124491786 124494286 4 220772328 220774828 @.06378701 2\\n2 138884816 138887316 2 149911138 149913638 @.00028917 20\\n8 173874918 173877418 9 149947670 149950170 @.12730879 0\\n7 61406108 61408608 7 91334040 91336540 @.00237168 @\\n4 201057508 201060008 4 212441548 212444048 @.63278008 1\\n1 259884132 259886632 1 260219904 260222404 0@.00110413 20\\n',\n",
|
||
" \"In [726]: colnames (combined. TCR_p3[[1]])\\n\\n‘parcode': '‘sample': 'TCR1': ‘cdr3_aa1'- ‘cdr3_nti1'- 'TCR2'- ‘cdr3_aa2'- '‘cdr3_nt2'- 'CTgene'- ‘CTnt'- '‘CTaa'- 'CTstrict'\\n\",\n",
|
||
" '40 MB 60 MB 80 MB 100 MB 120 MB 140 MB 160 MB 180 MB\\n\\n20 MB\\n\\nMB\\n\\nIN 09\\n\\naN 08\\n\\nIN OOL\\n\\n',\n",
|
||
" '',\n",
|
||
" 'In [3]: .libPaths(c(\"/usr/local/lib/R/library\", .libPaths()))\\n\\nIn [4]: library(Seurat)\\n\\nError in library(Seurat): there is no package called ‘Seurat’\\nTraceback:\\n\\n1. stop(packageNotFoundError(package, lib.loc, sys.call()))\\n',\n",
|
||
" 'In\\n\\nIn\\n\\n[171]:\\n\\n[172]:\\n\\ntable(combined_seurat$orig. ident)\\n\\npatient3 patient4\\n3160 323\\n\\ncombined_seurat <- combineExpression(\\ncombined_TCR,\\ncombined_seurat,\\ncloneCall = \"strict\",\\nproportion = FALSE\\n)\\n\\nError in combineExpression(combined_TCR, combined_seurat, cloneCall = \"strict\",\\nthere are groupings < 1\\nTraceback:\\n\\n1. stop(\"Adjust the cloneSize parameter - there are groupings < 1\")\\n2. .handleSimpleError(function (cnd)\\n\\nwatcher$capture_plot_and_output()\\ncnd <- sanitize_call(cnd)\\nwatcher$push(cnd)\\nswitch(on_error, continue = invokeRestart(\"eval_continue\"),\\n5 stop = invokeRestart(\"eval_stop\"), error = NULL)\\n. }, \"Adjust the cloneSize parameter - there are groupings < 1\",\\nbase: : quote(combineExpression(combined_TCR, combined_seurat,\\ncloneCall = \"strict\", proportion = FALSE) ))\\n\\n: Adjust the cloneSize parameter —\\n',\n",
|
||
" 'Si Plant Epigenome\\nBrowser\\n\\ny Local tracks\\n\\nBigWig methylome 16 suvr5 CG\\n\\nBigWig methylome 16 suvr5 CHG\\nBigWig methylome 16 suvr5 CHH\\nBigWig methylome merged WT All CG\\nBigWig methylome merged WT All CHG\\nBigWig methylome merged WT All CHH\\nBigWig methylome met1 CG\\n\\nBigWig methylome met1 CHG\\n\\n(_) BigWig methylome met1 CHH\\n\\nJOBOOBO\\n\\n~ Reference sequence\\n\\nC) Reference sequence\\n\\ny Annotation\\n\\nGenes\\nTransposons\\n\\n* pENCODE\\nStrain: Col-0\\ny ATAC-seq\\nCF Leaf ATAC (rep 1)\\n\\nCF Leaf ATAC (rep 2)\\n(2 Leaf ATAC Input\\n\\ny MethylC-seq\\n\\nLeaf Methylation\\n\\n+ ChiP-seq\\n\\nLeaf H2A.Z\\nLeaf H3K4me1\\nLeaf H3K4me3\\nLeaf H3K27me3\\nLeaf H3K36me3\\nLeaf H3K56ac\\nLeaf H3\\n\\nLeaf ChIP Input\\nH3K9me2\\n\\ngo000c00000\\n\\n4\\n=I\\n\\nIRNA-seq\\n\\nLeaf mRNA (rep 1)\\nLeaf mRNA (rep 2)\\n\\n15\\n\\nArabidopsis thaliana\\n\\n(TAIR10) + File\\n\\n2,000,000 4,000,000 6,000,000\\n\\n5,000,000\\n\\npI 62,536\\n\\nView Help\\n\\n8,000,000\\n\\n10,000,000 12,000,000 14,000,000 16,000,000 18,000,000 20,000,000 22,000,000\\n\\nQag Bes\\n\\n10,000,000 15,000,000\\n\\n20,000,000\\n\\n24,000,000\\n\\nGo Share\\n26,000,000\\n\\nGenes (feature density)\\n\\nTransposons (feature density)\\n\\n|\\n\\nLeaf Methylation\\n\\nH3K9me2\\n\\nan aL. Loud a\\n\\nBigWig methylome 16 suvr5 CHG\\n\\nBigWig methylome merged WT All CHG\\n\\nBigWig methylome met1 CHG\\n\\n0.6\\n\\n0.5\\n\\n',\n",
|
||
" '* Glyphosate (Roundup®) resistance; how does glyphosate act? TUT\\n\\ngo\\nHO-CH-CH-CH;O-POs\\noH\\n\\nD-Erythrose-4-P\\nvA “Ss\\nFrom Pentose POs-0\" ‘OH\\n\\nPhosphate Cycle OH\\nShikimate 3-P\\n\\n00H\\nwe t*\\nCHCHCOOH PorSep 2\\nPO;-0\" ‘coo\\n\\nOn\\n3-Enolpyruvy! shikimic acid-5-P (EPSP)\\n\\nCHyCHCOOH HOOC, _CH:CCOOH eCHCOOH\\n— — ed j—-— Co\\n‘0°co0n\\non T\\nArogenate Prephenate Chiorismate\\n\\nIt interferes with the shikimate pathway and thereby inhibits the biosynthesis of aromatic amino acids.\\n',\n",
|
||
" 'STUDY INFORMATION SYSTEM Quick links” Give feedback! @ ENGIEST Q Search for study programme or course\\n\\nAman Shamil Nalakath Usi-io\\nTak General information y | My study information v acd | TALLINNA, —, ®\\n245633LV v\\n\\nMy study information / Student performance records /\\n\\nStudent Code\\nAman Shamil Nalakath 245633LV\\nECTS credits as of 14.01.2025 Grade point average\\n6.00 5.000\\nBy semesters All\\n\\n- 2024/2025 Autumn\\n\\nCourse title Course code ECTS C/E Grade Date Lecturer All right? Remarks\\nBioinformatics II LKGOO50 6.0 E 5 23.12.2024 Airi Rump yes -\\n\\nTotal: 6.0 ECTS GPA: 5.00\\n',\n",
|
||
" 'oped Staats and Stabe\\n\\n(2854 eeplrig sit wainn<2n\\n(2621 Sayin machine ang sit Serer\\noops Ot Aye ont Seats\\nast Shin Caen sro wens\\nscion mini ae ot pom 2 en 8\\n{2 See 2\\n22102 Fond Set Biomass Sk sary\\n22101 Reeewchinmerin ie Ssrce Sp et\\nM8 | ce ee (urs 3:7\\n22015 Comptia aero Sik See\\naM soar Sem (ies 2)\\nonpnisesionsne Spt sain\\n22165 tne Boras sro nem\\nsassy iotictn Sone Bala gpa enn 8\\nNe Cen\\nBEBE science wing Python, MSC Spent (Toes 8:12)\\nsaanybneSren etm & pom Sing a8\\n(BRET Development = Fiera)\\nhing ein ene ont aime\\nBES spleators genomics, Set ion 17)\\n22250 teed ntgeamice Sik wanes\\nsamy | Eoonelogea ti spam Sing Fak\\nEEE surveillance of infectious diseases a (Mon 13:17)\\n22082 iene Sik San\\nBm semen feet (Mon 117)\\n22 Prantl cy sit nanny\\nRt Sonythesectrtenpts Sik Senate\\nlane peace ont ain\\n2180 Guay sit San\\nonc Price GHP)\\nvases | mtn ptarocna spa Soirg 8\\n\\n(Thre 12.47)\\n',\n",
|
||
" 'import cooltools. Lib.plotting\\n\\nvmax = 5000\\nnorm = LogNorm(vmin=1, vmax=100_000)\\nfruitpunch = sns.blend_palette([\\'white\\', \\'red\\'], as_cmap=True)\\n\\nf, axs = plt.subplots(\\nfigsize=(13, 10),\\nnrows=2,\\nncols=2,\\nshare:\\n\\nrue, sharey=True)\\n\\nax = axs[0, 0)\\n\\nax.set_title( ‘Pumpkin Spice\")\\n\\nim = ax.matshow(clr. matrix (balance=False) [\\nplt.colorbar(im, ax=ax ,fraction=0.046, pa\\nplt.xticks (chromstarts, clr. chromnames) ;\\n\\nvmax=vmax,\\n04, labe\\n\\ncmap=\\'fall\\') ;\\ncounts (linear)\\');\\n\\nax = axs[0, 1]\\n\\nax.set_title( ‘Fruit Punch\")\\n\\nim3 = ax.matshow(clr.matrix(balance=False) [:], vmax=vmax, cmap=fruitpunch) ;\\nplt.colorbar(im3, ax=ax, fraction=0.046, pad=0.04, label=\\'counts (linear)\\');\\nplt.xticks(chromstarts, clr. chromnames) ;\\n\\nax = axs[1, 0]\\n\\nim = ax.matshow(clr.matrix(balance=False) [:\\nplt.colorbar(im, ax=ax ,fraction=0.046, pa\\nplt.xticks(chromstarts, clr. chromnames) ;\\n\\n» Norm=norm, cmap=\\'fall\\');\\n04, label=\\'counts (1og)\");\\n\\nax = axs[1, 1]\\n\\nim3 = ax.matshow(clr.matrix(balance=False) [:], norm=norm, cmap=fruitpunch) ;\\nplt.colorbar(im3, ax=ax, fraction=0.046, pad=0.04, label=\\'counts (log)\\');\\nplt.xticks(chromstarts,clr.chromnames) ;\\n\\nplt.tight_layout()\\n13] @ 00s\\n\\nImportError Traceback (most recent call last)\\nFile ~/anaconda3/envs/cool_notebook/Lib/python3. 10/site-packages/cooltools/lib/plotting.py:6\\n5 try:\\n---> 6 from matplotlib.cm import register_cmap\\n7 except ImportError:\\n\\nImportError: cannot import name ‘register_cmap\\' from \\'matplotlib.cm\\' (/usr/users/papantonis1/anaconda3/envs/cool_notebook/lib/python3. 10/site-packages/matp lot Lib/cm. py)\\nDuring handling of the above exception, another exception occurred:\\n\\nModuleNotFoundError Traceback (most recent call last)\\nCell In{3], Line 1\\n\\n----> 1 import cooltools. Lib.plotting\\n\\nvmax = 5000\\n\\nnorm = LogNorm(vmit\\n\\n3\\n4\\n\\nFile ~/anaconda3/envs/cool_notebook/1ib/python3. 10/site-packages/cooltools/Lib/plotting. py:8\\n\\n6& from matplotlib.cm import register_cmap\\n7 except ImportError:\\n----> 8 from matplotlib.colormaps import register\\n\\n10 import matplotlib as mpl\\n11 import matplotlib.pyplot as plt\\n\\nModuleNotFoundError: No module named \\'matplotlib.colormaps\\'\\n',\n",
|
||
" \"ioh\\n\\nR\\n\\ni\\n\\nresponse to selection\\nselection intensity\\n\\n/ genetic variance\\n\\n' heritability\\n\",\n",
|
||
" 'Standardized Variance\\n\\n0.01 0.10 .\\nAverage Expression\\n\\nNon-variable count: 14485,\\nVariable count: 2000\\n\\nStandardized Variance\\n\\ncca\\nHSPMIB\\nHLA-DRA\\nHSPAG\\nKLE4* (GNLY spata\\n4 TYROBP °\\n* PSAP\\n| HLA-DRB1\\n2\\nof\\n\\n0.01 0.10 1.00 10.\\nAverage Expression\\n\\nNon-variable count: 14485,\\nVariable count: 2000\\n\\n',\n",
|
||
" 'Genetic context of bacterial aqpN genes\\n\\n44 AQPNsinKEGG (45% in arsenic resistance operons — 55 % in NO operon)\\n57 AQPNsin NCBI (68% in arsenic resistance operons — 32 % in NO operon)\\nAs(V)\\n\\nAs(II!)\\n',\n",
|
||
" \"apptainer shell --bind $d_path:/home/rstudio /home/aman/scrna_complete.sif\\nid /home/rstudio\\n\\njupyter notebook --ip=0.0.0.@ --port=$port --no-browser --NotebookApp.token=''\\n\",\n",
|
||
" '© aman — nano ./Downloads/assignment/Ecoli_|\\n\\nfi/Ecoli_hifi_genome.gff — 208x63\\n\\nnment/Ecoli_hifi/Ecoli_hifi_genome.gff\\n\\nile: ./Downloads/as\\n\\nWw PICO 5.09\\n\\ni#gff-version 3\\n\\n##sequence-region tig@0000001 1\\n\\ntige0eee0e1\\ntige0eee0e1\\ntige0eee0e1\\ntige0eee0e1\\ntige0eee0e1\\ntige0eee0e1\\ntige0eee0e1\\ntige0eee0e1\\ntige0eee0e1\\ntige0eee0e1\\ntige0eee0e1\\ntige0eee0e1\\ntige0eee0e1\\ntige0eee0e1\\ntige0eee0e1\\ntige0eee0e1\\ntige0eee0e1\\ntige0eee0e1\\ntige0eee0e1\\ntige0eee0e1\\ntige0eee0e1\\ntige0eee0e1\\ntige0eee0e1\\ntige0eee0e1\\ntige0eee0e1\\ntige0eee0e1\\ntige0eee0e1\\ntige0eee0e1\\ntige0eee0e1\\ntige0eee0e1\\ntige0eee0e1\\ntige0eee0e1\\ntige0eee0e1\\ntige0eee0e1\\ntige0eee0e1\\ntige0eee0e1\\ntige0eee0e1\\ntige0eee0e1\\ntige0eee0e1\\ntige0eee0e1\\ntige0eee0e1\\ntige0eee0e1\\ntige0eee0e1\\ntige0eee0e1\\ntige0eee0e1\\ntige0eee0e1\\ntige0eee0e1\\ntige0eee0e1\\ntige0eee0e1\\ntige0eee0e1\\ntige0eee0e1\\ntige0eee0e1\\ntige0eee0e1\\ntige0eee0e1\\ntige0eee0e1\\ntigeeeee0e1\\n\\nWie) Get Help\\nWed Exit\\n\\nProdigal: 002006\\nProdigal: 002006\\nProdigal: 002006\\nProdigal: 002006\\nProdigal: 002006\\nProdigal: 002006\\nProdigal: 002006\\nProdigal: 002006\\nProdigal: 002006\\nProdigal: 002006\\nminced:@.2.0\\n\\nProdigal: 002006\\nProdigal: 002006\\nProdigal: 002006\\nProdigal: 002006\\nProdigal: 002006\\nProdigal: 002006\\nProdigal: 002006\\nProdigal: 002006\\nProdigal: 002006\\nProdigal: 002006\\nProdigal: 002006\\nProdigal: 002006\\nProdigal: 002006\\nProdigal: 002006\\nProdigal: 002006\\nProdigal: 002006\\nProdigal: 002006\\nProdigal: 002006\\nProdigal: 002006\\nProdigal: 002006\\nProdigal: 002006\\nProdigal: 002006\\nProdigal: 002006\\nProdigal: 002006\\nProdigal: 002006\\nProdigal: 002006\\nProdigal: 002006\\nProdigal: 002006\\nProdigal: 002006\\nProdigal: 002006\\nProdigal: 002006\\nProdigal: 002006\\nProdigal: 002006\\nProdigal: 002006\\nProdigal: 002006\\nProdigal: 002006\\nProdigal: 002006\\nProdigal: 002006\\nProdigal: 002006\\nProdigal: 002006\\nProdigal: 002006\\nProdigal: 002006\\nProdigal: 002006\\nProdigal: 002006\\nProdigal: 002006\\n\\n465\\ncDS\\ncDS\\ncDS\\ncDS\\ncDS\\ncDS\\ncDS\\ncDS\\ncDS\\ncDS\\nCRI\\ncDS\\ncDS\\ncDS\\ncDS\\ncDS\\ncDS\\ncDS\\ncDS\\ncDS\\ncDS\\ncDS\\ncDS\\ncDS\\ncDS\\ncDS\\ncDS\\ncDS\\ncDS\\ncDS\\ncDS\\ncDS\\ncDS\\ncDS\\ncDS\\ncDS\\ncDS\\ncDS\\ncDS\\ncDS\\ncDS\\ncDS\\ncDS\\ncDS\\ncDS\\ncDS\\ncDS\\ncDS\\ncDS\\ncDS\\ncDS\\ncDS\\ncDS\\ncDS\\ncDS\\ncDS\\n\\n7533\\n\\n99\\n1718\\n2811\\n5892\\n7393\\n7888\\n8982\\n9643\\n10258\\n11177\\n11567\\n12412\\n13701\\n14611\\n16038\\n16693\\n17210\\n17540\\n18250\\n18726\\n19756\\n20511\\n21277\\n22479\\n23565\\n25018\\n25799\\n26529\\n26863\\n27693\\n28797\\n29615\\n30377\\n33014\\n33225\\n33615\\n35767\\n36774\\n37895\\n38158\\n39034\\n39596\\n40182\\n40790\\n42619\\n43560\\n45279\\n45821\\n46585\\n46988\\n47617\\n49050\\n50764\\n51926\\n52606\\n54986\\n\\nSPR\\n\\nWe) WriteOut\\nWe) Justify\\n\\n1643\\n\\n2452\\n\\n5477\\n\\n7400\\n\\n7875\\n\\n8979\\n\\n9656\\n\\n10242\\n11175\\n11461\\n12329\\n13449\\n14609\\n16038\\n16643\\n17016\\n17521\\n18250\\n18729\\n19775\\n20517\\n21137\\n22416\\n23471\\n24929\\n25794\\n26437\\n26900\\n27696\\n28601\\n29564\\n30271\\n32938\\n33148\\n33578\\n35693\\n36777\\n37895\\n38167\\n39030\\n39384\\n40057\\n40793\\n42616\\n43542\\n45269\\n45821\\n46588\\n46995\\n47458\\n49041\\n50507\\n51777\\n52453\\n54858\\n56119\\n\\ntet et eteetse\\n\\ntet et etetetsei\\n\\ni\\n\\ntet etetesti\\n\\n++H1\\n\\nF\\n\\nSPBV2VVDVVVOVVO\\n\\nPBYWDVDDWDD WDD VDD DD VDD VV VDD VDDD DVD VDDVDDVDDVDVDVDVDVDVDVDVDVDVVVVVVOVOQ:\\n\\nWii Read File\\nWil) Where is\\n\\nID=KBOCNLJJ_00001; eC_number=1.8.1.2;Name=cysI_1;db_xref=COG:C0G0155; gene=cysI_1;inference=ab initio prediction:Prodigal:002006,$\\nID=KBOCNLJJ_00002; eC_number=1.8.4.8;Name=cysH_1;db_xref=COG:C0G0175; gene=cysH_1;inference=ab initio prediction:Prodigal:002006,$\\nID=KBOCNLJJ_00003; eC_number=3.1.-.-—;Name=ygcB_1;db_xref=COG:C0G1203; gene=ygcB_1;inference=ab initio prediction:Prodigal:002006,$\\nID=KBOCNLJJ_00004;Name=casA_1;gene=casA_1;inference=ab initio prediction:Prodigal:002006,similar to AA sequence:UniProtKB:Q4690$\\nID=KBOCNLJJ_@0005 ; Name=casB_1;gene=casB_1;inference=ab initio prediction:Prodigal:002006,similar to AA sequence:UniProtKB:P7663$\\nID=KBOCNLJJ_00006;Name=casC_1;gene=casC_1;inference=ab initio prediction:Prodigal:002006,similar to AA sequence:UniProtKB:Q4689$\\nID=KBOCNLJJ_@0007 ; Name=casD_1;gene=casD_1;inference=ab initio prediction:Prodigal:002006,similar to AA sequence:UniProtKB:Q4689$\\nID=KBOCNLJJ_00008; eC_number=3.1. j;Name=casE_1;gene=casE_1;inference=ab initio prediction:Prodigal:002006,similar to AA sequen$\\nID=KBOCNLIJJ_00009; eC_number=3.1. j;Name=ygbT_1;db_xref=COG:C0G1518; gene=ygbT_1;inference=ab initio prediction:Prodigal:002006,$\\nID=KBOCNLJJ_00010; eC_number=3.1.-.-—;Name=ygbF_1;gene=ygbF_1;inference=ab initio prediction:Prodigal:002006,similar to AA sequen$\\nnote=CRISPR with 13 repeat units;rpt_family=CRISPR;rpt_type=direct\\n\\nID=KBOCNLJJ_00011;inference=ab initio prediction: Prodigal : 002006; locus_tag=KBOCNLJJ_00011;product=hypothetical protein\\nID=KBOCNLJJ_00012; eC_number=2.7.7.4;Name=cysD_1;db_xref=COG:C0G0175; gene=cysD_1;inference=ab initio prediction:Prodigal:002006,$\\nID=KBOCNLJJ_00013; eC_number=2.7.7.4;Name=cysN; db_xref=COG:C0G2895; gene=cysN;inference=ab initio prediction:Prodigal: 002006, simi$\\nID=KBOCNLJJ_00014; eC_number=2.7.1.25;Name=cysC; db_xref=COG:C0G@529; gene=cysC;inference=ab initio prediction:Prodigal: 002006, sim$\\nID=KBOCNLJJ_00015 ; Name=ygbE; gene=ygbE;inference=ab initio prediction:Prodigal:002006,similar to AA sequence:UniProtKB:P46141;1lo$\\nID=KBOCNLJJ_00016;Name=ftsB; db_xref=COG:C0G2919; gene=ftsB;inference=ab initio prediction:Prodigal:002006,similar to AA sequence$\\nID=KBOCNLJJ_00017; eC_number=2.7.7.60;Name=ispD; db_xref=COG:C0G1211; gene=ispD;inference=ab initio prediction:Prodigal: 002006, sim$\\nID=KBOCNLJJ_00018; eC_number=4.6.1.12;Name=ispF; db_xref=COG:C0G@245; gene=ispF;inference=ab initio prediction:Prodigal: 002006, sim$\\nID=KBOCNLJJ_00019; eC_number=5.4.99.27;Name=truD; db_xref=COG:C0G0585; gene=truD; inference=ab initio prediction:Prodigal:002006,si$\\nID=KBOCNLJJ_00020; eC_number=3.1.3.5;Name=surE; db_xref=COG:C0G0496; gene=surE;inference=ab initio prediction:Prodigal: 002006, simi$\\nID=KBOCNLJJ_00021; eC_number=2.1.1.77;Name=pcm; db_xref=COG:C0G2518; gene=pcm; inference=ab initio prediction:Prodigal: 002006, simil$\\nID=KBOCNLJJ_00022;Name=n1pD_1; db_xref=COG:C0G@739; gene=nlpD_1;inference=ab initio prediction:Prodigal:002006,similar to AA sequ$\\nID=KBOCNLJJ_00023;Name=rpoS; db_xref=COG:C0G0568; gene=rpoS;inference=ab initio prediction:Prodigal:002006,similar to AA sequence$\\nID=KBOCNLJJ_00024;Name=ygbN; db_xref=COG:C0G2610;gene=ygbN; inference=ab initio prediction:Prodigal:002006,similar to AA sequence$\\nID=KBOCNLJJ_00025; eC_number=5.3.1.35;Name=otnI; db_xref=COG:C0G3622; gene=otnI;inference=ab initio prediction:Prodigal: 002006, sim$\\nID=KBOCNLJJ_00026; eC_number=4.1.1.104;Name=otnC;gene=otnC;inference=ab initio prediction:Prodigal:002006,similar to AA sequence$\\nID=KBOCNLJJ_0@0027; eC_number=2.7.1.217;Name=otnK_1;db_xref=COG:C0G3395; gene=otnK_1;inference=ab initio prediction:Prodigal:00200$\\nID=KBOCNLJJ_00028; eC_number=2.7.1.217;Name=otnK_2;db_xref=COG:C0G3395; gene=otnK_2;inference=ab initio prediction:Prodigal:00200$\\nID=KBOCNLJJ_00029; eC_number=1.1.1.411;Name=1tnD; gene=1tnD;inference=ab initio prediction:Prodigal:002006,similar to AA sequence$\\nID=KBOCNLJJ_00030;Name=g1lcR;db_xref=COG:C0G1349; gene=glcR;inference=ab initio prediction:Prodigal:002006,similar to AA sequence$\\nID=KBOCNLJJ_00031; eC_number=3.1.3.16;Name=pphB; db_xref=COG:C0G@639; gene=pphB; inference=ab initio prediction:Prodigal: 002006, sim$\\nID=KBOCNLJJ_00032;Name=mutS;db_xref=COG:C0G0249; gene=mutS;inference=ab initio prediction:Prodigal:002006,similar to AA sequence$\\nID=KBOCNLJJ_00033;inference=ab initio prediction: Prodigal: 002006; locus_tag=KBOCNLJJ_00033;product=hypothetical protein\\nID=KBOCNLJJ_00034;inference=ab initio prediction: Prodigal : 002006; locus_tag=KBOCNLJJ_00034;product=hypothetical protein\\nID=KBOCNLJJ_00035 ; Name=fh1A; db_xref=COG:C0G3604;gene=fhlA;inference=ab initio prediction:Prodigal:002006,similar to AA sequence$\\nID=KBOCNLIJJ_00036; eC_number=4.2.1.—;Name=hypE; db_xref=COG:C0G@309; gene=hypE;inference=ab initio prediction:Prodigal: 002006, simi$\\nID=KBOCNLJJ_00037 ; Name=hypD; db_xref=COG:C0G0409; gene=hypD; inference=ab initio prediction:Prodigal:002006,similar to AA sequence$\\nID=KBOCNLJJ_00038; Name=hypC; db_xref=COG:C0G0298; gene=hypC;inference=ab initio prediction:Prodigal:002006,similar to AA sequence$\\nID=KBOCNLIJJ_00039 ; Name=hypB; db_xref=COG:C0G0378; gene=hypB; inference=ab initio prediction:Prodigal:002006,similar to AA sequence$\\nID=KBOCNLJJ_@0040 ; Name=hypA; db_xref=COG:C0G0375; gene=hypA;inference=ab initio prediction:Prodigal:002006,similar to AA sequence$\\nID=KBOCNLJJ_@0041;Name=hycA;gene=hycA;inference=ab initio prediction:Prodigal:002006,similar to AA sequence:UniProtKB:P@AEV4; 1lo$\\nID=KBOCNLJJ_00042; eC_number=1.-. j;Name=hyfA_1; db_xref=COG:C0G1142; gene=hyfA_1;inference=ab initio prediction:Prodigal:002006,$\\nID=KBOCNLJJ_00043; eC_number=7.1.1.—;Name=ndhB_1;gene=ndhB_1;inference=ab initio prediction:Prodigal:002006,protein motif :HAMAP:$\\nID=KBOCNLJJ_00044;Name=hycD; db_xref=COG:C0G0650;gene=hycD;inference=ab initio prediction:Prodigal:002006,similar to AA sequence$\\nID=KBOCNLJJ_@0045 ; Name=hycE; db_xref=COG:C0G3261; gene=hycE;inference=ab initio prediction:Prodigal:002006,similar to AA sequence$\\nID=KBOCNLJJ_00046; eC_number=7.1.1.—;Name=ndhI_1;gene=ndhI_1;inference=ab initio prediction:Prodigal:002006,protein motif :HAMAP:$\\nID=KBOCNLJJ_00047 ; Name=hycG_1; db_xref=COG:C0G3260; gene=hycG_1;inference=ab initio prediction:Prodigal:002006,similar to AA sequ$\\nID=KBOCNLJJ_00048;inference=ab initio prediction: Prodigal: 002006; locus_tag=KBOCNLJJ_00048;product=hypothetical protein\\nID=KBOCNLJJ_0@0049; eC_number=3.4.23.51;Name=hycI ;db_xref=COG:C0G0680;gene=hycI;inference=ab initio prediction:Prodigal:002006,si$\\nID=KBOCNLJJ_@0050; eC_number=3.2.1.86;Name=bg1H_1;db_xref=COG:C0G2723; gene=bg1H_1;inference=ab initio prediction:Prodigal:002006$\\nID=KBOCNLJJ_00051; Name=bg1F_1;db_xref=COG:C0G1263; gene=bg1F_1;inference=ab initio prediction:Prodigal:002006,similar to AA sequ$\\nID=KBOCNLJJ_00052;Name=ascG; db_xref=COG:C0G1609; gene=ascG; inference=ab initio prediction:Prodigal:002006,similar to AA sequence$\\nID=KBOCNLJJ_00053; eC_number=1.-.-.-—;Name=hyfA_2;db_xref=COG:C0G1142;gene=hyfA_2;inference=ab initio prediction:Prodigal:002006,$\\nID=KBOCNLJJ_00054; eC_number=6.2.-—.—;Name=hypF; db_xref=COG:C0G@068; gene=hypF;inference=ab initio prediction:Prodigal: 002006, simi$\\nID=KBOCNLJJ_00055; eC_number=1.18.1.-—;Name=norw; db_xref=COG:C0G1251; gene=norW; inference=ab initio prediction:Prodigal: 002006, sim$\\n\\nbad Prev Pg Wag Cut Text wie Cur Pos\\nWA) Next Pg wig) UnCut Text Way To Spell\\n\\n',\n",
|
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" 'i al\\n\\nLeaf Hi-C K4me3 HiChIP K27me3 HiChIP\\n\\neQTL-gene\\nlinks >20 kb |\\n\\nshuffled pairs\\n\\n',\n",
|
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" 'In [725]: sce <- combineExpression(\\ncombined. TCR_p3,\\npatient3_transform,\\ncloneCall = \"gene\",\\ngroup.by = “orig. ident\",\\nproportion = TRUE\\n\\n)\\n\\nError in ‘[.data.frame* (data, , c(\"barcode\", cloneCall, group.by)): undefined columns selected\\n\\nTraceback:\\n\\n1. na.omit(unique(data[, c(\"barcode\", cloneCall, group.by)]))\\n2. unique(data[, c(\"barcode\", cloneCall, group.by)])\\n\\n3. data[, c(\"barcode\", cloneCall, group.by)]\\n\\n4. ~[.data.frame* (data, , c(\"barcode\", cloneCall, group.by) )\\n5. stop(\"undefined columns selected\")\\n\\n6. .handleSimpleError(function (cnd)\\n\\n~{\\n\\n. watcher$capture_plot_and_output ()\\n\\ncnd <— sanitize_call(cnd)\\n\\nwatcher$push(cnd)\\n\\nswitch(on_error, continue = invokeRestart(\"eval_continue\"),\\n\\n. stop = invokeRestart(\"eval_stop\"), error = NULL)\\n\\n. }, “undefined columns selected\", base::quote(*[.data.frame* (data,\\n, c(\"barcode\", cloneCall, group.by))))\\n',\n",
|
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" '2.3.2 Overivew of 3C methods\\n\\nCrosslinking\\nchromatin\\n\\nSe\\n\\n™\\n\\nSonicate rx\\nY Nee\\nat Reverse PCR\\nIP crosslinking meacioe\\nv junction so < a0\\n— ———> PCR , ,\\naad - one versus one\\nBiotinylate Sonicate\\nReverse ; Ligate\\ncrosslinking y sequencing Hybridization/\\n“wv adapters pull down\\nLigate | — —___ >» —-» pcr ; Capture-C :\\nv oligos manyversus all\\n— -— 2nd\\ndigest\\nA Amplify ligated\\ndapters Biotine * MANE “x\\nPCR v —= oligos ~~ 5c\\nan 7 Microarray/ 7\\n—_— —_ — —_, many\\n>< — sequencing ,\\nversus many\\nVv Ligate\\nSequencing Vv\\nChIA-PET PCR from\\n‘many wp Ligate bait Bait |\\nversus many’ Reverse —_— > > —— Microarray/ i ac 1\\ncrosslinking sequencing ‘one versus all\\nsonicate\\noa _—\\n~~ iin - > Sequencing versus all\\nStreptavidin Ligate Amplification\\npurification sequencing\\nadapters\\n\\nRE digest\\ndilute\\n\\nTrends in Plant Science\\n',\n",
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" '@ Vivaldi File Edit View Bookmarks Mail Tools Window Help Of sa - es © € C) Ff Q ® SatMar 22 22:10\\n\\nee (ab) = > QO Y localhost:8785/graphics/plot_zoom 55 NW o& _ ne LOM IBattere\\n\\nYour Mac will sleep soon unless plugged into\\na power outlet.\\n\\nSpeed Dial Y Imported From... Y Imported From... Y Online Bewerbung QGIS API Docume... qgis - Trying to pe... New Script - Earth... Pastebin.com - #1... TargetP 2.0 - .. https://www. >=\\n\\npercent.mt\\n\\n[i zehn_dataset\\n\\n208 @2afAaxnea\\n\\n<a)\\n\\nN N\\n‘ g s” g\\nSS a? Q\\nIdentity\\n\\nSs seurat_in3_vign... v Analysis, visualization, an: *K Troubleshooting Single-C 3] Pastebin.com - #1 paste t R_ RStudio Server ChatGPT - zehn_internshi) -+ @& Ww\\n\\nSe\\n\\nS 0 @ 8 Oo —O—— 100% -22:10\\n',\n",
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" 'afari ile it iew istory ookmarks evelop indow lelp x Ss ©) p rs u 13. Nov :\\nSafari Fil Edi Vi Hi Bookmark Devel Wind Hel 4 © ©) F Q ®@ Thw13.Nov 11:17\\na >\\n\\nees o&-< COoW@ése github.com Sac © wm +\\n\\nG9 5. simulet... G Normaiiz... 9 search —... 69 omics ov... @& citHun -... S Whatis t... © ima tuto... @ cimve tu... GEMs -... BD) Guideline... © cosrattut... 9 10. consi... 63 171.10. c...\\n\\nrw) Platform Solutions Resources Open Source Enterprise Pricing Search or jump to... Sign in Sign up\\n\\n& opencobra/COBRA.tutorials Public Q Notifications Y Fork 57 W Star 23\\n<> Code © Issues 3 3 Pullrequests ©) Actions © Security [¥ Insights\\n{]) Files COBRA.tutorials / datalntegration / extractionTranscriptomic / (QO\\n\\nP master y (a github-actions[bot] created .HTML, .pdf and .m files €29c0c1-10 months ago ©) History\\n\\nLast commit message Last commit d...\\ndatalntegration/extractionTranscriptomic/options_methods\\n\\n[ dataintegration/extractionTranscriptomic/options_methods/options_MBA.mat\\n[) datalntegration/extractionTranscriptomic/options_methods/options_INIT.mat Updated tuorial data 7 years ago\\n[ datalntegration/extractionTranscriptomic/options_methods/options_iMAT.mat merge remote version 4 years ago\\n(} dataintegration/extractionTranscriptomic/options_methods/options_GIMME.mat . . .\\n\\nMoving tutorials to datalntegration 8 years ago\\n( dataintegration/extractionTranscriptomic/options_methods/options_mCADRE.mat\\n\\ncreated .HTML, .pdf and .m files 10 months ago\\n[ datalntegration/extractionTranscriptomic/options_methods/options_fastCore.mat p 9\\n® analysis/alternateOptimalSolutions created .HTML, .pdf and .m files 10 months ago\\n\\n| [) analysis/alternateOptimalSolutions/README.md Updated further tutorials not to update the toolbox 7 years ago\\n\\nD) analysis/alternateOptimalSolutions/tutorial_alternateOptimalSolutions.m\\n\\ncreated .HTML, .pdf and .m files 10 months ago\\n\\nD analysis/alternateOptimalSolutions/practical_alternateOptimalSolutions.m\\nD) analysis/alternateOptimalSolutions/tutorial_alternateOptimalSolutions.mIx\\nD analysis/alternateOptimalSolutions/tutorial_alternateOptimalSolutions.pdf\\n\\nD) analysis/alternateOptimalSolutions/practical_alternateOptimalSolutions.mIx\\n\\n[} analysis/alternateOptimalSolutions/practical_alternateOptimalSolutions.pdf\\n[) reconstruction/reconstructionSOP/tutorial_reconstructionSOP.m\\n\\n( reconstruction/reconstructionSOP/tutorial_reconstructionSOP.mlx\\n\\n[) reconstruction/reconstructionSOP/tutorial_reconstructionSOP.pdf\\n\\n( reconstruction/reconstructionSOP/tutorial_reconstructionSOP.html\\n\\nD) analysis/nutritionToolbox/inputData/demoFoodDescription.xisx\\nPY analveie/nutritinnTaolhay/innutNataldamaFaadNeccrintianFmntv ylex\\n\\n(9 README.md\\n> ® design\\n> MW doc\\n\\n. > (& reconstruction y\\n',\n",
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" 'trimmomatic PE \\\\\\n$R1 $R2 \\\\\\n$OUT_PAIRED_R1 $0UT_UNPAIRED_R1 \\\\\\n$OUT_PAIRED_R2 $OUT_UNPAIRED_R2 \\\\\\nILLUMINACLIP: TruSeq3-PE. fa:2:30:10:2:True \\\\\\nLEADING:5 \\\\\\nTRAILING:5 \\\\\\nSLIDINGWINDOW: 4:20 \\\\\\nMINLEN: 30\\n',\n",
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" 'Slide 1 Explanation (tRNA charging and structure)\\n\\n1. tRNA Structure: The slide shows a transfer RNA (tRNA) molecule, which has a characteristic\\n“cloverleat™ secondary structure. Key regions include:\\n‘+ Anticodon loop: This loop carries the anticodon that pairs with a specific codon on the\\n\\nmessenger RNA (mRNA).\\n\\n+ 3! acceptor stem: The amino acid is covalently attached to the 3 end (the CCA sequence)\\nof the tRNA.\\n\\n2. Aminoacyl-tRNA Synthetases: Each tRNA is ‘charged” with its corresponding amino acid by a\\n\\nspecific enzyme called an aminoacyl-tRNA synthetase. This enzyme:\\n+ Recognizes both the anticodon (on the tRNA) and the correct amino acid,\\n+ Catalyzes the attachment of the amino acid to the tRNA’s 3! end.\\n\\n3. Ensuring Fidelity: The matching of a tRNA\\'s anticodon to its amino aci\\nprotein synthe:\\n\\nis crucial for accurate\\n\\nIfa tRNA is charged with the wrong amino aci\\nincorporated into the protein\\n\\n, the wrong amino acid could be\\n\\nOverall, Slide 1 emphasizes how each tRNA molecule is “charged” or \"aminoacylated” in a highly\\nspecific manner, ensuring that when the tRNA’s anticodon base-pairs with the mRNA codon, the\\ncorrect amino acid is added to the growing polypeptide chain.\\n\\nSlide 2 Explanation (Protein Synthesis at the Ribosome)\\n1\\n\\nn: The process of translation begins at the start codon (AUG) on the mRNA, which codes\\nfor methio\\n\\n2,\\n2. Ribosome Sites: The ribosome has three sites where tRNAs can bind:\\n+ A (aminoacyl) site: Where the incoming charged tRNA (with its amino acid) first arrives.\\n\\n+P (peptidyl) site: Holds the tRNA carrying the growing peptide chain\\n\\n+ EC : Where the empty tRNA (after it has passe:\\nthe ribosome.\\n\\namino acid to the chain) leaves\\n\\n3. Elongation:\\n‘+ The ribosome moves along the mRNA in a 5\\'->3\\' direction.\\n+ Ateach codon, a charged tRNA enters the A site.\\n\\n‘+ The amino acid from the tRNA in the P site is transferred to the amino acid on the tRNA in\\nthe A site, lengthening the peptide chain.\\n\\n‘+ The ribosome shifts so the new peptidyl-tRNA moves to the P site, and the empty tRNA exits\\nfrom the E site.\\n\\n4. Termination: The process continues until a stop codon (e.g., UGA, UAA, or UAG) is reached. At\\nthat point, the completed polypeptide is released from the ri\\n\\nOverall, Slide 2 describes the mechanics of translation—how the ribosome reads the mRNA, recruits\\nthe correctly charged tRNAs, and synthesizes a polypeptide by forming peptide bonds between\\nsuccessive amino acids until a stop codon is encountered.\\n',\n",
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" 'Arabidopsis thaliana (TAIR10) + File View Help co Share\\n\\n-\\ngs Plant Ep igen ome 2,000,000 4,000,000 6,000,000 8,000,000 10,000,000 12,000,000 14,000,000 16,000,000 18,000,000 20,000,000 22,000,000 24,000,000 26,000,000\\nBrowser So Qaaa EIEN\\n\\n5,000,000 10,000,000 15,000,000 20,000,000\\n\\n* Local tracks 9 f 70 4\\nGenes (feature density)\\n\\nBigWig methylome 16 suvr5 CG\\n\\nBigWig methylome 16 suvr5 CHG\\nBigWig methylome 16 suvr5 CHH\\nBigWig methylome merged WT All CG\\nBigWig methylome merged WT All CHG\\nBigWig methylome merged WT All CHH\\nBigWig methylome met1 CG ;\\n\\nC) BigWig methylome met1 CHG Transposons (feature density) z|\\n(_) BigWig methylome met1 CHH\\n\\n~ Reference sequence 1\\n\\nCD) Reference sequence |\\n\\n* Annotation 2 Leaf Methylation\\n\\nGenes\\nTransposons\\n\\n* pENCODE 15\\nStrain: Col-0 H3K9me2\\ny ATAC-seq 3\\n\\nro aL Lou a ta lu! dot dd th be Lat pe\\n\\nC) Leaf ATAC (rep 1)\\nC) Leaf ATAC (rep 2)\\n(2 Leaf ATAC Input\\n\\n+ MethylC-seq 1\\n\\nLeaf Methylation\\n\\n+ ChIP-seq 9\\n\\nLeaf H2A.Z\\nLeaf H3K4me1\\nLeaf H3K4me3\\nLeaf H3K27me3\\nLeaf H3K36me3\\nLeaf H3K56ac\\n\\nH3K9me2\\n\\n4\\n=I\\n\\nIRNA-seq 2\\n\\nLeaf mRNA (rep 1)\\nLeaf mRNA (rep 2)\\n\\n',\n",
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" '93,000 KB 92,000 KB 91,000 KB 90,000 KB 89,000 KB 88,000 KB\\n\\n94,000 KB\\n\\n85 MB\\n\\n',\n",
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" 'ML classification model\\n\\nML regression model\\n\\nATAC QC\\n\\nATAC peak detection\\n\\n7 ,\\nGitHub\\n8\\nNextflow for ML\\n\\n',\n",
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" '',\n",
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" 'popgen_internship doc &\\n\\nFile Edit View Insert Format\\n\\nQ 5 @ BA F 100%\\n\\nas\\n\\nDocument tabs +\\n\\n|) Tab1\\n\\nHeadings you add to the document\\nwill appear here.\\n\\nv\\n\\nTools\\n\\nExtensions Zotero Help\\n\\nNormal text > Arial y= + BIrUA Fi @ G2\\n\\n1\\nbrit\\n\\nnon SAn nnn en i en i en i nn\\n\\nEv t= Yor iv\\n\\nmonn enon Anon nnn\\n\\nWhat are my next steps?\\n| did admixture\\nBut was not able to run structure\\nNext -\\nThis was what | wrote down before -\\nMajor: 1. Calculate L.D\\n2. Get the Matrix\\n\\n3. Plot the Matrix\\n\\nMinor: 1. Calc. theta pi, theta watterson, Tajima’s D and FST\\n',\n",
|
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" \"fruitsalad.txt\\n[amnala@base ~]$ cat fruitsalad.txt\\napple\\n\\norange\\n\\nbanana\\n\\npear\\n\\ngrapes\\nwatermelon\\napple\\n\\nkiwi\\n\\nbanana\\n\\ngrapes\\n\\npapaya\\n\\nmango\\n\\n[amnala@base ~]$ cat fruitsalad.txt | sort | uniq -u > fruitsalad_cleaned.txt\\n[amnala@base ~]$ ls\\n\\nfruitsalad.txt fruitsalad_cleaned.txt\\n\\n[amnala@base ~]$ cat fruitsalad_cleaned.txt\\n\\nkiwi\\nmango\\norange\\npapaya\\npear\\nwatermelon\\n[Lamnala@base ~]$ we —-h\\nwe: invalid option -- 'h'\\nTry 'wc --help' for more information.\\n[amnala@base ~]$ we --help\\nUsage: we [OPTION]... [FILE]...\\nor: we [OPTION]... --files@-from=F\\nPrint newline, word, and byte counts for each FILE, and a total line if\\nmore than one FILE is specified. A word is a non-zero-length sequence of\\ncharacters delimited by white space.\\n\\nWith no FILE, or when FILE is -, read standard input.\\n\\nThe options below may be used to select which counts are printed, always in\\nthe following order: newline, word, character, byte, maximum line length.\\n\\n-c, —-bytes print the byte counts\\n-m, --chars print the character counts\\n-1, --lines print the newline counts\\n--files®-from=F read input from the files specified by\\nNUL-terminated names in file F; i\\nIf F is - then read names from standard input\\n-L, --max-line-length print the maximum display width\\n-w, --words print the word counts\\n--help display this help and exit\\n\\n--version output version information and exit\\n\\nGNU coreutils online help: <https://www.gnu.org/software/coreutils/>\\nReport wc translation bugs to <https://translationproject.org/team/>\\nFull documentation at: <https://www.gnu.org/software/coreutils/we>\\nor available locally via: info '(coreutils) we invocation’\\n[amnala@base ~]$ wc -1 fruitsalad_cleaned.txt\\n\\n7 fruitsalad_cleaned.txt\\n\\n[amnala@base ~]$ cat fruitsalad_cleaned.txt\\n\\nkiwi\\n\\nmango\\norange\\npapaya\\npear\\nwatermelon\\n\",\n",
|
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" '[1]:\\n\\nimport h5py\\n\\n# Open the HDF5 file\\n\\nwith h5py.File(\\'cool_pileup_combined\\', \\'r\\') as f:\\n# Inspect the structure\\nprint(\"Keys:\", list(f.keys()))\\n\\n# Check the \\'data\\' dataset\\n\\ndata = f[\\'data\\'][:]\\n\\nprint(f\"\\'data\\' dataset shape: {data.shape}\")\\nprint(f\"\\'data\\' dataset contents:\\\\n{data}\")\\n\\nKeys: [\\'annotation\\', ‘attrs\\', ‘data\\']\\n‘data\\' dataset shape: (16488, 3)\\n‘data\\' dataset contents:\\n[[1.1873085 1.2874519 1.4797186]\\n[1. 7349982 2.228282 3.1729212]\\n[1.5040904 1.3009566 1.1008095]\\n[1.9000989 2.8981235 1.9658103]\\n[2.9235291 4.7604017 2.8729181]\\n[1.9822323 2.930699 1.9129672]]\\n\\n',\n",
|
||
" 'pd_coordinates pd_coordinates_norm area_rescaled volume volume_rescaled max/min max/min_rescaled max/mid max/mid_rescaled\\n\\npd_coordi inates_norrivol jume_rescaled\\n\\n1 0.841457173 2Vv 8 84.3282 1 262.645 0.904082476 315.639 0.841457173 1.80838 1.109435583 1.25577 0.988795276\\n0.472590426 1.499869372 2Vv 40 39.8527 0.472590426 374.662 1.289669891 562.616 1.499869372 1.35778 0.832993865 1.16018 0.913527559\\n0.789414454 1.46860121 2Vv 2 66.5699 0.789414454 401.555 1.382241575 550.887 1.46860121 2.00733 1.231490798 1.14868 0.904472441\\n\\n0.05007459 0.461003439 2Vv 382 4.2227 0.05007459 176.226 0.606609067 172.927 0.461003439 1.51603 0.930079755 1.16938 0.920771654\\n\\n0 0.44105729 2Vv 409 0 0 169.666 0.584028089 165.445 0.44105729 1.36474 0.837263804 1.08816 0.856818898\\n\\n0.271726421 0.889141852 2Vv 213 22.9142 0.271726421 273.522 0.941523528 333.526 0.889141852 1.64009 1.006190184 1.13433 0.893173228\\n0.90153116 1.42995388 2Vv 3 76.0245 0.90153116 384.165 1.322381329 536.39 1.42995388 1.84093 1.129404908 1.19379 0.939992126\\n0.543521621 1.539721682 2Vv 19 45.8342 0.543521621 430.08 1.480430966 577.565 1.539721682 2.23972 1.37406135 1.50357 1.183913386\\n0.332528146 0.931969289 2Vv 147 28.0415 0.332528146 284.051 0.977766686 349.591 0.931969289 1.66962 1.024306748 1.21528 0.956913386\\n0.668220121 1.56593266 2Vv 7 56.3498 0.668220121 408.191 1.405084162 587.397 1.56593266 1.85829 1.140055215 1.21369 0.955661417\\n0.39442559 0.881672043 2Vv 103 33.2612 0.39442559 269.808 0.928739114 330.724 0.881672043 1.58082 0.969828221 1.08492 0.854267717\\n0.120110473 0.963218789 2Vv 343 10.1287 0.120110473 273.945 0.942979588 361.313 0.963218789 1.20411 0.738717791 1.12562 0.886314961\\n0.201292094 0.982637093 2Vv 287 16.9746 0.201292094 271.203 0.933541014 368.597 0.982637093 1.24843 0.765907975 1.05004 0.82680315\\n0.543163497 1.341121271 0 0 0 0 0\\n\\n0 0.822401429 2Vv 422 240 45.804 0.543163497 346.13 1.191456404 503.068 1.341121271 1.3022 0.798895706 1.14935 0.905\\n0.238053225 0.795718589 2Vv 422 532 0 0 251.701 0.866410795 308.491 0.822401429 1.21767 0.74703681 1.16556 0.91776378\\n0.391559407 1.140641412 2Vv 422 468 20.0746 0.238053225 244.672 0.842215414 298.482 0.795718589 1.4731 0.903742331 1.19905 0.944133858\\n\\n0.92619195 0.732483805 2Vv 422 380 33.0195 0.391559407 306.413 1.054741661 427.866 1.140641412 1.23104 0.755239264 1.13944 0.89719685\\n0.312270391 0.903972168 2Vv 422 18 78.1041 0.92619195 233.945 0.805290696 274.762 0.732483805 1.82777 1.121331288 1.54869 1.219440945\\n0.165343266 0.692503532 2Vv 422 434 26.3332 0.312270391 265.749 0.914767134 339.089 0.903972168 1.20692 0.740441718 1.11939 0.881409449\\n0.755965383 1.29199968 2Vv 422 505 13.9431 0.165343266 222.65 0.766410795 259.765 0.692503532 1.27228 0.780539877 1.21059 0.953220472\\n0.085304679 0.852176695 2Vv 422 61 63.7492 0.755965383 359.234 1.236563285 484.642 1.29199968 1.74417 1.070042945 1.24457 0.979976378\\n0.862897583 0.831078884 2Vv 422 531 7.19359 0.085304679 250.32 0.861657086 319.66 0.852176695 1.20158 0.737165644 1.0443 0.822283465\\n0.640002988 1.208034976 2Vv 422 23 72.7666 0.862897583 264.404 0.910137345 311.746 0.831078884 1.83364 1.124932515 1.61684 1.273102362\\n0.465993582 0.704657301 2Vv 422 151 53.9703 0.640002988 335.284 1.154122061 453.146 1.208034976 1.48102 0.908601227 1.26317 0.994622047\\n0.079505314 0.839236491 2Vv 422 343 39.2964 0.465993582 230.123 0.792134522 264.324 0.704657301 1.37731 0.84497546 1.16129 0.914401575\\n0.497155163 1.087091786 0 0 0 0 0\\n0.295738555 0.742862094 2Vv 3656 6.70454 0.079505314 251.367 0.865261093 314.806 0.839236491 1.26539 0.776312883 1.09601 0.863\\n0.347502971 0.787289062 2Vv 4340 41.9242 0.497155163 324.216 1.116023545 407.779 1.087091786 1.7451 1.070613497 1.14676 0.90296063\\n0.160888054 0.684815121 2Vv 3937 24.9391 0.295738555 241.582 0.831578947 278.655 0.742862094 1.56649 0.96103681 1.05379 0.829755906\\n0.563198313 0.672144171 2Vv 4057 29.3043 0.347502971 257.411 0.886065884 295.32 0.787289062 1.67487 1.027527607 1.04578 0.823448819\\n\\n0 0.441382528 2Vv 3701 13.5674 0.160888054 226.804 0.780709786 256.881 0.684815121 1.27427 0.781760736 1.02973 0.810811024\\n0.230821955 0.925557836 2Vv 4557 47.4935 0.563198313 250.753 0.863147568 252.128 0.672144171 2.48109 1.522141104 2.06464 1.625700787\\n0.418287121 1.23185199 2Vv 3659 0 0 169.122 0.58215552 165.567 0.441382528 1.41345 0.867147239 1.19488 0.940850394\\n0.242220277 0.918693183 2Vv 3787 19.4648 0.230821955 270.218 0.930150425 347.186 0.925557836 1.39663 0.856828221 1.20462 0.948519685\\n\\n0.06654322 0.894652235 2Vv 4167 35.2734 0.418287121 337.717 1.162496988 462.08 1.23185199 1.47996 0.90795092 1.16167 0.914700787\\n0.661438285 0.605350431 0 0 0 0 0\\n\\n0 0.699104263 2Vv 424 B 3768 20.426 0.242220277 266.098 0.915968469 344.611 0.918693183 1.19038 0.730294479 1.11279 0.876212598\\n0.388786906 1.966284557 2Vv 424 B 3576 5.61147 0.06654322 255.645 0.87998692 335.593 0.894652235 1.29323 0.793392638 1.05666 0.832015748\\n0.559060907 1.257289862 2Vv 424 B 4652 55.7779 0.661438285 242.512 0.834780214 227.073 0.605350431 3.17334 1.946834356 2.59083 2.040023622\\n0.315756769 0.942798113 2Vv 424 B 3580 0 0 219.977 0.757209735 262.241 0.699104263 1.38364 0.848858896 1.11861 0.880795276\\n\\n0.47781525 1.548335155 2Vv 424 B 4082 32.7857 0.388786906 445.022 1.531864652 737.573 1.966284557 1.41967 0.87096319 1.10628 0.871086614\\n0.62069628 0.625195276 2Vv 424 B 4518 47.1446 0.559060907 339.4 1.168290248 471.622 1.257289862 1.7697 1.085705521 1.67371 1.31788189\\n0.155150946 0.860422276 2Vv 424 B 3934 26.6272 0.315756769 279.716 0.962844653 353.653 0.942798113 1.65847 1.017466258 1.12597 0.886590551\\n0.201089315 1.219367652 2Vv 424 B 4293 40.2933 0.47781525 393.096 1.353123817 580.796 1.548335155 1.5433 0.946809816 1.44433 1.137267717\\n0.848654424 0.930940258 2Vv 424 B 4637 52.3422 0.62069628 258.935 0.891311831 234.517 0.625195276 3.46584 2.126282209 2.94381 2.31796063\\n\\n0.06394741 0.768494575 2Vv 424 B 3639 13.0836 0.155150946 241.689 0.831947265 322.753 0.860422276 1.14963 0.705294479 1.12303 0.884275591\\n',\n",
|
||
" \"QW 6B github.com/kuikui-C/DconnLoop W © Search Startpage\\n\\n(1) README o\\n\\npip install matplotlib\\nconda install hicexplorer\\nconda activate DconnLoop\\n\\nUsage\\n\\nThe input data used can be downloaded in the supplementary materials of the paper. The input contact maps use\\nthe cool file format, which, if needed, can be converted and normalized using the HiCExplorer's hicConvertFormat\\ncommand.\\n\\nHiC to cool\\n\\nhicConvertFormat -m ./ENCFFQ97SKJ.hic --inputFormat hic --outputFormat cool -o ./ENCFF@97SKJ.c oO\\nhicConvertFormat -m ./ENCFFQ97SKJ_10000.cool --inputFormat cool —-outputFormat cool -o ./ENCFF\\n\\nGenerate positive and negative samples\\n\\npython PosNeg_Samp_Gen.py -p ./input/gm12878/Ra02014—GM12878-MboI-allreps—filtered.1@kb.cool — oO\\n\\nTraining\\n\\npython leave_one_train.py -d ./PosNeg_samp/ -g 1,2,3 —b 256 -lr @.001 -e 3@ -w 0.0005 -c ./mod oO\\n\\nTesting\\n\\npython leave_one_test.py -d ./PosNeg_samp/ -g 1,2,3 -c ./model/ -f ./model/chri5-record_test. oO\\n\\nScore\\n\\npython score_chromosome.py -p ./input/gm12878/Ra02014—GM12878-MboI-allreps—filtered.1@kb.cool oO\\n\\nCluster\\n\\npython cluster.py -d 5 -i ./scores/chr15.bed -r 10000 -m 0.97 -p 75 -e 10 -o ./cluster/chr15-L oO\\n\",\n",
|
||
" 'Statistics after read pairing\\n\\nAll Pairs\\n\\nFiltered Pairs\\n\\na\\nLI\\nLI\\n[|\\na\\nLJ\\n\\nUnmapped_pairs\\n\\nNot_Reported_pairs\\n\\nReported_pairs\\n\\nLow_qual_pairs\\n\\nPairs_with_singleton\\n\\nMultiple_pairs_alignments\\n\\n',\n",
|
||
" 'OMB\\n\\n100 MB\\n\\n200 MB\\n\\nChromosomes Show Normalization (Obs | Ctrl) Resolution (BP)\\n“aw “aw a a a — y,\\n2 Bp Observed Bala... None © Pivrb ttre teins\\n2.5MB 500KB 100KB 25KB 5KB 1KB 200BP\\nOMB 100 MB 200 MB 300 MB\\n\\n',\n",
|
||
" '| [23]: ls -ltrh sv\\ntotal 973M\\n-rw-rw-r-— 1 pst14 pst14 239M Jan 17 18:02 sample1_GCA_011696235.1_data.zip\\n-rw-rw-r-— 1 pst14 pst14 288M Jan 17 18:08 sample3_GCF_932294415.1_data.zip\\n-rw-rw-r-— 1 pst14 pst14 139M Jan 17 18:13 sample5_GCA_035584115.1_data.zip\\n-rw-rw-r-—- 1 pst14 pst14 72M Jan 17 18:42 sample2_GCA_032401905.1_data.zip\\n-rw-rw-r-- 1 pst14 pst14 236M Jan 17 18:44 sample4_GCF_014633365.1_data.zip\\ndrwxrwxr-x 3 pst14 pst14 77 Jan 18 12:48 samplel\\ndrwxrwxr-x 3 pst14 pst14 77 Jan 18 12:48 sample2\\ndrwxrwxr-x 3 pst14 pst14 77 Jan 18 12:48 sample3\\ndrwxrwxr-x 3 pst14 pst14 77 Jan 18 12:48 sample4\\ndrwxrwxr-x 3 pst14 pst14 77 Jan 18 12:49 sample5\\n#Doing BWA index for all the .fna files\\n\\nfor i in samplex/ncbi_dataset/data/x.fna; do\\nbwa index \"$i\"\\n\\ndone\\n\\n[bwa_idx_build] fail to open file \\'samplex/ncbi_dataset/data/x.fna\\'\\n\\nHal\\n\\n: No such file or directory\\n',\n",
|
||
" '# Run enrichment analysis\\nenrich <- enricher(\\ngene = genelist,\\npvalueCutoff = 0.1,\\n\\npAdjustMethod = \"BH\",\\nminGSSize = 10,\\nmaxGSSize = 500,\\n\\nqvalueCutoff = 0.2,\\nTERM2GENE = tempset)\\n\\n#print(\"Enrichment results structure\\n#print(str(enrich))\\nres <- data. frame(enrich)\\n\\n#print(\"First few rows of enrichment results\\n#print(head(res))\\ndir. create(\"/mnt/volume/data/group8/enrichments/\")\\n# Save results if enrichment results exist\\nif (nrow(res) >= 1) {\\nfilename <- pasteO(\"/mnt/volume/data/group8/enrichments/\", level, \"_DESeq2_Mercator_clusterprof.csv\")\\nwrite.csv(res, file = filename)\\n\\nplot_filename <- paste0(\"/mnt/volume/data/group8/enrichments/\", level, \"_DESeq2_Mercator_plot.pdf\")\\nggsave( filename = plot_filename,\\n\\nplot = dotplot (enrich),\\n\\nwidth = 15, height = 10)\\n\\n+\\n\\nbelse {\\nprint(paste(\"No enrichment data found for\", level, \"skipping.\"))\\nnext\\n\\nprint(\"ALl processing completed.\")\\n\\n@ 04s\\n\\n[1] “Summary of term2gene before filtering:\"\\n‘data. frame\\': 358250 obs. of 4 variables:\\n\\n$ Gene: chr \"“HORVU.MOREX. r3.2HGO150570\" \"HORVU.MOREX. r3.2HG0113330\" \"HORVU.MOREX. r3.3HG0319250\" \"HORVU.MOREX. r3.6HG0546280\" ...\\n$ Term : chr \"Photosynthesis\" \"Photosynthesis\" \"Photosynthesis\" \"Photosynthesis\" ...\\n\\n$ Filename: chr \"mercator_level1_barley.csv\" \"mercator_level1_barley.csv\" \"mercator_level1_barley.csv\" \"mercator_level1_barley.csv\" ...\\n$level : chr “leveli\" \"level\" \"level\" \"level\" .\\n\\nNULL\\n\\n[1] \"Unique levels in term2gene:\"\\n\\n[2] “levela\" \"Level2\" \"level\" \"Level\" \"levels\"\\n\\n[6] \"levels\" \"Level\" \"Levels\" “protscriber\" \"swissprot\"\\n\\n[1] \"Unique filenames in term2gene:\"\\n\\n[1] \"mercator_level1_barley.csv\" \"mercator_level2_barley. csv\"\\n\\n[3] \"mercator_level3_barley.csv\" \"mercator_level4_barley. csv\"\\n\\n[5] \"mercator_level5_barley.csv\" \"mercator_level6_barley. csv\"\\n\\n[7] \"mercator_level7_barley.csv\" \"mercator_level8_barley. csv\"\\n\\n[9] \"mercator_protscriber_barley.csv\" \"mercator_swissprot_barley.csv\"\\n[1] \"Processing enrichment for leveli\"\\n[1] “Filtered termagene table for level has 35825 rows\"\\n[1] “First few rows of filtered termagene:\"\\nTerm Gene\\n1 Photosynthesis HORVU.MOREX. r3.2HG@150570\\n2 Photosynthesis HORVU.MOREX. r3.2HG0113330\\n3 Photosynthesis HORVU.MOREX. r3.3HG0319250\\n\\nError in enricher(gene = genelist, pvalueCutoff = 0.1, pAdjustMethod = \"BH\", : could not find function “enricher\"\\nTraceback:\\n',\n",
|
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" 'Upregulated Downregulated\\n\\nH3K27me’ signal(cp) at upregulated genes HoKzrmed slgnalcotrt at uprepuaes genes nes\\n3x27 me3 signal HaK2763 signal\\n\\n‘ene astce (9) ‘ge dace\\n',\n",
|
||
" '@ Safari File Edit\\n\\nHa|~ <\\n\\n=) Google Docs\\n\\nView History Bookmarks Window Help @&®%OBSB ox zo S Tue 14. Oct 22:35\\n\\n146\\n\\n@ ® 2 quillbot.com Bea ¢, © wm +\\n\\nmH Al Detector - QuillBot Al a] Pastebin.com - #1 past... Humanize cancer biolo... G@ thesis_final - Grammarly * Download file | iLovePDF Start Page @ Grad school experience...\\n\\n© QuillBot PREMIUM\\n\\n&\\nParaphraser\\nGY\\n\\nGrammar\\nChecker\\niva)\\nha}\\n\\nAl Detector\\n\\nQ@\\n\\nPlagiarism\\nChecker\\n\\n@\\nAl\\nHumanizer\\n\\niC)\\nAl Chat\\nG&S\\n\\nAl lmage\\nGenerator\\n\\nSummarizer\\n\\nMA\\n\\nTranslate\\n\\n99\\n\\nCitation\\nGenerator\\n\\neG\\n\\nQuillBot\\nFlow\\n\\nae\\n\\n~”\\n\\nQuillBot for\\nmacOS\\n\\nAl Detector 3 Apps and Extensio... v i)\\n\\nFig. 18: Juicebox browser view of compartments at 250kb\\n\\nCompartments were determined using eigenvector decomposition and the Eigen one values\\nwere exported as bigwig file for downstream and visualization. Regions with positive\\neigenvector values (E1 > 0) were assigned to the transcriptionally active A compartment, Al Human\\nwhile negative values corresponded to the repressive B compartment. Compartment calling\\n\\nAl-generated @ o 4%\\ndone at 2 resolution (50 & 100kb) was essentially the same when visualized, confirming the\\n\\nAl-generated & Al-refined @ 2%\\nrobustness across scales. Saddle! plotsirevealed highly similar compartment segregation)\\n\\nHuman-written & Al-refined @ 0%\\n\\nHuman-written @ 94%\\n\\n¥Y Understanding your results\\n\\naa]\\nga\\n\\nGlobal\\n\\nA/B compartment structure and strength appear to be largely preserved(Figure 19).\\n\\nFig. 20: Saddle profile plot. Strength of the compartmentalization was measured by taking\\nthe ratio between (AA+BB) / (AB+BA) (which forms the red and blue corners in saddle plot)\\n\\n3,565 Words i) cp @ Analysis complete\\n\\nWant your text to sound more authentic? efine with Paraphra\\n',\n",
|
||
" 'Ss Q Sr Sun16.Nov 16:14\\n\\nm Bort © & &) & F\\n\\n@ Safari File Edit View History Bookmarks Develop Window Help\\na »\\noO\\neee f— - < O (c) I1| @ =) jobs.apeng.uantwerpen.be Ga co © 4 +\\n® Industri... © Rewrite... § engeno... § enGeno... § enGeno... § enGeno... % explorin... B inaustii... BD An Al-b... & Application... @® orcip & Argyris... e JOBS mm Profess...\\nwy Lf Vi oA\\n\\nApplication Confirmation\\nYou have successfully submitted your job application\\n\\nJobs Applied For\\nPosting Date 20/10/2025\\n\\nJob Title Industrial PhD in Cancer Multi -omics & Machine Learning\\nApplication Date 16/11/2025\\n\\nJob ID 4180\\n\\nLocation Campus Drie Eiken\\n\\n@ Job Application Platform\\n\\nfe View Submitted Application\\n\\nXW Internet Accoiunte\\n',\n",
|
||
" '[22]\\n\\n#vt\\n\\nfor i in xbowtie.vcf; do\\nvt peek \"$i\"\\necho \"Analysis complete for $i\"\\necho \"\"\\n\\ndone\\n\\nbash\\n\\n',\n",
|
||
" 'In [727]:\\n\\nsce <- combineExpression(\\ncombined. TCR_p3,\\npatient3_transform,\\ncloneCall = \"CTgene\",\\ngroup.by = “orig. ident\",\\nproportion = TRUE\\n\\nError in ‘[.data.frame* (data, , c(\"barcode\", cloneCall, group.by)): undefined columns selected\\nTraceback:\\n\\nna.omit(unique(data[, c(\"barcode\", cloneCall, group.by)]))\\nunique(data[, c(\"barcode\", cloneCall, group.by)])\\n\\ndata[, c(\"barcode\", cloneCall, group.by)]\\n\\n‘[.data.frame* (data, , c(\"barcode\", cloneCall, group.by) )\\nstop(\"undefined columns selected\")\\nshandleSimpleError(function (cnd)\\n\\nAOUBWNP\\n\\nwatcher$capture_plot_and_output ()\\ncnd <— sanitize_call(cnd)\\nwatcher$push(cnd)\\nswitch(on_error, continue = invokeRestart(\"eval_continue\"),\\n. stop = invokeRestart(\"eval_stop\"), error = NULL)\\n. }, “undefined columns selected\", base::quote(*[.data.frame* (data,\\n, c(\"barcode\", cloneCall, group.by))))\\n',\n",
|
||
" 'RUN wget https://github.com/samtools/htslib/releases/download/1.18/htslib-1.18.tar.bz2 && \\\\\\ntar -xvf htslib-1.18.tar.bz2 && \\\\\\ncd htslib-1.18 && \\\\\\n./configure --enable-libcurl && \\\\\\nmake -j$(nproc) && \\\\\\nmake install && \\\\\\ncd .. && rm -rf htslib-1.18*\\n\\n# User addition\\n\\nRUN useradd -m -u 1001 aman && echo \\'aman:123\\' | chpasswd\\nRUN usermod —aG sudo aman\\n\\nRUN usermod -aG rstudio aman\\n\\n# persistent volumes. Use flag -v\\nRUN mkdir -p /home/rstudio/data\\n\\nRUN chown -R aman:aman /home/rstudio\\nVOLUME [\"/home/rstudio/data\"]\\n',\n",
|
||
" \"% modification relative to H3 or GFP\\n\\nHSP22neg HSP70\\n64 64\\n,\\n44 44\\n,\\n2 = 24 °\\ni . t\\nrere peers oa thas\\n8 8\\nHSP18* HSP18neg HSP101\\n. 64 64\\na\\n44 a4\\n6\\n’\\n25 ‘ 278 . ’\\n* .\\noe ae ee we he! ol. ob bt ol a!\\n8 87\\nAPX2neg AtGP\\n64 a 64\\n.\\n44 44\\n*\\nv .\\n.\\n24\\n.\\n. .\\ntr 0 |\\nNHS 24h 48h NHS 24h 48h 3\\nH3K4me3 H3kK9ac Actin\\nels\\na4\\n@ H3 G Gre . .\\n24\\nLigies\\n' 0\\nNHS 24h 48h 1NHS 24h 48h NHS 24h 48 H IHS 24h 48h\\nH3K4me3 H3K9ac H3K4me3 H3K9ac\\n\\nMemory genes Non-memory genes\\n\",\n",
|
||
" 'Sequential function of phytoantycipins and phytoalexins in pre- and\\npost-invasive resistance exemplified by indole glucosinolates (IGs)\\nand camalexin (nonadapted powdery mildew resistance).\\n\\n(a)\\n\\nETVILdAD\\n\\nPenetration resistance (PEN) Resistance to postinvasive growth\\n\\nPEN2 is a B-thioglucoside glucohydrolase (TGG, syn.: myrosinase) New Phytologist (2015) 206: 948-964\\n\\ndoi: 10.111 1/nph.13325\\n',\n",
|
||
" 'Using a R script, | defined a dataframe with sample names, countfiles and the condition (OG or\\n1G)\\n\\nThen using DESeq2 library, | created a DESeq2 dataset object from HTSeq Count files (which\\nwas stored in the initial data frame). Then DESeq2 was ran on this object with 1G as a\\nreference condition.\\n\\nDiffferent type of variance stabilizing transformations such as vsd, rld and ntd were then applied\\non these. Following this Mean-Standard deviations for these data were plotted -\\n\\np\\nA aie count\\n\\nS808 I\\n\\n?\\n\\nrank(mean) © rankimean)\\n\\nsd\\n\\nrank(mean)\\n',\n",
|
||
" 'Yofe, L, Dahan, R. & Amit, I. Single-cell genomic approaches for developing\\nthe next generation of immunotherapies. Nat. Med. 26, 171-177 (2020).\\n\\nBanchereau, R., Cepika, A.-M. & Pascual, V. Systems approaches to human\\nautoimmune diseases. Curr. Opin. Immunol. 25, 598-605 (2013).\\n\\nDavis, M. M., Tato, C. M. & Furman, D. Systems immunology: just getting\\n\\nstarted. Nat. Immunol. 18, 725-732 (2017).\\n\\nGermain, R. N., Meier-Schellersheim, M., Nita-Lazar, A. & Fraser, I. D. C.\\nSystems biology in immunology: a computational modeling perspective.\\n\\nAnnu. Rev. Immunol. 29, 527-585 (2011).\\n',\n",
|
||
" 'Adapter Content [Zi\\n\\n‘ecamuteprctoe cout open tr lay wanna aap ce a oat\\n\\nFastQC: Adapter Content\\n\\nStatus Checks\\nSua ren Fc sc ston wn mite ere erm ey eg ys\\nFastQC: Status Checks\\nSoftware Versions\\n\\nai 8 a nse\\n‘anes Py ar ot es le\\n\\nSiseqera\\n',\n",
|
||
" 'features = c(B=\\'Ms4a1\\',B=\\'Cd19\\',MM=\\'Cd14\\' ,MM=\"Lyz2\\',MM=\\'Fcgr3\\',MM=\\'Ms4a7\\',MM=\\'Fcerig\\' ,MM=\\'Cst3\\',MM=\\'H2-Aa\\',MM=\\'Ly6d\\'\\nrRNA=\\'AY036118\\', rRNA=\\'Gm42418\\' ,Mphase=\\'Cenpa\\' ,Mphase=\\'Ccnb2\\' ,Mphase=\\'Birc5\\' ,Mphase=\\'Mki67\\',Sphase=\\'Pcna\\',\\nSphase=\\'Mcm3\\' , Sphase=\\'Ccne2\\', \\'T\\'=\\'Cd8b\\', \\'T\\'=\\'Cd8a\\', \\'T\\'=\\'Cd4\\', \\'T\\'=\\'Cd3g\\', \\'T\\'=\\'Cd3e\\', \\'T\\'=\\'Cd3d\\')\\n\\nRidge plots\\n\\nRidgePlot(zehn_s, features = features, ncol = 2) # Visualize single cell expression distributions in each cluster\\n\\n#Idents(zehn_s)\\n#table(zehn_s$SingleR. labels)\\n\\n#markers <- FindAllMarkers(zehn_s, only.pos = TRUE, min.pct = @.25, logfc.threshold = 0.25)\\n#head (markers)\\n\\n#--------—_------ Vioin plots\\n\\nVlnPlot(zehn_s, features = features)\\n\\n—--Feauture plots\\n\\nFeaturePlot(zehn_s, features = features)\\n\\nDotPlot(zehn_s, features = features) + RotatedAxis() #per gene\\nDoHeatmap(subset(zehn_s, downsample = 100), features = features, size = 3) # NK cells inflated due to hierarchical c\\ntable(zehn_s$SingleR. labels)\\n\\n# Visualize #co-expression# of two features simultaneously # Yellow = red+green?\\nFeaturePlot(zehn_s, features = features, blend = TRUE)\\n\\nhead(zehn_s@meta.data)\\n\\nVlUnPlot(zehn_s, features\\nVlUnPlot(zehn_s, features\\nVlUnPlot(zehn_s, features\\n\\n“percent.mt\", split. by\\npercent.mt\", split.by\\n“percent.mt\", split. by\\n\\neurat_clusters\")\\ningleR. labels\")\\n“orig. ident\")\\n\\nDotPlot(zehn_s, features = features, split.by = \"SingleR. labels\",\\ncols = c(\"blue\", \"red\", \"green\")) + RotatedAxis() ## Combined labels for meaningful grouping\\n\\nDoHeatmap(zehn_s, features = VariableFeatures(zehn_s) [1:20], cells = 1:42, size =\\nangle = 90) + NoLegend()\\n\\nplot <- DimPlot(zehn_s, reduction = \"pca\") + NoLegend()\\nLabelClusters(plot = plot, id = \"ident\")\\n\\nError in ‘FetchData()*:\\n\\n! None of the requested variables were found (10 out of 25 shown): Ms4a1, Cd19, Cd14, Lyz2, Fcgr3, Ms4a7, Fcerlg, C\\nst3, H2-Aa, Ly6d\\n\\nTraceback:\\n\\n1. ExIPlot(object = object, type = \"ridge\", features = features,\\n. idents = idents, ncol = ncol, sort = sort, assay = assay,\\n. y.max = y.max, same.y.lims = same.y.lims, cols = cols, group.by = group.by,\\n. log = log, layer = layer, stack = stack, combine = combine,\\n: fill.by = fill.by)\\n2. FetchData(object = object, vars = features, slot = layer, cells = cells)\\n3. FetchData.Seurat(object = object, vars = features, slot = layer,\\n. cells = cells)\\nabort(message = paste@(\"None of the requested variables were found\",\\n. m2, \": \", paste(head(x = vars.missing, n = 10L), collapse =\", \")),\\n. class = \"varsNotFoundError\")\\n5. signal_abort(cnd, .file)\\n6. signalCondition(cnd)\\n\\n4\\n\\n',\n",
|
||
" 'In [192]: sapply(combined_TCR, function(df) {\\nsum(df$barcode %in% colnames(combined_seurat) )\\n\\n3)\\n$1: 0 S2: 0 S3: 0 S4: 0 S5: 0 S6: 0 S7: 0 S8: 0 S1: 274\\n',\n",
|
||
" 'Visualization: HiGlass\\n\\nHICCUPs juicer_tools:\\n\\nbedpe file\\n\\n¥\\n\\nEnrichmnet: Juicer\\nAPA,\\nTADs: Arrowhead\\n\\nJuicer\\n\\nv\\n\\nVisualization: JuiceBox\\nAnalysis: HiC Straw\\n\\nTrimmomatic, FostQC\\n\\nHIC-Pro\\n(Current)\\n\\nvalidpairs file\\n\\n¥\\n\\nAnalysis: Cooler\\nlibrary python\\n\\n>\\n\\nFitHiC2 loop caller\\n\\nEnrichment:\\ncoolpup.py\\n',\n",
|
||
" \"Comparison between Megahit and Spades result\\n\\nMetric\\n\\nSPAdes (scaffolds)\\n\\nMEGAHIT (final.contigs)\\n\\n# Contigs\\n\\n3 (most samples)\\n\\n3-4 (Sample 1 has 4)\\n\\nLargest Contig (bp)\\n\\n86,372-90,525\\n\\n86,641-90,807\\n\\nTotal Length (bp)\\n\\n131,459-135,394\\n\\n132,023-155,288\\n\\nGC Content (%) 35.69-36.96 35.08-36.95\\nN50 (bp) 86,372-90,525 86,641-90,807\\nL50 1 1\\n\\nN90 (bp) 18,214-19,048 18,496-25,821\\nL90 3 3\\n\\n# N's per 100 kbp 0.00 0.00\\n\\n\",\n",
|
||
" \"In [2]: import numpy as np\\nimport matplotlib.pyplot as plt\\n\\n# Load the Q matrix from ADMIXTURE output\\ng_matrix = np. loadtxt('inp_admix.3.Q')\\n\\n# Create the plot\\nplt. figure(figsize=(10, 5))\\nfor i in range(q_matrix.shape[1]):\\nplt.bar(range(q_matrix.shape[0]), g_matrix[:, i], bottom=np.sum(q_matrix[:, :i], axis=1))\\n\\nplt.title('Ancestry Proportions (K=3)')\\nplt.xlabel('Individuals')\\nplt.ylabel('Ancestry Proportion')\\nplt.show()\\n\\nAncestry Proportions (K=3)\\n\\n1.04\\n\\n0.87\\n\\n0.6 4\\n\\n0.4 4\\n\\nAncestry Proportion\\n\\n0.27\\n\\n0 10 20 30 40 50\\nIndividuals\\n\\n\",\n",
|
||
" '@ = Safari File Edit View History\\n\\nBookmarks\\n\\nWindow Help\\n\\n& Mon13. Oct 17:45\\n\\nr\\n\\n@ee« M- < >\\n\\nPops\\n\\n©) QUIIBOt eres\\n\\nParaphr\\naser\\n\\nry\\n\\nGramm\\nar Ch...\\n\\nAl\\nDetec...\\nG\\n\\nPlagiari\\nsm...\\n\\nQ\\nAl\\nHuma...\\n\\nic)\\nAl Chat\\nfe\\n\\nAllma\\ne Gen>\\n\\nME\\noe\\n\\na\\nuillBot\\nfor m...\\n\\neo\\n\\n=) quillbot.com\\n\\n& Perfect you!\\n\\nAl Detector\\n\\nEnglish French Spanish German All v\\n\\nswitched compartments WwW\\ncompared to 129 without a\\nswitch, while 8 downregulated\\n\\ngenes overlapped with switches compared to 38 without. Although the odds |\\n\\nFig. 35: (A) Correlation between insulation change and gene expression\\n\\nTo asses the relationship between insulation changes and transcriptional\\nchanges, Ainsulation\\n\\nin a 50kb window was compared with log2fold change of gene expression\\nobtained from\\n\\nDESeq2. Upregulated and downregulated genes both tended to reside at loci\\nwith reduced\\n\\ninsulation relative to controls, with mean Ainsulation values slightly negative in\\nboth groups.\\n\\n3\\n\\nWhen extended to all expressed genes, upregulated and downregulated DEGs\\nshowed\\n\\nsignificantly lower insulation compared with non-DEGs (Mann-Whitney p <\\n0.01). These results\\n\\nindicate that loss of local insulation is modestly but consistently associated\\nwith differential gene\\n\\nexpression following PRC2 inhibition.\\n\\nFig. 36: TSSs of DEGs near a weakened TAD boundary with an overlap window.\\nBackground\\n\\nset is expression matched\\n\\nThe proximity of DEGs to weakened TAD boundaries was examined. A\\nsignificantly higher\\n\\nproportion of upregulated genes was found within 15-25 kb of weakened\\nboundaries compared\\n\\nto background (Otsu: OR ~4.9, p < 1e-9; Li: OR ~5.2, p < 1e-13). Downregulated\\ngenes also\\n\\nshowed enrichment, but the effect was weaker and less consistent across\\n\\nthresholds. When\\n\\nrs\\n\\nPredicting loop types\\n\\nTa inunetiants which aanamic and aniaanamic faatiras hact aradict lann\\n\\nif) cp @ Analysis complete\\n\\nWant your text to sound more authentic?\\n\\n3,611 Words\\n\\ning in all your favorite apps with QuillBot for macOS QP Wie rR elmuraesy\\n\\nModel Version: v5.7.1\\n\\n<~ Share\\n\\n6”\\n\\nof text is likely Al @)\\n© QuillBor\\n\\nAl suman\\nAl-generated (0)\\nAl-generated & Al-refined (0)\\nHuman-written & Al-refined (0)\\n\\nHuman-written @\\n\\nUnderstanding your results\\n\\n& Download\\nFeedback\\nD\\nHistory\\n© 6%\\n0%\\n0%\\n94%\\n\\nRefine with Paraphraser\\n\\noa\\n\\nbe) Apps and Extensions v\\n\\n+ ©\\nx\\n\\n>\\n\\n',\n",
|
||
" 'In [30]: samples =\\n\\ncallset[\\'samples\\']\\n# Check sample names\\n\\nprint (samples)\\n[\\'FG_001\\' \\'FG_002\\'\\n\"FG_009\\' \\'FG_010\\'\\n\\'FG_@17\\' \\'FG_018\\'\\n\"FG_@25\\' \\'FG_026\\'\\n\"FG_033\\' \\'FG_034\\'\\n\"FG_@41\\' \\'FG_042\\'\\n\"FG_049\\' \\'FG_050\\'\\n\"FG_@57\\' \\'FG_058\\'\\n\\n\"FG_003\\'\\n\"FG_011\\'\\n\"FG_019\\'\\n\"FG_027\\'\\n\"FG_035\\'\\n\"FG_043\\'\\n\"FG_051\\'\\n\"FG_059\\'\\n\\n\"FG_004\\'\\n\"FG_012\\'\\n\"FG_020\\'\\n\"FG_028\\'\\n\"FG_036\\'\\n\"FG_044\\'\\n\"FG_052\\'\\n\"FG_060\\']\\n\\n\"FG_0@5\\'\\n\"FG_013\\'\\n\"FG_021\\'\\n\"FG_029\\'\\n\"FG_037\\'\\n\"FG_045\\'\\n\"FG_053\\'\\n\\n\"FG_006\\'\\n\"FG_014\\'\\n\"FG_022\\'\\n\"FG_030\\'\\n\"FG_038\\'\\n\"FG_046\\'\\n\"FG_054\\'\\n\\n\"FG_007\\'\\n\"FG_015\\'\\n\"FG_023\\'\\n\"FG_031\\'\\n\"FG_@39\\'\\n\"FG_047\\'\\n\"FG_@55\\'\\n\\n\"FG_008\\'\\n\"FG_016\\'\\n\"FG_024\\'\\n\"FG_032\\'\\n\"FG_040\\'\\n\"FG_048\\'\\n\"FG_056\\'\\n',\n",
|
||
" '.\\n\\n@ MainWindow Mon Nov 4 21:17\\n\\neee [Juicebox 2.17.00] Hi-C Map <9>: inter.hic\\n\\nFile View Bookmarks Assembly Dev\\nChromosomes\\n\\nAll All Be\\n\\nShow\\n\\nNormalization (Obs | Ctrl) Color Range\\n2 I Tr\\n\\n3773\\n\\nObserved None None\\n\\nI I I I I I It\\n2.5MB 500 KB 100KB 25KB 5KB 1KB 200BP\\n\\nLayerO << oO\\n\\nShow Annotation Panel J\\n\\n',\n",
|
||
" 'Figure 2 Testing MSMC on simulated data. a b ~ 10,000 years ago. Sees\\n\\n(a) To test the resolution of MSMC applied to = Simulation — 40,000 years ago, 8 haplotypes\\ntwo, four and eight haplotypes, we simulated — 2 haplotypes +++» 100,000 years ago, simulation\\na series of exponential population growths and — 4 haplotypes — 100,000 years ago, 4 haplotypes\\n\\n— 100,000 years ago, 8 haplotypes\\n\\n— 8 haplotypes\\n\\ndeclines, each changing the population size by\\na factor of ten. MSMC recovers the resulting\\nzigzag pattern (on a double-logarithmic plot)\\nin different times, depending on the number\\nof haplotypes. With two haplotypes, MSMC\\ninfers the population history from 40,000 to\\n\\n3 million years ago, whereas, with four and\\neight haplotypes, it infers the population\\nhistory from 8,000 to 30,000 years ago ra 7 ;\\nand from 2,000 to 50,000 years ago, 10 10 10 10 10 10\\nrespectively. (b) Model estimates from two Time (years ago) Time (years ago)\\n\\nsimulated population splits 10,000 and 100,000 years ago. The dotted lines plot the expected relative cross coalescence rate between the two\\npopulations before and after the splits. Maximum-likelihood estimates are shown in red (four haplotypes) and purple (eight haplotypes). As expected,\\nfour haplotypes yield good estimates for the older split, whereas eight haplotypes give better estimates for the more recent split.\\n\\nond\\no\\n\\n°\\n©\\n\\n10°\\n\\n°\\nb\\n\\n10*\\n\\nEffective population size\\n°\\nny\\n\\nRelative cross coalescence rate\\no\\no\\n\\n°\\n\\n',\n",
|
||
" '10-\\ne\\ne\\n8 e\\n5 0 e\\ne group\\n2 e eis\\nR @ oc\\n8\\n9 -10-\\n20+\\ne\\n-io 0 to 20\\n\\nPC1: 40% variance\\n',\n",
|
||
" \"Veeb dls MOODLE E-mail Help\\n\\nTah, Catalogue Dashboard My courses Q Dp Aman Shamil Nalakath © aa\\n\\nBioinformatics Il MOOC: View: Overview report\\n\\nBioinformatics Il information 2024 Course Participants Grades\\n\\nGeneral Introduction to Bioinformatics II - Ol...\\n\\nIntroduction to the course\\n\\nCourse info (link to study information syste... Overview report\\n\\nTeacher's announcements\\n\\nCourse participant's forum (ask questions fr... Aman Shamil Nalakath\\n\\nProject Work 1 - Genome project plan\\n\\nGrade\\n\\nBioinformatics group project example from ... Course name\\n\\nWeek 1 Bioinformatics II MOOC 97.00\\n\\nWeek 1 general discussion\\n\\nLecture 1 A - Introduction\\n\\nVideo: Lecture 1 A - Introduction\\n\\nStudents introduction and aims (DL 12.09. 2...\\n\\nHow to (seriously) read a scientific paper\\n\\nArticle 1\\n\\nLecture 1B - Setting up HPC Access\\n\\nVideo: Lecture 1 B - Setting up HPC access TAL\\n\\nMeet and greet\\n\\nGet the mobile app\\n\\nCoursework 1 on Article 1: Aspects of geno... Policies\\n\\n\",\n",
|
||
" '[19]\\n\\ncd ~\\n\\ncat <<EOF > run_spades.sh\\n\\n#!/bin/bash\\n\\nBASE_DIR=\"illegal_logging_trees/fastqc_raw/trimmomatic\"™\\n\\nSAMPLES=(\"wood_sample_1\" \"wood_sample_2\" \"wood_sample_3\" “wood_sample_4\" “wood_sample_5\"\\nTHREADS=8\\n\\nKMERS=\"21,33,55,77,99\"\\n\\nfor SAMPLE in \"${SAMPLES[@]}\"; do\\necho \"Processing $SAMPLE...\"\\n\\nFORWARD_PAIREI\\nREVERSE_PAIREI\\n\\n\"${BASE_DIR}/${SAMPLE}/${SAMPLE}_forward_paired. fq.gz\"\\n${BASE_DIR}/${SAMPLE}/${SAMPLE}_reverse_paired. fq.gz\"\\n\\nOUTPUT_DIR=\"$HOME/illegal_logging_trees/fastqc_raw/${SAMPLE}_spades_out\"\\n\\nspades.py -o \"$OUTPUT_DIR\" \\\\\\n-1 \"$FORWARD_PAIRED\" \\\\\\n-2 \"$REVERSE_PAIRED\" \\\\\\n--only-assembler \\\\\\n--careful \\\\\\n-t \"$THREADS\" \\\\\\n-k \"$KMERS\"\\n\\necho \"$SAMPLE done.\"\\ndone\\n\\nEOF\\n\\n',\n",
|
||
" '@ = Terminal Shell Edit\\n\\nOmertrzk © & &) HF\\n\\n- crop_simu — aman@unicorn: ~ — ssh aman@10.162.143.69 — 181x63\\n\\n(aman_conda_env) aman@unicorn:/mnt/storage3/aman/wdbasejuicer$ ~/juicer_base/scripts/juicer.sh -S merge -d /mnt/storage3/aman/wdbasejuicer -g GCF_000005005.2 -D ~/juicer_base -p /mn\\nt/storage3/aman/basejuicer/references/chrom.sizes -y /mnt/storage3/aman/basejuicer/restriction_sites/GCF_000005005.2_DpnII.txt -s DpnII -z /mnt/storage3/aman/basejuicer/references/G\\n\\nCF_000005005.2_B73_RefGen_v4_genomic.fna -f -t 16\\n\\n*««! /mnt/storage3/aman/basejuicer/restriction_sites/GCF_000005005.2_DpnII.txt does not exist. It must be created before running this script.\\n(aman_conda_env) aman@unicorn:/mnt/storage3/aman/wdbasejuicer$ ~/juicer_base/scripts/juicer.sh -S merge -d /mnt/storage3/aman/wdbasejuicer -g GCF_000005005.2 -D ~/juicer_base -p ~/b\\nasejuicer/references/chrom.sizes -y ~/basejuicer/restriction_sites/GCF_000005005.2_DpnII.txt -s DpnII -z ~/aman/basejuicer/references/GCF_000005005.2_B73_RefGen_v4_genomic.fna -f -t\\n\\n16\\n\\nUsing /home/aman/basejuicer/restriction_sites/GCF_000005005.2_ DpnII.txt as site file\\n\\n+*k*! Move or remove directory \"/mnt/storage3/aman/wdbasejuicer/aligned\" before proceeding.\\n*kk! Type \"juicer.sh -h \" for help\\n(aman_conda_env) aman@unicorn:/mnt/storage3/aman/wdbasejuicer$ rm -r /mnt/storage3/aman/wdbasejuicer/aligned\\n\\n(aman_conda_env) aman@unicorn:/mnt/storage3/aman/wdbasejuicer$ ~/juicer_base/scripts/juicer.sh -S merge -d /mnt/storage3/aman/wdbasejuicer -g GCF_000005005.2 -D ~/juicer_base -p ~/b\\nasejuicer/references/chrom.sizes -y ~/basejuicer/restriction_sites/GCF_000005005.2_DpnII.txt -s DpnII -z ~/aman/basejuicer/references/GCF_000005005.2_B73_RefGen_v4_genomic.fna -f -t\\n\\n16\\n\\nUsing /home/aman/basejuicer/restriction_sites/GCF_000005005.2_ DpnII.txt as site file\\n[W::bam_hdr_read] EOF marker is absent. The input is probably truncated\\n\\nsamtools merge: failed to read header from \"/mnt/storage3/aman/wdbasejuicer/splits/a.fastq.gz.bam\"\\n***k! Some problems occurred somewhere in creating sorted align files.\\n\\n(aman_conda_env) aman@unicorn:/mnt/storage3/aman/wdbasejuicer$ ls -ltrh\\n\\ntotal 12K\\n\\n-rw-rw-r-- 1\\ndrwxrwxr-x 2\\ndrwxrwxr-x 2\\ndrwxrwxr-x 2\\n\\ntotal 4066\\n-rw-rw-r--\\n-rw-rw-r--\\n-rw-rw-r-—\\nlrwxrwxrwx\\nlrwxrwxrwx\\n-rw-rw-r-—\\n-rw-rw-r-—\\n-rw-rw-r-—\\n-rw-rw-r--\\n\\n41\\n41\\n41\\n1\\n41\\n1\\n41\\n1\\n41\\n-rw-rw-r-- 1\\n\\naman\\naman\\naman\\naman\\n\\naman\\naman\\naman\\naman\\naman\\naman\\naman\\naman\\naman\\naman\\n\\naman\\naman\\naman\\naman\\n\\naman\\naman\\naman\\naman\\naman\\naman\\naman\\naman\\naman\\naman\\n\\n®@ Oct 25 22:41 test_output.sam\\n4.0K Oct 25 22:41 fastq\\n4.0K Oct 25 23:19 splits\\n4.0K Oct 26 06:31 aligned\\n(aman_conda_env) aman@unicorn:/mnt/storage3/aman/wdbasejuicer$ ls -ltrh ./splits/\\n\\n)\\n23G\\n23G\\n\\n19\\n19\\n298G\\n\\n7)\\n\\n61\\n\\n2\\n\\n63G\\n\\nOct\\nOct\\nOct\\nOct\\nOct\\nOct\\nOct\\nOct\\nOct\\nOct\\n\\n25\\n25\\n25\\n25\\n25\\n25\\n25\\n25\\n25\\n25\\n\\n22:\\n22:\\n22:\\n23:\\n23:\\n23:\\n23:\\n23:\\n23:\\n23:\\n\\n41\\n41\\n42\\n11\\n11\\n11\\n11\\n19\\n19\\n19\\n\\n\\'**. fastq.gz.sam\\'\\n\\na_R1.fastq.gz\\n\\na_R2.fastq.gz\\n\\na_R2.fastq —> ../fastq/a_R2.fastq\\na_Ri.fastq -> ../fastq/a_R1.fastq\\na.fastq.sam\\n\\na.fastq.gz.bam\\na.fastq_norm.txt.res.txt\\na.fastq_linecount.txt\\n\\na.fastq.bam\\n\\n(aman_conda_env) aman@unicorn:/mnt/storage3/aman/wdbasejuicer$ rm ./splits/a.fastq.gz.bam\\n(aman_conda_env) aman@unicorn:/mnt/storage3/aman/wdbasejuicer$ ~/juicer_base/scripts/juicer.sh -S chimeric -d ~/mnt/storage3/aman/wdbasejuicer -g GCF_@00005005.2 -D ~/juicer_base -p\\n~/basejuicer/references/chrom.sizes -y ~/basejuicer/restriction_sites/GCF_000005005.2_DpnII.txt -s DpnII -z ~/basejuicer/references/GCF_000005005.2_B73_RefGen_v4_genomic.fna -f -t\\n\\n16\\n\\nUsing /home/aman/basejuicer/restriction_sites/GCF_000005005.2_ DpnII.txt as site file\\n\\nmkdir: cannot create directory \\'/home/aman/mnt/storage3/aman/wdbasejuicer/aligned\\': No such file or directory\\n\\n*k*! Unable to create /home/aman/mnt/storage3/aman/wdbasejuicer/aligned, check permissions.\\n\\n(aman_conda_env) aman@unicorn:/mnt/storage3/aman/wdbasejuicer$ rm -r /mnt/storage3/aman/wdbasejuicer/aligned\\n\\n(aman_conda_env) aman@unicorn:/mnt/storage3/aman/wdbasejuicer$ ~/juicer_base/scripts/juicer.sh -S chimeric -d ~/mnt/storage3/aman/wdbasejuicer -g GCF_@00005005.2 -D ~/juicer_base -p\\n~/basejuicer/references/chrom.sizes -y ~/basejuicer/restriction_sites/GCF_000005005.2_DpnII.txt -s DpnII -z ~/basejuicer/references/GCF_000005005.2_B73_RefGen_v4_genomic.fna -f -t\\n\\n16\\n\\nUsing /home/aman/basejuicer/restriction_sites/GCF_000005005.2_ DpnII.txt as site file\\n\\nmkdir: cannot create directory \\'/home/aman/mnt/storage3/aman/wdbasejuicer/aligned\\': No such file or directory\\n\\n*k*! Unable to create /home/aman/mnt/storage3/aman/wdbasejuicer/aligned, check permissions.\\n\\n(aman_conda_env) aman@unicorn:/mnt/storage3/aman/wdbasejuicer$ ls -ltrh /home/aman/mnt/storage3/aman/wdbasejuicer\\n\\nls: cannot access \\'/home/aman/mnt/storage3/aman/wdbasejuicer\\': No such file or directory\\n\\n(aman_conda_env) aman@unicorn:/mnt/storage3/aman/wdbasejuicer$ ~/juicer_base/scripts/juicer.sh -S chimeric -d /mnt/storage3/aman/wdbasejuicer -g GCF_000005005.2 -D ~/juicer_base -p\\n~/basejuicer/references/chrom.sizes -y ~/basejuicer/restriction_sites/GCF_000005005.2_DpnII.txt -s DpnII -z ~/basejuicer/references/GCF_000005005.2_B73_RefGen_v4_genomic.fna -f -t 1\\n\\n6\\n\\nUsing /home/aman/basejuicer/restriction_sites/GCF_000005005.2_ DpnII.txt as site file\\n(-: Looking for fastq files...fastq files exist\\n--- Using already created files in /mnt/storage3/aman/wdbasejuicer/splits\\n\\n(-: Aligning files matching /mnt/storage3/aman/wdbasejuicer/fastq/*_Rx. fastqx\\nto genome /home/aman/basejuicer/references/GCF_000005005.2_B73_RefGen_v4_genomic.fna with site file /home/aman/basejuicer/restriction_sites/GCF_000005005.2_DpnII.txt\\nUsing already aligned reads /mnt/storage3/aman/wdbasejuicer/splits/a.fastq.sam\\n\\ni\\n\\nSat Oct 26 9:19\\n\\nrm\\n\\nTerminal\\n\\na\\n\\nFileZilla alias\\n\\n',\n",
|
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" 'In [409]: combined_TCR <- combineTCR(\\nlist(patient3 = combined.TCR_p3, patient4 = combined.TCR_p4),\\nsamples = c(\"patient3\", \"patient4\"),\\nremoveNA = FALSE,\\nremoveMulti = FALSE,\\nfilterMulti = FALSE\\n)\\n\\nError in (function (..., row.names = NULL, check. rows = FALSE, check.names = TRUE, : arguments imply differing numb\\ner of rows: 2471, @\\nTraceback:\\n',\n",
|
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" '‘The difference between orthologs and paralogs lies in their evolutionary origin and functional\\n\\nrgence:\\n\\n1. Orthologs\\n\\n* Def\\n\\n1n: Genes that originate from a common ancestor due to a speci\\n\\n+ Function: Often retain similar functions across different species.\\n\\n+ Example: The hemoglobin gene in humans and mice is orthologous because both species\\ninherited it from a common ancestor.\\n\\nints:\\n\\nKey\\n\\nV Arise from speciation events\\nV Found in different species\\n\\nV Generally have similar functions\\n\\n2. Paralogs\\n\\n* Def\\n\\n1n: Genes that arise from a gene duplication event within the same species.\\n+ Function: May evolve new or specialized functions.\\n\\n+ Example: Hemoglobin and myoglobin in humans—both originated from a gene duplication event\\nbut evolved to serve different functions.\\n\\nKey Points:\\nV Arise from gene duplication events\\nV Found within the same species (or later diverging species)\\n\\nV Can have different functions\\n\\nSummary Table\\nFeature Orthologs Paralogs\\nOrigin Speciation Gene duplication\\nFound in Different species ‘Same species (or later divergence)\\nFunction Often conserved Can diverge significantly\\n\\nExample Human vs. mouse hemoglobin Human hemoglobin vs. myoglobin\\n',\n",
|
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" 'Analysis using 3DCOORDX\\n\\nEpidermal layer Cell Distance\\n\\nElongation\\nratios (max/min)\\n',\n",
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" 'P~ av ptot the corrected data in fatt neatnap and compare to the white-reé cotomap a4\\nnn thanks for the alternative cotlormap toning 0 Nttps://tuitUer, con/HiC nenes/status/120632691912262522/photo/L9HE\\nAngort natplattib.pyplot as plt\\nAnport nunpy 25\\nform = Loslorn(vainet, yaae=100\\nfraitpunch = SS-blend patettel[white\\', *red\"], as_enapeTeue)\\n1 axs = pt-cubptots(\\naxe aslo, 01\\nfrsset titel Pumpkin Spice\")\\nin\\'= ax.natshow(cle-natrix(botancesFalse) (:], vaaxevaak, cmap=\\' ‘att:\\npltseotorbar(im, axcax ,fraction=0.046, pased.04, labele\\'counts (Linear):\\npltcxtictsenronstarts,cle-ehroaanes);\\nx= aslo, 1)\\narsaet titlel rust Punch\")\\nnd = axcnatshou(clr.natrbx(balancestalse) [+], vaaxevaax, enapetruttpunch):\\npltseotorbar(in3, axzax, tractlane0. 46, padod.04, Labelm\\'counts (Linear) \");\\npitexticts(eheonstarts,clesehronanes):\\nromanora, enap=\"fatt):\\nTabelz‘counts (10g):\\npitscolorbor(im, a ty tabel= counts (129)\"):\\npltcxtictsenronstarts,clrsehromanes):\\npieetignt_tayout(d\\n1 Om yen\\nett In(91, Line 12\\nars asi, 01\\nUa ar set titiet Pumpkin Spice\")\\n<> 18 n> axsnatshow(clrsnatrin(oatancesfalse) (21, vnaxcimax, enape\"\"att\"):\\nFy teaction=0.046, pa aunts (Lineoe) Ys\\n21 plt-aticka(ehronstarts,clrchronmanes)\\nFite ~/anacandss/envs/cool_notebook/ib/python3.10/site-peckages/aatplotib/aKes/_ates.py:8470, An Axes.Ratstow(selt, 2, sskvargs)\\np-asanyerray(Z)\\n‘Cordgin\\'s \"upper\",\\nSnterpolation\\': “nearest”,\\naspect’ ‘equal’, 1 (already the Sastow defeutt)\\nsstsaros)\\nself title.set 9.05)\\n72 Self xaxis tek t0p()\\nFite ~/anacandas/envs/coo1_sotebook/lib/python3.10/site-peckages/aatplotib/_inSt_.py:3521, sn _preprocess data.clocals>.snner(ak, data, 3798, #shvargs)\\n1518 efunctoots wraps (fone)\\nAt data is none:\\n128 F _print supported values:\\nBay “nag te fy supported values are Cy \\'.Josn(napirepr, values) )*\\nValuetrrors “YALL! 1s not a valid value for cnaps supported values are ‘Accent, “Aecent.r\\', \"Blues\", ‘Sues e*, \"BSG, \"HYBG_r*, ‘SUGh\\', “BUGR_r*, “BuPU\\', ‘BUPULe*, “OMAR, “OMiap.r\\', “Oark2\", \"OAPKZe*, “Sabu, \"GrBuLr*, \"Grays!, “Grays.e*, ‘Greens’, ‘Greens r*, \"Greys!, “Greys.e*, ‘OMRA\", “OrRdLe*, ‘Oranges’, ‘Oranges.e*, \"PRGn\\', “PRGRe*, ‘Patred!, “Palred.r\\', “Pastelt\\', \"Pasteli.r\\', \"Pastel2\\', “Pastel2.e*, \"PAYG, “PIVGr\\', \"PuBu\\', “PuBLGO!, “PuBuGn.c\\', “Pusu, \"PuOe\", “PuDr_f*, \"PURG, “PuRde*, “Purples\\', “Purplesf*, \"RéBu\", \"RABLLe*, *RAGy\\', \"RAG\\nPumpkin Spice\\n10 “ee\\nos\\n06\\no4\\n02\\n00\\n10\\n08\\n06\\n00\\n09 na oa 06 oe 10 oo o oa os oe To\\nPROBLEMS OUTPUT BEBUGCONSOLE TERMINAL ORTS_JUPYTER gore te OH 4 x\\n\\nty\\ntrem atlatincalomaps laprt repster\\n\\nexept ort\\nrom atletib.on aprt repster_ capo repster\\n\\nport matlab as apt\\n“ort natplath. phat as pt\\n‘ore ney an oe\\n',\n",
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" \"Dear Dr. Rosa,\\n\\nThank you for getting back to me. | understand the limitations. Would it be okay if | contact you again maybe during March/April next year? In the meantime, I'll explore other\\noptions as you suggested. Thank you again\\n\\nRegards,\\nAman\\n\\n» Lozano Duran, Rosa <rosa.lozano-duran@uni-tuebingen.de>\\n\\nThu 8/29/2024, 8:09 PM\\n\\nDear Aman,\\n\\nlam sorry it took so long for me to come back to you. | have been\\n\\ntrying to find a potential supervisor for you in the group, but\\nunfortunately every suitable candidate is over-committed at the moment\\nand cannot take on another student. | am sorry that | cannot be more\\npositive right now.\\n\\nOne alternative, if you are interested in geminiviruses, would be the\\ngroups at the DSMZ in Braunschweig (e.g. BjGrn Krenz) - perhaps they\\nhave the capacity to host you in your preferred period? It is a great\\nplace with vast experience in plant-virus interactions; | can only\\nrecommend it.\\n\\nIf you see another opportunity to to an internship with us next year,\\nplease reach out and we can try again.\\n\\n| wish you the best of luck in your MSc's!\\n\\nBest wishes,\\n\",\n",
|
||
" 'Skipping\\nSkipping\\nSkipping\\nSkipping\\nSkipping\\nSkipping\\n\\nCluster\\nCluster\\nCluster\\nCluster\\nCluster\\nCluster\\n\\nChrom\\nChrom\\nChrom\\nChrom\\nChrom\\nChrom\\n\\nchromosome_2: index\\nchromosome_4: index\\nchromosome_2: index\\nchromosome_4: index\\nchromosome_2: index\\nchromosome_4: index\\n\\nimport matplotlib.pyplot as plt\\nimport seaborn as sns\\n\\nplt. figure(figsize=(16, 8))\\nsns.boxplot(data=df_r2, x=\\'Group\\', y=\\'r2\\', showfliers=False)\\nplt.xticks(rotation=90)\\nplt.ylabel(\\'Mean r? (LD) in 5kb windows\")\\nplt.title(r\\'$r*2$ Across Clusters and Chromosomes \\')\\nplt.tight_layout()\\nplt.show()\\n\\n-1\\n-1\\n-1\\n-1\\n-1\\n-1\\n\\nis\\nis\\nis\\nis\\nis\\nis\\n\\nout\\nout\\nout\\nout\\nout\\nout\\n\\nof\\nof\\nof\\nof\\nof\\nof\\n\\nbounds for\\nbounds for\\nbounds for\\nbounds for\\nbounds for\\nbounds for\\n\\naxis\\naxis\\naxis\\naxis\\naxis\\naxis\\n\\nS2Se000\\n\\nwith\\nwith\\nwith\\nwith\\nwith\\nwith\\n\\nsize\\nsize\\nsize\\nsize\\nsize\\nsize\\n\\nSeseo000\\n\\nP Across Clusters and Chromosomes\\n\\n0.12\\n\\n2 ° 2\\n3° ry 2\\na cy S\\n\\nMean r? (LD) in 5kb windows\\n\\n°\\n3\\ng\\n\\n0.02\\n\\nCluster 0 - chromosome_1\\n\\nCluster 0 - chromosome_3\\n\\nCluster 1 - chromosome_1\\n\\nCluster 1 - chromosome_3\\nCluster 2 - chromosome_1\\n\\nCluster 2 - chromosome_3\\n\\n',\n",
|
||
" 'chril startl endl chr2 start2 end2\\n',\n",
|
||
" 'Library (tximport)\\n\\n# Define paths to trimmed reads and extract sample names\\nfastg files <- list.files(path = \"/mnt/volume/data/group8/studies/trimmed\", pattern = \"* 1.fastq.gz\", full.names = TRUE)\\nsamples <- basename(fastg files) %>% sub(\"_1\\\\\\\\.fastq\\\\\\\\.g:\\n\\n# Create paths to Kallisto abundance files\\nfiles <- file.path(\"/mnt/volume/data/group8/kallisto_output\"\\nnames (files) <- samples\\n\\nsamples, “abundance. tsv\")\\n\\n# Check that all files exist\\n\\nmissing files <- files[!file.exists(files)]\\n\\nif (length(missing_files) > 0) {\\ncat(\"Warning: The following abundance.tsv files are missing: \\\\n\")\\nprint(missing_files)\\n\\n} else {\\ncat(\"AUL abundance.tsv files found. \\\\n\")\\n\\n}\\n\\n# Load the tx2gene mapping\\n‘tx2gene <- read. csv(\"/mnt/volume/data/group8/references/tx2gene.csv\")\\n\\n# Run tximport to summarize counts to gene level\\ntxi <- tximport(files, type = \"kallisto\", tx2gene = tx2gene)\\n\\n# Check the structure of the imported object\\nstr(txi)\\n\\n# Save gene-level counts to a CSV file\\n\\nwrite. csv(txigcounts, file = \"/mnt/volume/data/group8/kallisto_output/gene_counts.csv\", row.names = TRUE)\\n\\nwrite. csv(txigabundance, file = \"/mnt/volume/data/group8/kallisto_output/gene_counts_abundance.csv\", row.names = TRUE)\\ncat(\"Gene-level counts saved to /mnt/volume/data/group8/kallisto_output/gene_counts.csv\\\\n\")\\n\\n@ oas\\n\\nALL abundance. tsv files found.\\n\\nError in tximport(files, type =\\nTraceback:\\n\\n\"kallisto\", tx2gene = tx2gene): length(files) > @ is not TRUE\\n\\n1. stopifnot(length(files) > @)\\n2. stop(simpleError(msg, call = if (p <- sys.parent(1L)) sys.call(p)))\\n\\nIn the next notebook, we try and take a look at this data with PCA and find differentially expressed genes.\\n',\n",
|
||
" 'Copper sparing by cytochrome c,\\nexpression in cyanobacteria & algae\\n\\nIn cyanobacteria and some algae, plastocyanin function can be\\nreplaced by cytochrome c, (Fe-heme protein) when Cu is deficient\\n\\nCRR = Copper response regulator\\n\\nthylakoid lumen\\n\\nprotease\\n\\nHours since Cu addition\\n-Cu 2 6 16 26 +Cu\\n\\nPlasto-\\n\\ncyanin PE Copper <J\\n— VAW\\n\\nCyt co lron (Fe) Proteolysis av\\n\\nKropat, }., Gallaher, $.D., Urzica, E.L, Nakamoto, SS., Strerkert, D_ Tottey, S, Mason, A.Z. and Merchant, S.S. (2015). Copper economy in Chlamydomonas: Pricritized allocation and reallocation of copper to respiration vs. photosynthesis.\\nProc. Natl. Acad. Sci. USA 112: 2644-265] ; Merchant, S., and Bogorad, L. (1986). Regulation by copper of the expression of plasiey yanin and cytochrome c552 in Chlamydomonas reinhardi. Mol. Cell. Biol. 6: 462-469.\\n',\n",
|
||
" \"| (17]:\\n\\nhicPlotTADs -h\\n\\nusage: hicPlotTADs —-tracks tracks.ini —-region chr1:1000000—4000000 -o image.png\\n\\nPlots genomic tracks on specified region(s). Citations : Ramirez et al. High-\\nresolution TADs reveal DNA sequences underlying genome organization in flies\\nNature Communications (2018) doi:10.1038/s41467-@17-02525-w Lopez—Delisle et\\nal. pyGenomeTracks: reproducible plots for multivariate genomic datasets.\\nBioinformatics (2020) doi:10.1093/bioinformatics/btaa692\\n\\noptional arguments:\\n\\n-h, —-help show this help message and exit\\n\\n--tracks TRACKS File containing the instructions to plot the tracks\\nThe tracks.ini file can be genarated using the\\n“make_tracks_file*’ program.\\n\\n--region REGION Region to plot, the format is chr:start-end\\n\\n--BED BED Instead of a region, a file containing the regions to\\nplot, in BED format, can be given. If this is the\\ncase, multiple files will be created. It will use the\\nvalue of —-outFileName as a template and put the\\ncoordinates between the file name and the extension\\n\\n--width WIDTH figure width in centimeters (default is 40)\\n\\n--plotWidth PLOTWIDTH\\nwidth in centimeters of the plotting (central) part\\n\\n--height HEIGHT Figure height in centimeters. If not given, the figure\\nheight is computed based on the heights of the tracks\\nIf given, the track height are proportionally scaled\\nto match the desired figure height\\n\\n--title TITLE, -t TITLE\\nPlot title\\n\\n--outFileName OUTFILENAME, -out OUTFILENAME\\nFile name to save the image, file prefix in case\\nmultiple images are stored\\n\\n--fontSize FONTSIZE Font size for the labels of the plot (default is 0.3 *\\nfigure width)\\n\\n--dpi DPI Resolution for the image in case the ouput is a raster\\ngraphics image (e.g png, jpg) (default is 72)\\n\\n—-trackLabelFraction TRACKLABELFRACTION\\nBy default the space dedicated to the track labels is\\n@.05 of the plot width. This fraction can be changed\\nwith this parameter if needed\\n\\n—-trackLabelHAlign {left, right, center}\\nBy default, the horizontal alignment of the track\\nlabels is left. This alignemnt can be changed to right\\n\\nor center.\\n\\n—-decreasingXAxis By default, the x-axis is increasing. Use this option\\nif you want to see all tracks with a decreasing\\nx-axis.\\n\\n--version show program's version number and exit\\n\\nhicPlotTADs --matrix TADs_zscore_matrix.h5 \\\\\\n--outFileName TADs_summary.png \\\\\\n--bedFile TADs_domains.bed \\\\\\n--boundariesFile TADs_boundaries.bed \\\\\\n--dpi 300\\n\\nusage: hicPlotTADs —-tracks tracks.ini —-region chr1:1000000—4000000 -o image.png\\nhicPlotTADs: error: the following arguments are required: —-tracks\\n\\n12\\n\",\n",
|
||
" 'My Drive > Colab Notebooks ~\\n\\n| Type v || People ~ || Modified v || Source ~v\\n\\nName 4\\n\\ncO Copy of data_preprocessing_tools.ipynb\\n\\n6 Data.csv\\n\\nDate modified\\n\\n10:49 PM\\n\\nNov 21\\n\\nFile size\\n\\n12 KB\\n\\n226 bytes\\n',\n",
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" '3. Filtering well-supported variants from artifacts\\nBasic quality filtering:\\n\\nveflib provides a variety of functions for VCF manipulation. We can perform variant filtering using arbitrary expressions based on values in the\\nINFO and FORMAT fields using vcffilter.\\n\\n# check the INFO tags in our vef file\\ngrep \"#INFO\" F_graminearum.raw_variants.vcf | less -S\\n\\n# basic filter to remove low-quality sites\\n\\nveffilter -f \\'QUAL > 10\\' F_graminearum.raw_variants_split.vcf > F_graminearum.QUALfilt.vcf\\n## check how many variants pass the filter\\n\\nvt peek F_graminearum. QUALfilt.vcf\\n\\n# We know that QUAL value increases with the number of reads covering the site (depth of coverage).\\n# So we can improve this basic filter scaling quality by depth (A0=\"Count of full observations of this alternate\\nveffilter -f ‘QUAL / AO > 10\\' F_graminearum.raw_variants_split.vcf > F_graminearum.DP_Qualfilt.vcf\\n\\nvt peek F_graminearum.DP_Qualfilt.vcf\\n\\nYou can explore other filters to the data based on information about the alignment in the VCF file.\\nFor some examples see dDocent filtering tutorial.\\n\\n4. Filtering and calculating statistics with VCFTools\\n\\nIn most biological systems, transitions (A<->G, C<->T) are far more likely than transversions, so we expect the Ts/Tv ratio to be pretty far from\\n0.5, which is what it would be if all mutations between DNA bases were random. We expect that false SNPs present a different Ts/Tv ratio than\\ntrue SNPs. This is sometimes used to evaluate the quality of filters applied. Let us compare the Ts/Tv ratio in regions of bad quality (marked as\\nfiltered) compared to the unfiltered regions:\\n\\nveffilter -f ‘QUAL / AO > 10\\' /data/proj/teaching/NGS_course/Data/Lecture3/complete_data/11_variantCall/F_gramin«\\nveffilter -f ‘QUAL / AO < 10\\' /data/proj/teaching/NGS_course/Data/Lecture3/complete_data/11_variantCall/F_gramint\\n\\nveftools --vcf F_graminearum.filt.vcf --out F_graminearum.pass.HG970333.1 --FILTER-summary\\nveftools --vcf F_graminearum.fail.vcf --out F_graminearum. fail.HG970333.1 --FILTER-summary\\n\\ncat F_graminearum. fail.H6970333.1. FILTER. summary\\ncat F_graminearum. pass .HG970333.1. FILTER. summary\\n\\nWe want to calculate the window-based Ts/Tv and some diversity statistic (pi):\\n',\n",
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||
" 'Results - 1. Confocal Images\\n\\n~ Adaxial oi1\\n\\nNucelus ss __-» Abaxial oi2\\n\\nAdaxial ii1. <——\\n\\nAbaxial ii2 __—» Chalaza\\n\\n—+ Funiculus\\n',\n",
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||
" 'Biochemical and molecular requirements for toxin activity\\n(= modes of toxin specificity)\\n\\ne Solubility of the pro-toxin in the gut\\n- Lepidoptera, Diptera: strongly alkaline pH\\n- Coleoptera: neutral - weekly acidic pH\\n\\ne Proteolytic specificity\\n- Lepidoptera, Diptera: Serin proteases\\n- Coleoptera: Cystein-, Aspartic acid proteases\\n\\ne Receptor binding\\n- mainly domain Il; in addition domain Ill\\n- domain |: “quality” of the pore\\n',\n",
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" 'run071_Sample_Tag_Calls.csv 28.2 KB Mar 23, 2025, 12:19 PM\\nrun071_VDJ_perCell.csv 132.3 KB Mar 23, 2025, 12:19 PM\\n',\n",
|
||
" 'Save prompt\\n\\nDo you want to save a snapshot of your work?\\n\\nSelect an option: | no\\n\\nCancel OK\\n\\n32__roiset.zip attempt_32_\\n: timestamp_150.tif\\n',\n",
|
||
" 'conda create -n test_velocyto\\n\\nconda activate test_velocyto\\n\\nmamba install numpy scipy cython numba matplotlib scikit-learn h5py click\\npip install pysam\\n\\npip install velocyto\\n\\npip install numpy\\n\\npip install --no-build-isolation velocyto\\n\\nvelocyto --version\\n\\n',\n",
|
||
" 'In [516]: lapply(contig_list_1, nrow)\\nlapply(contig_list_2, nrow)\\n\\n1. 8871\\n\\n$P17B\\n5504\\n$P17L\\n5304\\n$P18B\\n3005\\n$P18L\\n2467\\n$P19B\\n15003\\n$P19L\\n5011\\n$P20B\\n18513\\n$P20L\\n347\\n',\n",
|
||
" \"| [12]:\\n\\nzcat FitHiC.spline_pass1.res20000.significances.txt.gz | head\\n\\nfey\\n=\\n5\\nray\\n\\n10000\\n10000\\n10000\\n10000\\n10000\\n10000\\n10000\\n10000\\n10000\\n\\nPRPRPPPRPRPR\\n\\nawk '$7 <= @.05' FitHiC.spline_pass1.res20000.significances.txt.gz | wc -l\\n\\n1279317\\n\\nawk '$1 =\\n3575\\n\\nawk '$1 != $3 && $7 <=\\n\\n1275742\\n\\nfragmentMid1\\n\\n1\\n\\nPRPRPPRPRPRP\\n\\n$3 && $7 <=\\n\\nchr2\\n30000\\n2470000\\n3070000\\n3130000\\n6270000\\n7470000\\n8790000\\n20830000\\n24010000\\n\\n@.05' FitHiC.spline_pass1.res20000.significances.txt.gz | wc -l\\n\\n@.05' FitHiC.spline_pass1.res20000.significances.txt.gz | wc -l\\n\\nfragmentMid2 contactCount\\n1 1. 000000e+00 1, 000000e+00\\n1 3.096889e-01 1, 000000e+00\\n1 2.609742e-01 1, 000000e+00\\n1 2.569911e-01 1, 000000e+00\\n1 1.502626e-01 1, 000000e+00\\n1 1.350215e-01 1, 000000e+00\\n1 1.343277e-01 1, 000000e+00\\n1 8.989326e-02\\n1 6.377743e-02\\n\\nPRPRPPR\\n\\np-value q-value bias1\\n1.\\n+ 000000e+00\\n+ 000000e+00\\n+ 000000e+00\\n+ 000000e+00\\n+ 000000e+00\\n1.\\n1. 000000e+00\\n1. 000000e+00\\n\\n000000e+00\\n\\n000000e+00\\n\\nbias2\\n\\nPRPRPRPRPP\\n\\n000000e+00\\n\\nExpCC\\n- 000000e+00\\n- 000000e+00\\n- 000000e+00\\n- 000000e+00\\n- 000000e+00\\n- 000000e+00\\n1.\\n1. 000000e+00\\n1. 000000e+00\\n\\n1. 000000e+00\\n1. 000000e+00\\n\\n26.440884\\n+ 370613\\n+ 302423\\n+ 297047\\n+ 162828\\n+ 145051\\n@.144249\\n\\neooo 9°\\n\\n@.094193\\n@.065902\\n\",\n",
|
||
" 'File Edit Font\\n\\nFile Edit Font\\n\\n',\n",
|
||
" '3.3 SUVRS affects the gene region more\\n\\nPascatrnted metrytationtvet\\n\\n',\n",
|
||
" 'Exploring Drought Stress-Related Gene Families in Grasses: this project analyzes gene families associated with drought tolerance in\\ncereal crops. It examines structural variants and their functional impact on the genetic diversity of drought-resistance genes.\\n',\n",
|
||
" 'In [415]: sapply(combined.TCR_p3, nrow)\\nsapply(combined.TCR_p4, nrow)\\n\\nP3_S1: 2471 2:0 3:0 4:0 5:06:07:08:0\\n\\nP4_S1: 641\\n',\n",
|
||
" '[pst14@frontend ref_gen]$ head -n 3@ alignment_stats_2.txt | grep *SN | cut -f 2-\\nraw total sequences:\\nfiltered sequences:\\nsequences: 363636\\nis sorted: 1\\n\\n1st fragments: 181818\\nlast fragments: 181818\\n\\nreads\\nreads\\nreads\\nreads\\nreads\\nreads\\nreads\\nreads\\n\\nMapped: 300190\\nmapped and paired:\\nunmapped: 63446\\nproperly paired:\\npaired: 363636\\nduplicated:\\n\\nMQe: 22465\\nQC failed:\\n\\nnon-primary alignments:\\n\\ntotal\\ntotal\\ntotal\\nbases\\nbases\\nbases\\nbases\\n\\nlength: 47169329\\nfirst fragment len\\nlast fragment leng\\nMapped: 39474722\\nmapped (cigar):\\ntrimmed: 0\\nduplicated:\\n\\nmismatches: 1289379\\nCpst14efrontend ref_gen]$\\n\\n363636\\n7)\\n\\n266108 # paired-end technology bit set + both mates mapped\\n\\n183304 # proper-pair bit set\\n# paired-end technology bit set\\n\\n(7) # PCR or optical duplicate bit set\\n# mapped and MQ=@\\n\\nQ\\n\\n7)\\n\\n# ignores clipping\\ngth: 23589996 # ignores clipping\\n\\nth: 23579333 # ignores clipping\\n# ignores clipping\\n31931868 # more accurate\\n\\nQ\\n# from NM fields\\n\\n',\n",
|
||
" '=> ERROR [ 2/13] RUN apt-get update && apt-get install -y build-essential\\n\\n> [ 2/13] RUN apt-get update && apt-get install -y build-essential\\nthon3-pip zlib1g-dev libncurses5-dev libbz2-dev liblzma-dev\\n-476 Get:1 http://archive.ubuntu.com/ubuntu jammy InRelease [270 kB]\\n-477 Get:2 http://security.ubuntu.com/ubuntu jammy-security InRelease [129 kB]\\n-655 Get:3 http://archive.ubuntu.com/ubuntu jammy-updates InRelease [128 kB]\\n-695 Get:4 http://archive.ubuntu.com/ubuntu jammy-backports InRelease [127 kB]\\n-197 Err:2 http://security.ubuntu.com/ubuntu jammy-security InRelease\\n.197 At least one invalid signature was encountered.\\n-992 Err:1 http://archive.ubuntu.com/ubuntu jammy InRelease\\n-992 At least one invalid signature was encountered.\\n-698 Err:3 http://archive.ubuntu.com/ubuntu jammy-updates InRelease\\n-698 At least one invalid signature was encountered.\\n-137 Err:4 http://archive.ubuntu.com/ubuntu jammy-backports InRelease\\n.137 At least one invalid signature was encountered.\\n-176 Reading package lists...\\n+326\\n+326\\n+326\\n+326\\n+326\\n+326\\n+326\\n+326\\n\\nwget\\n\\nWWWWWWWWWWWNNPPRPRPOOOO\\n\\nm=ememeame\\n\\n12 #dependencies\\n\\n13 >>> RUN apt-get update && apt-get install -y \\\\\\n14 | >>> build-essential \\\\\\n15 >>> wget \\\\\\n\\n16 | >>> unzip \\\\\\n\\n17 | >>> bzip2 \\\\\\n\\n18 >>> gcc \\\\\\n\\n19 >>> gt+ \\\\\\n\\n20 >>> openjdk-11-jdk \\\\\\n21 >>> git \\\\\\n\\n22 | >>> curl \\\\\\n\\n23 | >>> make \\\\\\n\\n24 | >>> ca-certificates \\\\\\n25 | >>> vim \\\\\\n\\n26 >>> python3 \\\\\\n\\n27 | >>> python3-pip \\\\\\n\\n28 >>> zlibig-dev \\\\\\n\\n29 | >>> libncurses5-dev \\\\\\n30 | >>> libbz2-dev \\\\\\n\\n31 | >>> liblzma-dev \\\\\\n\\n32 | >>> samtools \\\\\\n\\n33 | >>> locales \\\\\\n\\n34 >>> && apt-get clean && rm -rf /var/lib/apt/lists/*\\n35\\n\\nERROR: failed to solve: process \"/bin/sh -c apt-get update && apt-get install -y\\nes vim python3 python3-pip zlib1g-dev libncurses5-dev\\nully: exit code: 100\\n\\nwget\\n\\nsamtools\\n\\nlibbz2-dev\\n\\nunzip\\n\\nbzip2\\nlocales\\n\\nunzip\\n\\nbuild-essential\\n\\nbzip2\\n\\ngcc\\n\\nwget\\n\\ngcc\\n\\ng++\\n\\nunzip\\n\\n: GPG error: http://security.ubuntu.com/ubuntu jammy-security InRelease: At least one invalid signature was encountered.\\nThe repository \\'http://security.ubuntu.com/ubuntu jammy-security InRelease\\' is not signed.\\nGPG error: http://archive.ubuntu.com/ubuntu jammy InRelease: At least one invalid signature was encountered.\\nThe repository \\'http://archive.ubuntu.com/ubuntu jammy InRelease\\' is not signed.\\nGPG error: http://archive.ubuntu.com/ubuntu jammy-updates InRelease: At least one invalid signature was encountered.\\nThe repository \\'http://archive.ubuntu.com/ubuntu jammy-updates InRelease\\' is not signed.\\nGPG error: http://archive.ubuntu.com/ubuntu jammy-backports InRelease: At least one invalid signature was encountered.\\nThe repository \\'http://archive.ubuntu.com/ubuntu jammy-backports InRelease\\' is not signed.\\n\\ngt+ openjdk-11-jdk git curl make ca-certificates vim 6.7s\\nopenjdk-11-jdk git curl make ca-certificates vim python3 py\\n&& apt-get clean && rm -rf /var/lib/apt/lists/*:\\nbzip2 gcc gt+ openjdk-11-jdk git curl make ca-certificat\\nlocales && apt-get clean && rm -rf /var/lib/apt/lists/*\" did not complete successf\\n\\nliblzma-dev\\n\\nView build details: docker-desktop://dashboard/build/desktop—linux/desktop—linux/x7c5h@h4a7x9t3nqc4klhezvz\\n\\naman@Laptop-von-Aman juicer_hpro %\\n\\nsamtools\\n\\n',\n",
|
||
" 'First name* ©\\nLast name* ©)\\nEmail address* ©\\nTitle*\\n\\nPosition*\\n\\nAurélien\\n\\nTellier\\n\\naurelien.tellier@tum.de\\n\\nProf. Dr.\\n\\n',\n",
|
||
" '(patch B)\\n\\nNormalized PD coordinate intervals\\n\\nTotal cell number Cumulative cell volume Cumulative cell area\\n(patch B epidermis) (patch B epidermis) (patch B epidermis)\\n\\n(0.75, 1.00]\\n\\n0.75, 1.00] (0.75, 1.00)\\n\\n(0.50, 0.75)\\n\\n0.50, 0.75]\\n\\no.50, 0.75]\\n\\n(patch B)\\n\\na\\nad\\nog\\nox]\\noO\\n2\\n\\n{0.25, 0.50]\\n\\n10.25, 0.50) (0.25, 0.50)\\n\\n0.00, 0.25)\\n\\nNormalized PD coordinate intervals\\n\\nNormalized PD coordinate intervals\\n\\n0.00, 0.25]\\n\\n[o.00, 0.25)\\n\\nstage 2\\n\\nen cage mm stage 2101 mm Stage 2410\\nSage 2 SS sna sagen\\nco as~CSOS”S*~«saSS*“‘iaSCiS SCC SSC i 7 > 3 Pa 3 5 z 7 re %\\nTotal cell number (patch B) Rescaled cell volume (patch B) Rescaled cell area (patch B)\\nMm Stage 2-IIl\\nl@@™ Stage 2-IV\\n\\nmm Stage 2-V\\n',\n",
|
||
" 'import cooltools. Lib. plotting\\n\\nvmax = 5000\\nnorm = LogNorm(vmin=1, vmax=100_000)\\nfruitpunch = sns.blend_palette([\\'white\\', ‘red\\'], as_cmap=True)\\n\\nf, axs = plt.subplots(\\nfigsize=(13, 10),\\n\\nncols=2,\\nsharex=True, sharey=True)\\n\\nax = axs[@, 0]\\n\\nax.set_title( \"Pumpkin Spice\")\\n\\nim = ax.matshow(clr.matrix(balanc\\nplt.colorbar(im, ax=ax ,fraction=0.046, pad=0.04, label=\"counts (Linear) \\');\\nplt.xticks(chromstarts, cLr.chromnames) ;\\n\\nax = axs[@, 1\\n\\nax.set_title( ‘Fruit Punch\")\\n\\n4im3 = ax.matshow(clr.matrix(balance=False) [:], vmax=vmax, cnap=fruitpunch) ;\\nplt.colorbar(im3, ax=ax, fraction=0.046, pad=0.04, label=\"counts (Linear)\\');\\nplt.xticks(chromstarts, cLr.chromnames) ;\\n\\nax = axs[1, 0]\\n\\nim = ax.matshow(clr.matrix(balance=False) [:], normenorm, cmap=\\'fall\\'\\nplt.colorbar(im, ax=ax ,fraction=0.046, pad=0.04, label=\\'counts (1og)\\')\\nplt.xticks(chromstarts, cLr.chromnames) ;\\n\\nax = axs[1, 1)\\nm3 = ax.matshow(clr.matrix(balance=False)\\nplt.colorbar(in3, ax=ax, fraction=0.046, pa\\nplt.xticks (chromstarts, clr.chromnanes) ;\\n\\n1, normenorm, cnap=fruitpunch)\\n04, label=\"counts (10g)\"\\n\\nplt.tight_layout()\\n5] @ 00s\\n\\nInport€rror Traceback (most recent call last)\\nFile ~/anaconda3/envs/cool_notebook/Lib/python3. 10/site-packages/cooltools/Lib/plotting.py:6\\n5 try:\\n\\n\\\\\\n\\n& from matplotlib.cm import register_cnap\\n7 except InportError:\\n\\nImportError: cannot import name \\'register_cmap\\' from \\'matplotlib.cm\\' (/usr/users/papantonis1/anaconda3/envs/cool_notebook/ Lib/python3. 10/site-packages/matplotlib/cm.py)\\n\\nDuring handling of the above exception, another exception occurred:\\n\\nModuleNotFoundError Traceback (most recent call last)\\nCell Inf5}, line 1\\n---> 1 import cooltools. Lib.plotting\\n\\n3 vmax = 5\\n\\n4 norm = LogNorm(vmin=1, vmax-\\n\\nFile ~/anaconda3/envs/cool_notebook/Lib/python3. 10/site-packages/cooltools/Lib/plotting.py:8\\n\\n& from matplotlib.cm import register_cnap\\n7 except InportError:\\n--> 8 from matplotlib.colormaps import register\\n\\n10 import matplotlib as mpl\\n11 import matplotlib.pyplot as plt\\n\\nModuleNotFoundError: No module named ‘matplotlib.colormaps’\\n\\nimport matplotlib\\nprint (matplotlib.__version.\\n\\n4] ¥ 00s\\n\\n)\\n\\n3.10.1\\n',\n",
|
||
" 'for i in {1..10}; do\\necho \"Processing chromosome $i\"\\n\\noutput_file=\"${output_dir}/${i}_${i}_matrix. txt\"\\njava -jar /home/aman/juicer/CPU/common/juicer_tools.2.20.0@.jar dump observed NONE /mnt/storage3/aman/hicpro2juicebox/data.allValidPairs.hic \"$i\" \"$i\" BP 25000 > /mnt/storage3/aman/jdump/${i}_${i}_matrix. txt\\n\\necho \"Juicer dump done\"\\ndone\\n\\n[16]\\n\\nProcessing chromosome 1\\nJuicer dump done\\n',\n",
|
||
" 'In [35]: # Compare to gene names in the object\\npresent_features <- features[features %in% rownames(zehn_s)]\\nmissing_features <- features[! features %in% rownames(zehn_s)]\\n\\ncat(\"@ Present genes:\\\\n\")\\nprint(present_features)\\n\\ncat(\"\\\\nX Missing genes:\\\\n\")\\nprint (missing_features)\\n\\nPresent genes:\\nnamed character(Q)\\n\\nX Missing genes:\\nB\\n\\nB MM\\n\\n\"Ms4a1\" \"Cd19\" \"Cd14\"\\nMM MM MM\\n\"Cst3\" \"H2-Aa\" \"Ly6d\"\\nMphase Mphase Sphase\\n\"Birc5\" \"Mki67\" \"Pcna\"\\nT T T\\n\\n\"Cq4\" \"Cq3g\" \"Cq3e\"\\n\\nMM\\n\\n\"Lyz2\"\\nrRNA\\n\"AY0@36118\"\\nSphase\\n\"Mcm3\"\\n\\nT\\n\\n\"Cd3d\"\\n\\nMM\\n\"Fogr3\"\\nrRNA\\n\"Gm42418\"\\nSphase\\n\"Ccne2\"\\n\\nMM\\n\"Ms4a7\"\\nMphase\\n\"Cenpa\"\\nT\\n\"Cd8b\"\\n\\nMM\\n\"Fcoer1g\"\\nMphase\\n\"Ccnb2\"\\nT\\n\\n\"Cd8a\"\\n',\n",
|
||
" 'A Sequenced ignment and\\nHi-C Reads Chimera Handling\\nRI Re\\na —————I\\n\\nDuplicate\\nMerge Sort removal\\n\\nMap creation\\n\\n_\\n\\n',\n",
|
||
" '@FastQC Report\\n\\nSummary\\n\\nQbasic Statistics\\n\\nOber base sequence quality\\n\\nOber sequence quality scores\\nOber base sequence content\\nOQer sequence GC content\\nOber base N content\\nOsequence Length Distribution\\nOsequence Duplication Levels\\nQ overrepresented sequences\\nQoaaapter Content\\n\\nTue 8 Oct 2024\\n\\n5_merged_2_paired.fastq\\n\\nOper base sequence content\\n\\n100\\n\\n90\\n\\n80\\n\\n70\\n\\n60\\n\\n50\\n\\n40\\n\\n30\\n\\n20\\n\\n10\\n\\n123456789\\n\\nSequence content across all bases\\n\\n11 $13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51 53 55 57 59 61 63 65\\nPosition in read (bp)\\n\\n%T\\n%C\\n\\n%G\\n',\n",
|
||
" 'Tools Description\\n\\nsmooth Gaussian kernel density\\nestimation higher accuracy and\\nsensitivity\\n\\nF-seq\\n',\n",
|
||
" \"[19]: zcat FitHiC.spline_pass1.res20000.significances.txt.gz | awk '($1 ~ /*[1-9]$|*10$/ || $1 ~ /*B73V4_ctg[1-9]$|*B73V4_ctg10$/) && \\\\\\n($3 ~ /*[1-91$|*10$/ || $3 ~ /*B73V4_ctg[1-9]1$|*B73V4_ctg10$/)' | head\\n\\n1 10000 861 30000 861 1. 000000e+00 1.000000e+00 1. 000000e+00 1. 000000e+00 26.440884\\n1 10000 861 2470000 1 3.096889e-01 1.000000e+00 1. 000000e+00 1. 000000e+00 @.370613\\n1 10000 861 3070000 1 2.609742e-01 1.000000e+00 1. 000000e+00 1. 000000e+00 @.302423\\n1 10000 861 3130000 1 2.569911e-01 1.000000e+00 1. 000000e+00 1. 000000e+00 Q@.297047\\n1 10000 861 6270000 1 1.502626e-01 1.000000e+00 1. 000000e+00 1. 000000e+00 @.162828\\n1 10000 861 7470000 1 1.350215e-01 1.000000e+00 1. 000000e+00 1. 000000e+00 @.145051\\n1 10000 861 8790000 1 1.343277e-@1 1.000000e+00 1. 000000e+00 1. 000000e+00 @.144249\\n1 10000 861 20830000 1 8.989326e-02 1. 000000e+00 1. 000000e+00 1.000000e+00 @.094193\\n1 10000 861 24010000 1 6.377743e-02 1. 000000e+00 1. 000000e+00 1.000000e+00 @.065902\\n1 10000 861 27510000 1 5.066134e-02 1. 000000e+00 1. 000000e+00 1.000000e+00 @.051990\\n\\n\",\n",
|
||
" 'i! /bin/bash\\ncheckMakeDirectory(){\\necho -e \"checking directory: $1\"\\nif [ ! -e \"$1\" ]; then\\necho -e \"\\\\tmakedir $1\"\\nmkdir -p \"$1\"\\nfi\\n}\\n\\nchromList=\"20 21 22 \"\\nresolutions=\"5000\"\\nDPATH=\"/path/of/frequence_matrix/\"\\nsave_path=$(dirname \"SDPATH\")/chr_all_sample\\nmkdir -p \"$save_path\"\\nmatrix_size=21\\nfor resolution in $resolutions; do\\necho $resolution\\ndisplay_reso=$((resolution / 100@@))\\nfor chrom in $chromList; do\\necho $chrom\\npython chr_all_sample.py ${DPATH}KR_matrix_${display_reso}kb.chr$chrom ${save_path}/chr${chrom}_matrixsize${matrix_size}_tmp.npy $matrix_size ${display_reso}\\n\\npython control_contact.py ${save_path}/chr${chrom}_matrixsize${matrix_size}_tmp.npy ${save_path}/chr${chrom}_matrixsize${matrix_size}.npy\\ndone\\n\\ndone\\n',\n",
|
||
" 'library(\"pheatmap”)\\n\\nselect <- order (ronMeans (counts (ddsHTSeq,normalized=TRUE)),\\ndecreasing=TRUE) [1:50]\\n\\ndf <- as.data. frame(colData(ddsHTSeq) (\"condition”))\\n\\npheatmap(assay(ntd)[select,], cluster_rows=FALSE, show_rownames=FALSE,\\nCluster_cols=TRUE, annotation_col=df)\\n\\npheatmap(assay(vsd)[select,], cluster_rows-FALSE, show_rownames=FALSE,\\nCluster_cols=TRUE, annotation_col=df)\\n\\npheatmap(assay(rld)[(select,], cluster_rows-FALSE, show_rownames@FALSE,\\nCluster_cols=TRUE, annotation_col=df)\\n\\nsamplevists <- dist(t(assay(vsd)))\\n',\n",
|
||
" 'E 3D cell type max quantitative\\nraw image prediction segmentation labeling analysis\\n\\n',\n",
|
||
" 'Figure 1 MSMC locally infers branch lengths a Recombination\\n\\nand coalescence times from observed\\n\\nmutations. (a) Schematic of the model. Total branch length T Time\\nLocal genealogies change along the sequences (past)\\nby recombination events that rejoin branches of First coalescence t\\n\\nthe tree, according to the SMC’ model®®. (hidden state) %\\nThe pattern of mutations depends on the %\\n\\ngenealogy, with few mutations on branches % a SS\\nwith recent coalescences and more mutations\\nin deeper branches. The hidden states of the\\nmodel are the time to the first coalescence and\\nthe identity of the two sequences participating\\nin the first coalescence. (b) MSMC can locally\\ninfer its hidden states, shown by the posterior\\nprobability with color. In black, we plot the\\nfirst coalescence time as generated by the\\nsimulation. This local inference works well\\n\\nfor two, four and eight haplotypes. As more 300\\nhaplotypes are used, the typical time to the Position (kb)\\nfirst coalescence event decreases, whereas the 4 haplotypes\\ntypical segment length increases.\\n\\ncs\\n\\nLog\\n\\nFirst coalescence fy...\\n\\nof the sample size (M), <t> = 2/(M(M — 1)), in\\nunits of 2No generations (Fig. 1b and Online\\nMethods), where No is the long-term average\\neffective population size. Here we demonstrate\\n\\n0 200 400 600 800 1,000 1,200 1,400\\napplication of our model on up to 8 haplotypes, Position (kb)\\nwhich allows us to study changes in popula- 8 haplotypes\\ntion size occurring as recently as 70 genera- 0.15\\ntions ago. As a special case of MSMC for two\\nhaplotypes, we provide a new implementation\\nof PSMC that we call PSMC’ because it uses\\nthe SMC’ model, which accounts for recombi-\\nnation events between segments with the same\\ntime to coalescence®. PSMC’ accurately esti- 500 1,000 1,500 2,000 2,500\\nmates the recombination rate (Supplementary Position (kb)\\nFig. 1), which is not the case for PSMC’.\\n\\nFirst coalescence tj,.\\n\\nS\\no\\n\\n0.05\\n\\nFirst coalescence tj...\\n\\nAyiqeqosd 10N0}s0q\\n',\n",
|
||
" \"aa\\n\\n[19]: #only extracting the alternate allele count @ tv i Fe\\n\\n#By selecting only the alternate allele counts, the genotypes are converted into a simple numerical coding: @ for homozygous reference (0/0),\\ngn = gt.to_allele_counts()[:, :, 1]\\n\\ncoords, model = allel.pca(\\ngn,\\nn_components=10,\\nscaler='patterson',\\nploidy=2\\n\\nimport matplotlib.pyplot as plt\\nplt.scatter(coords[:, @], coords[:, 1], alpha=0.7)\\nplt.xlabel('PC1')\\n\\nplt.ylabel('PC2')\\n\\nplt.title('PCA of Genotype Data')\\n\\nplt.show()\\n\\nMatplotlib is building the font cache; this may take a moment.\\n\",\n",
|
||
" '(21]\\n\\n(1\\n\\nchmod +x run_spades.sh\\n\\n./run_spades.sh\\n\\n',\n",
|
||
" 'A Sequenced Alignment and Duplicate Map creation\\nHi-C Reads Chimera Handling Merge Sort removal\\nRI R2 RARA\\ni 1 — =~ —————— = .\\nSS .\\n; == Se = I\\nC ‘t 1 ———— a =\"\\ness I | =\\n: p 1 }\\n+ —v ===. >} 1 7 , 1\\n',\n",
|
||
" 'Ww PICO 5.09\\n\\nOS aman — nano ./Downloads/assignment/Ecoli_nano/Ecoli_nano_genome.gff — 208x63\\n\\nnment/Ecoli_nano/Ecoli_nano_genome.gff\\n\\nFile: ./Downloads/as\\n\\ni#gff-version 3\\n##sequence-region tig@@000002 1 4646496\\n\\ntige00e0002 Prodigal:002006 CDS 42 611 + (7) ID=IKAOHOFJ_00001;Name=cvrA_1;db_xref=COG:C0G3263;gene=cvrA_1;inference=ab initio prediction:Prodigal:002006,similar to AA sequ$\\ntige00e0002 Prodigal:002006 CDS 797 946 - (7) ID=IKAOHOFJ_00002;inference=ab initio prediction: Prodigal : 002006; locus_tag=IKAOHOFJ_00002;product=hypothetical protein\\ntige00e0002 Prodigal:002006 CDS 1098 1257 - (7) ID=IKAOHOFJ_00003; eC_number=5.1.1.1;inference=ab initio prediction: Prodigal:002006,protein motif:HAMAP:MF_@1201; locus_tag=IKAOH$\\ntigee0e0002 Prodigal:002006 CDS 1257 2117 - (7) ID=IKAOHOFJ_00004; eC_number=5.1.1.1;Name=dadX_1;db_xref=COG:C0G0787; gene=dadX_1;inference=ab initio prediction:Prodigal:002006,$\\ntige0000002 Prodigal:002006 CDS 2127 3248 - (7) ID=IKAOHOFJ_00005; eC_number=1.4.99.-—;Name=dadA_1;db_xref=COG:C0G0@665; gene=dadA_1;inference=ab initio prediction:Prodigal:002006$\\ntige0000002 Prodigal:002006 CDS 3239 3424 - (7) ID=IKAOHOFJ_00006; eC_number=1.4.99.-—;Name=dadA_2;db_xref=COG:C0G0665; gene=dadA_2;inference=ab initio prediction:Prodigal:002006$\\ntige0000002 Prodigal:002006 CDS 3754 5178 + (7) ID=IKAOHOFJ_00007;inference=ab initio prediction: Prodigal : 002006; locus_tag=IKAOHOFJ_00007;product=hypothetical protein\\ntige00e0002 Prodigal:002006 CDS 5337 5966 - (7) ID=IKAOHOFJ_00008; Name=fadR_1;db_xref=COG:C0G2186; gene=fadR_1;inference=ab initio prediction:Prodigal:002006,similar to AA sequ$\\ntige00e0002 Prodigal:002006 CDS 5936 6055 - (7) ID=IKAOHOFJ_00009 ; Name=fadR_2;db_xref=COG:C0G2186; gene=fadR_2;inference=ab initio prediction:Prodigal:002006,similar to AA sequ$\\ntige00e0002 Prodigal:002006 CDS 6302 7816 + (7) ID=IKAOHOFJ_@0010 ; Name=nhaB_1; db_xref=COG:C0G3067; gene=nhaB_1;inference=ab initio prediction:Prodigal:002006,similar to AA sequ$\\ntige00e0002 Prodigal:002006 CDS 7962 8492 + (7) ID=IKAOHOFJ_0@0011;Name=dsbB_1;db_xref=COG:C0G1495; gene=dsbB_1;inference=ab initio prediction:Prodigal:002006,similar to AA sequ$\\ntigee0e0002 Prodigal:002006 CDS 8538 8687 - (7) ID=IKAOHOFJ_00012 ; Name=umuC_1; db_xref=COG:C0G@389; gene=umuC_1;inference=ab initio prediction:Prodigal:002006,similar to AA sequ$\\ntige0000002 Prodigal:002006 CDS 8687 9553 - (7) ID=IKAOHOFJ_00013 ; Name=umuC_2;db_xref=COG:C0G@389; gene=umuC_2;inference=ab initio prediction:Prodigal:002006,similar to AA sequ$\\ntige0000002 Prodigal:002006 CDS 9565 9804 - (7) ID=IKAOHOFJ_00014; eC_number=2.7.7.7;Name=dinB_1;gene=dinB_1;inference=ab initio prediction:Prodigal:002006,protein motif :HAMAP:$\\ntige0000002 Prodigal:002006 CDS 9804 10223 - (7) ID=IKAOHOFJ_00015; eC_number=3.4.21.-—;Name=umuD; db_xref=COG:C0G1974; gene=umuD; inference=ab initio prediction:Prodigal: 002006, sim$\\ntige00e0002 Prodigal:002006 CDS 10596 11507 + (7) ID=IKAOHOFJ_00016;Name=hlyE;gene=hlyE;inference=ab initio prediction:Prodigal:002006,similar to AA sequence:UniProtKB:P77335;1lo$\\ntige00e0002 Prodigal:002006 CDS 11714 12160 - (7) ID=IKAOHOFJ_00017;inference=ab initio prediction: Prodigal : 002006; locus_tag=IKAOHOFJ_00017;product=hypothetical protein\\ntige00e0002 Prodigal:002006 CDS 12252 12911 - (7) ID=IKAOHOFJ_00018 ; Name=ycgM; db_xref=COG:C0G0179; gene=ycgM; inference=ab initio prediction:Prodigal:002006,similar to AA sequence$\\ntige00e0002 Prodigal:002006 CDS 12983 13276 - (7) ID=IKAOHOFJ_00019 ; Name=ycgL ; db_xref=COG:C0G3100;gene=ycgL;inference=ab initio prediction:Prodigal:002006,similar to AA sequence$\\ntigee0e0002 Prodigal:002006 CDS 13517 13999 + (7) ID=IKAOHOFJ_00020;inference=ab initio prediction: Prodigal : 002006; locus_tag=IKAOHOFJ_00020;product=hypothetical protein\\ntige0000002 Prodigal:002006 CDS 14020 14388 - (7) ID=IKAOHOFJ_00021;inference=ab initio prediction: Prodigal: 002006; locus_tag=IKAOHOFJ_00021;product=hypothetical protein\\ntige0000002 Prodigal:002006 CDS 14908 15603 + (7) ID=IKAOHOFJ_00022;Name=minC; db_xref=COG:C0G0850; gene=minC;inference=ab initio prediction:Prodigal:002006,similar to AA sequence$\\ntige0000002 Prodigal:002006 CDS 15627 16439 + (7) ID=IKAOHOFJ_00023; Name=minD; db_xref=COG:C0G2894; gene=minD; inference=ab initio prediction:Prodigal:002006,similar to AA sequence$\\ntige00e0002 Prodigal:002006 CDS 16443 16709 + (7) ID=IKAOHOFJ_00024;Name=minE ; db_xref=COG:C0G0851; gene=minE;inference=ab initio prediction:Prodigal:002006,similar to AA sequence$\\ntige00e0002 Prodigal:002006 CDS 17823 17996 + (7) ID=IKAOHOFJ_00025;inference=ab initio prediction: Prodigal: 002006; locus_tag=IKAOHOFJ_00025;product=hypothetical protein\\ntige00e0002 Prodigal:002006 CDS 18058 18342 + (7) ID=IKAOHOFJ_00026;inference=ab initio prediction:Prodigal:002006,similar to AA sequence:UniProtKB:Q7DFV3; locus_tag=IKAOHOFJ_000$\\ntige00e0002 Prodigal:002006 CDS 18352 18681 + (7) ID=IKAOHOFJ_00027 ; Name=ymgD; gene=ymgD; inference=ab initio prediction:Prodigal:002006,similar to AA sequence:UniProtKB:P@AB46;1lo$\\ntigee0e0002 Prodigal:002006 CDS 18764 19780 - (7) ID=IKAOHOFJ_00028;Name=icsA_1;db_xref=COG:C0G3468; gene=icsA_1;inference=ab initio prediction:Prodigal:002006,similar to AA sequ$\\ntige0000002 Prodigal:002006 CDS 19865 21403 - (7) ID=IKAOHOFJ_00029;inference=ab initio prediction: Prodigal: 002006; locus_tag=IKAOHOFJ_00029;product=hypothetical protein\\ntige0000002 Prodigal:002006 CDS 21784 22002 - (7) ID=IKAOHOFJ_00030;Name=ymgF ; gene=ymgF; inference=ab initio prediction:Prodigal:002006,similar to AA sequence:UniProtKB:P58034;1lo$\\ntige0000002 Prodigal:002006 CDS 22134 23657 - (7) ID=IKAOHOFJ_00031; eC_number=3.1.4.52;Name=pdeG; gene=pdeG;inference=ab initio prediction:Prodigal:002006,similar to AA sequence:$\\ntige00e0002 Prodigal:002006 CDS 23989 24237 - (7) ID=IKAOHOFJ_00032;Name=ymgC; gene=ymgC; inference=ab initio prediction:Prodigal:002006,similar to AA sequence:UniProtKB:P75994;1lo$\\ntige00e0002 Prodigal:002006 CDS 24350 24616 - (7) ID=IKAOHOFJ_00033;Name=ariR; gene=ariR; inference=ab initio prediction:Prodigal:002006,similar to AA sequence:UniProtKB:P75993;1lo$\\ntige00e0002 Prodigal:002006 CDS 24960 25196 - (7) ID=IKAOHOFJ_00034;Name=ycgZ; gene=ycgZ;inference=ab initio prediction:Prodigal:002006,similar to AA sequence:UniProtKB:P75991;1lo$\\ntige00e0002 Prodigal:002006 CDS 25510 26721 + (7) ID=IKAOHOFJ_00035;Name=bluF;gene=bluF;inference=ab initio prediction:Prodigal:002006,similar to AA sequence:UniProtKB:P75990;1lo$\\ntigee0e0002 Prodigal:002006 CDS 26926 27657 + (7) ID=IKAOHOFJ_00036;Name=b1uR; db_xref=COG:C0G0789; gene=bluR; inference=ab initio prediction:Prodigal:002006,similar to AA sequence$\\ntige0000002 Prodigal:002006 CDS 27878 28282 + (7) ID=IKAOHOFJ_00037 ; Name=ydf0_1; db_xref=COG:C0G5562; gene=ydf0_1;inference=ab initio prediction:Prodigal:002006,similar to AA sequ$\\ntige0000002 Prodigal:002006 CDS 29407 29571 - (7) ID=IKAOHOFJ_00038; eC_number=1.1.1.42;Name=icd_1;db_xref=COG:C0G0538; gene=icd_1;inference=ab initio prediction:Prodigal:002006,s$\\ntige0000002 Prodigal:002006 CDS 29805 30059 - (7) ID=IKAOHOFJ_00039; eC_number=3.1.21.-—;Name=mcrA_1;db_xref=COG:C0G1403; gene=mcrA_1;inference=ab initio prediction:Prodigal:002006$\\ntige00e0002 Prodigal:002006 CDS 30263 30637 - (7) ID=IKAOHOFJ_00040; eC_number=3.1.21.-;Name=mcrA_2;db_xref=COG:C0G1403; gene=mcrA_2;inference=ab initio prediction:Prodigal:002006$\\ntige00e0002 Prodigal:002006 CDS 30744 31298 - (7) ID=IKAOHOFJ_00041;Name=hin; db_xref=COG:C0G1961; gene=hin;inference=ab initio prediction:Prodigal:002006,similar to AA sequence:U$\\ntige00e0002 Prodigal:002006 CDS 31327 31668 + (7) ID=IKAOHOFJ_00042;inference=ab initio prediction: Prodigal : 002006; locus_tag=IKAOHOFJ_00042;product=hypothetical protein\\ntige00e0002 Prodigal:002006 CDS 31743 32117 + (7) ID=IKAOHOFJ_00043;inference=ab initio prediction: Prodigal : 002006; locus_tag=IKAOHOFJ_00043;product=hypothetical protein\\ntigee0e0002 Prodigal:002006 CDS 32089 32691 - (7) ID=IKAOHOFJ_00044;Name=tfaE_1;gene=tfaE_1;inference=ab initio prediction:Prodigal:002006,similar to AA sequence:UniProtKB:P0915$\\ntige0000002 Prodigal:002006 CDS 32691 33479 - (7) ID=IKAOHOFJ_00045;inference=ab initio prediction: Prodigal: 002006; locus_tag=IKAOHOFJ_00045;product=hypothetical protein\\ntige0000002 Prodigal:002006 CDS 33483 34067 - (7) ID=IKAOHOFJ_00046;inference=ab initio prediction: Prodigal : 002006; locus_tag=IKAOHOFJ_00046;product=hypothetical protein\\ntige0000002 Prodigal:002006 CDS 34058 34807 - (7) ID=IKAOHOFJ_00047;inference=ab initio prediction: Prodigal: 002006; locus_tag=IKAOHOFJ_00047;product=hypothetical protein\\ntige00e0002 Prodigal:002006 CDS 34776 35249 - (7) ID=IKAOHOFJ_00048;inference=ab initio prediction: Prodigal: 002006; locus_tag=IKAOHOFJ_00048;product=hypothetical protein\\ntige00e0002 Prodigal:002006 CDS 35215 35430 - (7) ID=IKAOHOFJ_00049;inference=ab initio prediction: Prodigal : 002006; locus_tag=IKAOHOFJ_00049;product=hypothetical protein\\ntige00e0002 Prodigal:002006 CDS 35442 35900 - (7) ID=IKAOHOFJ_00050;inference=ab initio prediction: Prodigal : 002006; locus_tag=IKAOHOFJ_00050;product=hypothetical protein\\ntige00e0002 Prodigal:002006 CDS 35888 36808 - (7) ID=IKAOHOFJ_00051;inference=ab initio prediction: Prodigal : 002006; locus_tag=IKAOHOFJ_00051;product=hypothetical protein\\ntigee0e0002 Prodigal:002006 CDS 36818 37156 - (7) ID=IKAOHOFJ_00052;inference=ab initio prediction: Prodigal : 002006; locus_tag=IKAOHOFJ_00052;product=hypothetical protein\\ntige0000002 Prodigal:002006 CDS 37153 37710 - (7) ID=IKAOHOFJ_00053;inference=ab initio prediction: Prodigal : 002006; locus_tag=IKAOHOFJ_00053;product=hypothetical protein\\ntige0000002 Prodigal:002006 CDS 37754 37954 - (7) ID=IKAOHOFJ_00054;inference=ab initio prediction: Prodigal : 002006; locus_tag=IKAOHOFJ_00054;product=hypothetical protein\\ntige0000002 Prodigal:002006 CDS 38045 38719 + (7) ID=IKAOHOFJ_00055; eC_number=3.4.21.88;Name=lexA_1;gene=lexA_1;inference=ab initio prediction:Prodigal:002006,protein motif :HAMA$\\ntige00e0002 Prodigal:002006 CDS 38894 39202 + (7) ID=IKAOHOFJ_00056;inference=ab initio prediction: Prodigal : 002006; locus_tag=IKAOHOFJ_00056;product=hypothetical protein\\n\\nWie) Get Help\\nWed Exit\\n\\nWe) WriteOut\\nWe) Justify\\n\\ni} Read File\\nWil) Where is\\n\\nbad Prev Pg Wag Cut Text wie Cur Pos\\nWA) Next Pg wig) UnCut Text Way To Spell\\n',\n",
|
||
" 'Research Experience\\n(1 Project title/Research Area\\nInstitution, City, Country\\n\\nStart, end and duration\\nSupervisor\\n\\nType\\n\\nTechnical Skills Acquired\\n\\nSupervisor will provide reference\\n\\nsingle cell RNA-seq analysis of lung tumor cells,\\nTechnical University of Munich, Freising, Germany\\n\\n2025-03-10 until 2025-05-12 (Duration: 0.2 years)\\n\\nDr. Dietmar Zehn\\n\\nPart-time\\n\\nIntro to pipelines - Seurat, scRepertoire, Differential gene expression study,\\nClustering & Cell type annotation\\n\\n@ Project title/Research Area\\n\\nInstitution, City, Country\\nStart, end and duration\\nSupervisor\\n\\nType\\n\\nTechnical Skills Acquired\\nSupervisor will provide reference\\n\\n‘Analyzing Fusarium graminareum population structure, genetic diversity and\\nlinkage disequillibrium\\n\\nTechnical University of Munich, Freising, Germany\\n\\n2025-01-01 until 2025-05-01 (Duration: 0.3 years)\\n\\nDr. Aurelien Tellier\\n\\nPart-time\\n\\nPopulation genetic & linkage disequilibrium analysis, statistical computing\\nyes\\n\\n@) Project title/Research Area\\nInstitution, City, Country\\nStart, end and duration\\nSupervisor\\n\\nType\\n\\nTechnical Skills Acquired\\n\\nSupervisor will provide reference\\n\\n3D Ovule morphogenesis in Arabidopsis Thaliana\\nTechnical University of Munich, Freising, Germany\\n\\n2024-11-01 until 2024-12-06 (Duration: 0.1 years)\\n\\nProf. Dr. Kay Schneitz,\\n\\nFull-time\\n\\nLive imaging confocal microscopy, Quantitative image analysis, Modelling and\\n‘Simulation, Hypothesis testing\\n\\nyes\\n\\n(4 Project titleResearch Area\\nInstitution, City, Country\\nStart, end and duration\\n\\nChromatin loops in Maize Genome\\nTechnical University of Munich, Freising, Germany\\n2024-09-09 until 2025-02-17 (Duration: 0.4 years)\\n\\nSupervisor Dr. Frank Johannes\\n\\nType Full-time\\n\\nTechnical Skills Acquired Hi-C data processing, Loop analysis, Data visualization, Scripting, Format\\n\\nSupervisor will provide reference yes\\n\\nselapp - selection application page 2 of 10 printed 2025-04-06 15:25:31 (CEST) by user#applicant\\nSelection: vbeSpring2025 Applicant-ID: 701258\\n\\nVienna <\\nBioCenter\\n\\nPRO PROGRAMME\\n( Project title/Research Area\\n\\nInstitution, City, Country\\nStart, end and duration\\nSupervisor\\n\\nType\\n\\nTechnical Skills Acquired\\nSupervisor will provide reference\\n\\nApplicant: Aman Shamil Nalakath (aman.nalakath@tum.de)\\n\\n‘Temporal analysis of drought effects on winter wheat nitrogen utilization\\nefficiency in Freising, Bavaria\\n\\nTechnical University of Munich, Freising, Germany\\n\\n2023-10-30 until 2024-02-12 (Duration: 0.3 years)\\n\\nDr. Kang Yu\\n\\nFull-time\\n\\nGIS handling, Data modelling, Statistical computing - R, Google Earth Engine\\n',\n",
|
||
" 'In [105]:\\n\\nIn [83]:\\n\\nIn [84]:\\n\\nIn [85]:\\n\\nIn (106):\\n\\nIn (107):\\n\\nmerged_seurat <- RunPCA(merged_seurat, npcs = 35)\\n\\nWarning message in PrepDR(object = object, features = features, verbose = verbose):\\n“The following 49 features requested have not been scaled (running reduction without them): CXCL8, FCERIG, KIR2DL1,\\nNARCKS, KRT1, RAM2, ILIB, LYZ, CXCL3, ZBTB16, MTX2, KIR2DS4, MAFB, KLRC2, CCNA2, KRT72, ADAM23, PRKCH-ASL, LINCOL2i\\n5, HBAL, GCNT4, SLC41A2,\\'CYBB, ZFY, P2RYi4, ADM, SlisD4, ENSGO0000289826, ZBED3,\\'ZNFI84, MS4A1, CCDC122, CéR4, NCAP\\n\\nG, SMIN43,\\nPCa\\nPositive:\\n\\nNegative:\\n\\nPC_2\\nPositive:\\n\\nNegative:\\n\\nPc_3\\nPositive:\\n\\nNegative:\\n\\nPc_4\\nPositive:\\n\\nNegative:\\n\\nPCS\\nPositive:\\n\\nNegative:\\n\\nUSPSY, LGR4, FOXP3, GRPEL2-AS1, RRAGC-DT, ICAL, DDX3Y, APOLD1, IFIT1, USP46, UTY, CHI3L2, XIST, TXLNGY”\\n\\nHSPAIA, DUSP4, CXCL13, HLA-DRB1, CLS, CD74, HSP9GAA1, RBPJ, RGS1, PMAIPL\\nNKG7, GZMB, PROMI, MYO7A, CRTAM, HSPAIB, HLA-DRA, ITGAE, CBLB, CCL4\\nVCAMI, TUBB, TNFRSF9, BHLHE4®, KRTS6, SNX9, HMGN2, HNGB2, SAMSNI, IKZF3\\nFTH1, EEFIA1, IL7R, PABPC1, RPL34, RPS18, RPS6, TPT1, SELL, RPS12\\n\\nANXAL, KLF2, RPS8, RPL3, RPL32, RPS27, RPS3A, RPL21, RPS13, SESN3\\n\\nRPS29, RPL39, RPL13A, RPS2@, RPS23, LNCRNA-IUR, RPS2, RPL10, RPL9, RPSI4\\n\\n‘Tue, STMN1, TUBA1B, HNGN2, TYMS, MKI67, TOP2A, ACTB, VIM, HMGB2\\n‘ASPM, SMC4, CENPU, ACTG1, PCNA, ANP32B, DUT, CENPF, HMGB1, TMPO\\n\\nH2AZ1, HSPAIB, SMC2, PFN1, H2AZ2, HLA-DRA, CENPE, DEK, HSPAIA, DNAICI\\nMALATi, TALAM1, LINC-PINT, FYN, CBLB, PDE3B, AOAH, RNFI9A, CCL5, CNOT6L\\nTSPYL2, CENIP2, ATXN1, PPPIRI6B, LYST, MAF, SYTL3, SIK3, ATPIB3, PRKCH\\nPARPS, TIN, PYHIN1, RALGAPA1, ITGA4, IKZF2, NEAT1, AKNA, PTPN22, STAT\\n\\nCXCL13, SESN3, LNCRNA-IUR, SELL, SNX9, RBPJ, SNED1, KRT86, MYO7A, ACTNL\\nPDESB, TXNIP, VCAM1, NR3C1, RYR2, NDFIP1, ENTPD1, TUBB, TYMS, LEF1\\n\\nLAYN, \"HYGN2, RALA, ITGAE, GALNT2, HAVCR2, RPL32, CLEC2D, SEC14L1, RPL4\\nHSPAIA, FOS, FOSB, HSPAIB, HSPOAA1, NR4A2, DNAJB1, ZFP36, KLF6, MYADM\\nJUND, NR4A1, NR4A3, CCL4, MCL1, DNAJAL, YPELS, DUSP2, GZMK, HSPAB\\n\\nGNLY, ANXAL, TNFAIP3, HSP9@AB1, RGCC, DUSP1, GFPT2, ZFP36L1, EGR1, ATP183\\n\\nHSPAIA, HSPA1B, HSP9OAA1, CXCL13, ANXA1, TOB1, HSPH1, DNAJB1, RGCC, HSP9@ABI\\nFOS, FOSB, IRS2, TXNIP, BAG3, RBPJ, HSPB1, DNAJA1, TPT1, SESN3\\n\\nRPL34, EEFIA1, LNCRNA-IUR, PHLDA1, PMAIP1, SNX9, MYO7A, GATA3, ANKRD28, RPS18\\n‘Tue, STHN1, GZMK, HYGN2, TYMS, AOAH, TUBAIB, AUTS2, TOP2A, CD74\\n\\nNKI67, ATP1B3, DUSP2, ENC1, HMGB2, PIK3R1, RUNX3, PCNA, MALAT1, ITM2C\\nDKK3,\\'HMGB1, H2AZ2, LOHA, MAF, EPHAS, DNAIC9, PPP2RSC, ATAD2, TALAML\\n\\nGNLY, CCL5, HLA-B, CD74, FTH1, HLA-DRB1, ZFP36, NKG7, CXCL13, GZMB\\nPRF1, IL2RB, FOSL2, ATP1B3, DUSP2, MT-CO1, CREM, RPSi8, DUSP4, GAPDH\\n\\nHLA-DRA, CD52, VIM, CST7, HLA-DPA1, IL32, CRTAM, CCND2, ARLAC, CTSW\\n\\nHSPAIA, HSPA1B, TUBB, MALAT1, HSP9OAA1, TALANI, MKI67, STMN1, TUBA1B, LINC-PINT\\nTOP2A, SMC4, FOSB, DNAIB1, PDE3B, HMGN2, ASPM, TYMS, LINCO1619, HMGB2\\n\\nATAD2, HSPHi, SIK3, EZH2, ARHGAPIS, BACH2, CENPF, DNAJA1, KNL1, SMCHD1\\n\\nmerged_seurat\\n\\n‘An object of class Seurat\\n42872 features across 3483 samples within 2 assays\\n\\nActive assay: SCT (18998 features, 300 variable features)\\n3 layers present: counts, data, scale.data\\n\\n1 other assay present: RNA\\n\\n1 dimensional reduction calculate\\n\\n: pea\\n\\nprint(class(merged_seurat))\\nprint(names(Assays(merged_seurat)))\\n\\nfa) \"s\\nattr(,\\n\\nrat\"\\njackage\"’)\\n\\n[1] \"Seuratobject™\\n\\nNULL\\n\\nprint(merged_seurat {{\"RNA\")])\\nprint(merged_seurat {{\"SCT\")])\\n\\nAssay (v5)\\n\\ndata with 23874 features for 3483 cells\\n\\nFirst 10 features:\\nAIBG, AIBG-AS1, A1CF, A2M, A2M-AS1, A2ML1, A2ML1-AS1, A3GALT2, AAAS,\\n\\nAACS.\\nLayers:\\n\\ncounts.patient3, counts.patientd\\nSCTAssay data with 18998 features for 3483 cells, and 2 SCTModel(s)\\nTop 10 variable features:\\n\\nHSPAIA, HSPAIB, CCL4, GNLY, FOS, CCL4L2, XCL1, CXCL13, HSPA6, IL7R\\n\\nDefaultAssay(merged_seurat) <- \"RNA\"\\n\\nerged_seurat <- RunPCA(merged_seurat, assay = \"RNA\", reduction.name = “pca. cna!)\\n\\nWarning message:\\n\"No layers found matching search pattern provided”\\n\\nError in *PrepDR5()*:\\n\\'\\'No layer matching pattern ‘scale.data\\' not found. Please run ScaleData and retry\\n\\nTraceback:\\n\\n1, RunPCA.Seurat(merged_seurat, assay\\n\\nRNA\", reduction. name\\n\\npea. rna\")\\n\\n2. RunPCA(object = object [[assay]], assay = assay, features = features,\\npcs = nocs, rev.pca = rev.pca, weight.by.var = weight.by.var,\\n',\n",
|
||
" '3.3 SUVRS affects the gene region more than the TE region\\n\\n',\n",
|
||
" 'Binned Normalized Y Coordinate\\n\\nArea Histogram\\n\\n[0.75, 1.00]\\n\\n[0.50, 0.75]\\n\\n[0.25, 0.50]\\n\\n[0.00, 0.25]\\nMm Stage 2-Il\\nMmm Stage 2-IV\\nMm Stage 2-V\\n\\n0 5 10 15 20\\nSum of pool rescaled Area\\n',\n",
|
||
" 'Assigning cell type identity to clusters\\n\\nFortunately in the case of this dataset, we can use canonical markers to easily match the unbiased clustering to known cell types:\\n\\nClusterID Markers Cell Type\\n\\n(e) IL7R, CCR7 Naive CD4+ T\\n1 CD14, LYZ CD14+ Mono\\n\\n2 IL7R, S100A4 Memory CD4+\\n3 MS4A1 B\\n\\n4 CD8A CD8+T\\n\\n5 FCGR3A, MS4A7_ FCGR3A+ Mono\\n6 GNLY, NKG7 NK\\n\\n7 FCER1A, CST3 DC\\n\\n8 PPBP Platelet\\n\\nnew.cluster.ids <- c(\"Naive CD4 T\", \"CD14+ Mono\", \"Memory CD4 T\", \"B\", \"CD8 T\", \"FCGR3A+ Mono\",\\n\"NK\", \"DC\", \"Platelet\")\\n\\nnames(new.cluster.ids) <- levels (pbmc)\\n\\npbmc <- RenameIdents(pbmc, new.cluster. ids)\\n\\nDimPlot(pbmc, reduction = \"umap\", label = TRUE, pt.size = @.5) + NoLegend()\\n',\n",
|
||
" \"for i in xbowtie.vcf; do\\nveffilter -f 'QUAL / AO > 10' $i > $i. filt.vcf\\nvceffilter -f 'QUAL / AO < 10' $i > $i.fail.vcf\\ndone\\n\",\n",
|
||
" 'Patch B\\nepidermis\\n',\n",
|
||
" '[6]: import pandas as pd BrvVeg?ae\\nimport matplotlib.pyplot as plt\\n\\n# Load the BEDPE file into a pandas DataFrame\\nfile_path = \"/mnt/storage3/aman/wdbasejuicer_new/hiccups_output/merged_loops. bedpe\"\\ncolumns = [\\n\\nMehra\", \"xa\", \"2\", “chr2\", \"y1\", \"2\",\\n\\n“name”, “score”, \"strandi\", \"strand2\",\\n\\n\"color\", \"observed\", “expectedBL\", “expectedDonut\",\\n\\n“expectedi\", “expectedV\", \"fdrBL\", \"fdrDonut\",\\n\\n“fdrn\", “fdrv\"\\n\\ndata = pd.read_csv(file_path, sep=\"\\\\t\", comment=\\'#\\', names=columns)\\n\\n# Calculate the distance based on the midpoints of upstream and downstream loci\\ndata[\"distance\"] = data.apply(\\nLambda row: abs(((row[\"x1\"] + rowl\"x2\"1) / 2) - ((row[\"y1\"] + row(\"y2\"]) / 2))\\nif row[\"chri\"] == row{\"chr2\"] else None,\\naxis=1\\n\\n# Drop rows with missing distance (interchromosomal loops or invalid rows)\\ndata = data.dropna(subset=(\"distance\"])\\n\\n# Convert distances to integers\\ndata[\"distance\"] = data[\"distance\"].astype( int)\\n\\n# Plot the distances\\nplt.figure(figsize=(10, 6))\\n\\nplt.hist(data[\"distance\"], bins=100, edgecolor=\"black\")\\nplt.title(\"Distribution of Loop Distances from BEDPE File\")\\nplt.xlabel(\"Distance (bp)\")\\nplt.ylabel(\"Frequency\")\\nplt.grid(axis=\"y\", Linestyle=\\'\\nplt.show()\\n\\n—\", alpha=0.7)\\n\\nprint (data[\"distance\"].unique())\\nprint (data.head())\\n\\nDistribution of Loop Distances from BEDPE File\\n\\n0.04\\n0.02\\n>\\nFs\\n2\\n3 0.00\\nS\\n&\\n0.02\\n0.04\\n0.0 0.2 04 0.6 08 10\\nDistance (bp)\\na\\n\\nEmpty DataFrame\\nColumns: [chr1, x1, x2, chr2, yl, y2, name, score, strandi, strand2, color, observed, expectedBL, expectedDonut, expectedH, expectedv, fdrBL,\\nfdrDonut, fdrH, fdrv, distance]\\n\\nIndex: []\\n\\n[0 rows x 21 columns]\\n',\n",
|
||
" 'In [122]: head(combined_seurat@meta. data)\\n\\nA data.frame: 6 x 8\\n\\norig.ident nCount_RNA nFeature_RNA percent.mt nCount_SCT nFeature SCT integrated_snn_res.0.5 seurat_clusters\\n\\n<chr> <dbi> <int> <dbi> <dbi> <int> <fct> <fct>\\n\\n4100 zehn_dataset 23935, 5941 4.904951 1729 1148 4 4\\n11356 zehn_dataset 1299 836 4.080062 1489 835 1 1\\n21277 = zehn_dataset 2243 1335 6.776638 2090 1332 2 2\\n30102 zehn_dataset 2860 1681 5.524476 2205 1654 4 4\\n41586 zehn_dataset 1933, 1123 7.604759 1908 1121 1 1\\n41975 zehn_dataset 925 607 9.081081 1474 619 3 3\\n',\n",
|
||
" '(14]\\n\\n[15]\\n\\n{17]\\n\\n[18]\\n\\n[19]\\n\\n[20]\\n\\n# categorize loops as promoter-promoter, prom-enh, enh-enh\\nv 0.0s\\n\\nimport pandas as pd\\nimport pybedtools\\n\\nv 0.0s\\n\\n# === Load your loops ===\\n\\nall_loops = pd.read_csv(\"/usr/users/papantonis1/aman/microc_project/loop_calling_premade_hic/jupyter_notes/ml_final2_multimodal.csv\", sep=\",\")\\n\\n# Keep the original loop count\\noriginal_loop_count = all_loops.shape[0]\\n\\n=== Load ENCODE cCRE BED ===\\nccres = pybedtools.BedTool(\"/usr/users/papantonis1/aman/gh_promoters\")\\n\\nPratt oops i iFenra™; *Scare *end2\")].copy()\\nal_df.columns = a2_df.columns = [\\'Chromosome\\', \\'Start\\', ‘End\\']\\n\\n=== Convert anchors to BedTool ===\\nal_bed = pybedtools.BedTool. from_dataframe(al_df)\\na2_bed = pybedtools.BedTool. from_dataframe(a2_df)\\n\\na2_bed = a2_bed.sort(\\nccres = ccres.sort()\\n\\n# Intersect with cCREs\\nbed_columns = [\\'name\\', \\'score\\', ‘strand\\', ‘thickStart\\', \\'thickEnd\\',\\n‘itemRgb\\', ‘ccre\\', ‘encodeLabel\\', ‘\\'zScore\\', ‘ucscLabel\\',\\n\\nal_anno = al_bed.closest(ccres, d=True, “ts! first’) /to_dataframe(\\nnames=[\\'chrom\\', \\'start\\', ‘end\\'] + bed_columns + [\\'distance\\']\\n\\n)\\n\\na2_anno = a2_bed.closest(ccres, d=True, t=\\'first\\').to_dataframe(\\n\\nnames=[\\'chrom\\', \\'start\\', ‘end\\'] + bed_columns + [\\'distance\\']\\n)\\nal_anno = al_anno.drop_duplicates(subset=[\\'chrom\\', ‘start\\', ‘end\\'])\\na2_anno = a2_anno.drop_duplicates(subset=[\\'chrom\\', ‘start\\', ‘end\\'])\\nv 0.0s\\n\\nall_loops = all_Loops.merge(\\nal_anno,\\nleft_on=[\\'chri\\', \\'start1\\', ‘end1\\'],\\nright_on=[\\'chrom\\', \\'start\\', ‘end\\'],\\nhow=\\'left\\'\\n\\n).drop(columns=[\\'chrom\\', \\'start\\', ‘end\\'])\\n\\na2_anno,\\nleft_on=[\\'chr2\\', \\'start2\\', ‘end2\\'],\\nright_on=[\\'chrom\\', \\'start\\', ‘end\\'],\\n\\nhow=\\'left\\'\\n).drop(columns=[\\'chrom\\', \\'start\\', ‘end\\'])\\nv 0.1s\\nall_Loops\\nv 0.0s\\nUnnamed: Unnamed: Unnamed: chr1 start1 end1 = chr2 start2 end2 status\\n0 0 te) Q- chr1 1952500 1957500 chr 2042500 2047500 shared\\n2 2 2 2 ~~ chri 2412500 2417500 —chr1 2552500 2557500 = shared\\n3 3) 3 3 chr1 3490000 3495000 chr1 3615000 3620000 _ shared\\n4 4 4 4 chr1 3565000 3570000 chr1 3615000 3620000 _ shared\\n32272 32272 32272 32272 chrY 10945000 10950000 chrY 11290000 11295000 gained\\n32273 32273 32273 32273 chrY 10982500 10987500 chrY 11292500 11297500 gained\\n32275 32275 32275 32275 chrY 11530000 11535000 chrY 11760000 11765000 gained\\n32276 32276 32276 32276 chrY 19432500 19437500 chrY 20742500 20747500 gained\\n32277 rows x 63 columns\\nal = rowl*ancnori_type’ J\\na2 = row[\\'anchor2_type\\']\\nif al == \\'promoter\\' and a2 == \\'promoter\\':\\nreturn \\'promoter-promoter\\'\\nelif al == \\'enhancer\\' and a2 == ‘enhancer\\':\\n\\nreturn ‘enhancer-enhancer\\'\\nelif \\'promoter\\' in [al, a2] and ‘enhancer\\' in [al, a2]:\\n\\nreturn ‘other\\'\\n\\nall_loops[\\'loop_type\\'] = all_loops.apply(get_loop_type, axis=1)\\n® 0.3s\\ntry:\\n—> 3805 return self._engine.get_loc(casted_key)\\n\\n3806 except KeyError as err:\\n\\nFile index.pyx:167, in pandas._libs. index. IndexEngine.get_loc()\\n\\nsarieionel ee ee yey re ee\\n\\nFile pandas/_libs/hashtable_class_helper.pxi:7089, in pandas._libs.hashtable.Py0bjectHashTable.get_item()\\nKeyError: ‘anchori_type\\'\\n\\nThe above exception was the direct cause of the following exception:\\n\\nCell In[20], line 13\\n\\n10 else:\\n11 return ‘other\\'\\n---> 13 all_loops[\\'loop_type\\'] = all_loops.apply(get_loop_type, axis=1)\\n3815 # = InvalidIndexError. Otherwise we fall through and re-raise\\nARIA # the TvneFrror.\\n\\nKeyError: ‘anchori_type\\'\\n\\nOutput is truncated. View as a scrollable element or open in a text editor. Adjust cell output settings...\\n\\nall_Loops\\n\\nUnnamed: Unnamed: Unnamed:\\n\\nthickStart_!\\n\\nNaN\\nNaN\\n\\nNaN\\nNaN\\n\\nNaN\\n\\nNaN\\nNaN\\n\\nNaN\\n\\nthickEnd_y\\n\\nE1_anchor2_ctrl\\n\\nNaN\\nNaN\\nNaN\\nNaN\\n\\n0.2 0.1 chr1 start1 end1 = chr2 start2 end2 status\\n\\n0 0 te) O chr1 1952500 1957500 chr 2042500 2047500 shared\\n\\n1 1 1 1 chr1 2202500 2207500 _—chr1 2382500 2387500 shared\\n\\n2 2 chri 2412500 2417500 chr1 2552500 2557500 shared\\n\\n3 3) 3 3 chr1 3490000 3495000 chr1 3615000 3620000 _ shared\\n32272 32272 32272 32272 chrY 10945000 10950000 chrY 11290000 11295000 gained\\n32273 32273 32273 32273 chrY 10982500 10987500 chrY 11292500 11297500 gained\\n32274 32274 32274 32274 chrY 11292500 11297500 chrY 11722500 11727500 gained\\n32275 32275 32275 32275 chrY 11530000 11535000 chrY 11760000 11765000 gained\\n39976 39976 392976 39976 chrY 19429500 19427500 chrY 20742500 20747500 aained\\n\\n0.270996\\n0.270996\\n0.409095\\n-1.229772\\n0.280603\\n\\nNaN\\nNaN\\n\\nNaN\\nNaN\\n\\nNaN\\n\\nNaN\\nNaN\\n\\nNaN\\n\\nE1_anchor2_rbp1\\n\\nitemRgb_y\\nNaN\\nNaN\\nNaN\\nNaN\\n\\nNaN\\n\\nNaN\\nNaN\\n\\nNaN\\n\\nNaN\\nNaN\\nNaN\\nNaN\\n0.177009\\n0.177009\\n0.332632\\n\\n-1.167283\\n0.539909\\n\\nccre_)\\n\\nNaN\\nNaN\\n\\nNaN\\nNaN\\n\\nNaN\\n\\nNaN\\nNaN\\n\\nNaN\\n\\nencodeLabel_y\\n\\ndelta_E1_anchor1\\n\\nNaN\\nNaN\\nNaN\\nNaN\\n\\n-0.076090\\n-0.221548\\n-0.093987\\n-0.012394\\n-0.009282\\n\\nNaN\\nNaN\\n\\nNaN\\nNaN\\n\\nNaN\\n\\nNaN\\nNaN\\n\\nNaN\\n\\nzScore_|\\n\\nNaN\\nNaN\\n\\nNaN\\nNaN\\n\\nNaN\\n\\nNaN\\nNaN\\n\\nNaN\\n\\ndelta_E1_anchor2\\n\\nNaN\\nNaN\\nNaN\\nNaN\\n\\nPython\\n\\nPython\\n\\nPython\\n\\nDr Du ow\\n\\nPython\\n\\nPython\\n\\nucscLabel_y accessionLabe\\n\\nNaN\\nNaN\\n\\nNaN\\nNaN\\n\\nNaN\\n\\nNaN\\nNaN\\n\\nNaN\\n\\nPython\\n\\nATAC_signal _ status_slop\\n\\n0.078179 shared_2.5kb\\n0.200288 shared_2.5kb\\n0.069768 shared_2.5kb\\n0.050523 shared 2.5kb\\n\\n-0.093987\\n-0.093987\\n-0.076464\\n0.062489\\n0.159306\\n\\n0.019050 gained_2.5kb\\n0.022277 gained_2.5kb\\n0.022359 gained_2.5kb\\n0.019858 gained_2.5kb\\n0.091187 aained 2 5kb\\n',\n",
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" '@ Safari File Edit View History Bookmarks Window Help @&®%OBSB ox zo @ Tue 14. Oct 22:30\\n\\n63\\n\\no-< Co @e © 2 quillbot.com Bae © o& +\\n\\n=) Google Docs & Al Detector - QuillBot Al oe Pastebin.com - #1 past... Humanize cancer biolo... G thesis_final - Grammarly * Download file | iLovePDF Start Page © Grad school experience...\\n\\n© QuillBot PREMIUM Al Detector 3 Apps and Extensio... v i)\\n\\nas H3K27me3, H2AK119u), and inhibition of transcriptional initiation or elongation(51).\\n\\nActivating marks keep chromatin open and facilitate the recruitment of factors such as RNA\\nParaphraser | . a. . . Al Human\\npolymerase II, whereas repressive marks promote compaction by recruiting silencing\\n\\nA .\\n\\nv complexes like PRC1, PRC2, and HP1(1). NAiSPSSUISEIOHIOF OIyCORMIFUNEtION NISFUBES EEESE] Algenerated © © 1%\\nGrammar\\nChecker processes and contributes to tumorigenesis, acting as an epigenetic driver of malignancy Atgenerated & Ab-refined © 1%\\nrp even in the absence of genetic mutations. Histone modifications regulate transcription by Human-written & ALrefined © 0%\\n\\nSA\\n\\nAl Detector creating binding sites for protein complexes(52).\\nHuman-written @ 98%\\nQ\\nPlagiarism Polycomb establishes long-range contacts through H3K27me3-marked loop anchors that\\nChecker connect loci in the same compartments and usually span many megabases, largely\\n@ independent of CTCF and TADs(53). These contacts rely on EZH2 occupancy at RNA-binding Y Understanding your results\\nHumeniver sites of PRC2, which nucleates and spreads H3K27me3 to create anchors for these long\\nchromatin contacts(53). A study on neuronal cells observed that polycomb establishes\\n~. extensive long range interactions, including trans contacts, and unlike CTCF-cohesin loops,\\n> these contacts cluster repressed and bivalent loci into multi-locus networks, creating a\\nni — distinct repressive layer(54). Also polycomb domains can assemble into condensate-like fori\\nGenerator via phase-separating subunits such as CBX2 and Ph, creating hubs that promote chroma Al-generated\\n= compaction and stable repression(55).\\nSummarizer\\neA PRC2 is a core polycomb complex made of EZH1/2, SUZ12, EED, and associated Low\\n\\nTranslate cofactors(56). Its primary function is to catalyze methylation of histone H3 at lysine 27 Confidence\\n99 (Hisk27me1/2/3) thereby establishing transcriptionally repressed chromatin states! The The confidence level indicates how sure our Al Detector i\\n\\nabout the content detected. Lower confidence levels\\n\\nCitation methylation mark is deposited through the complex\\'s catalytic subunits EZH1/2. Within 1 jndicate a greater chance of false positives or false\\nGenerator complex, embryonic ectoderm development or EED is a subunit that binds to H3K27me: \"°82\"\\'¥*>:\\ngE\\nWas this helpful? ite) aD\\n\\nQuiliBot 3,541 Words i) cp @ Analysis\\na\\n\\nQuillBot for Want your text to sound more authentic? Refine with Paraphras... >\\nmac\\n\\nClick here to learn how our Al detector uses Al\\n\\nExcellent 5,832 reviews on > Trustpilot\\n',\n",
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" '@ Vivaldi File Edit View Bookmarks Mail Tools Window Help S®e@vutH @Brt¥ sz @0 O S Wed 4. Jun 14:04\\n\\n(BR = =| Q © 8A docs.google.com/spreadsheets/d/1Puep_w4Y4vZRtkfBr9BvZvOz7VPlxuofjf... W Q B&B 7) wt eS > @ F oo @\\n\\nCluster_access ¥ @& @ ®) Oo &@ a-\\n\\n© Share ~ CO Q)\\nFile Edit View Insert Format Data Tools Extensions Help ri\\n\\nQo @ GF 100% >| £ % O .09 123 | Defaul... ~ - (10) + BIrsA * A Eydivrib~r Ay @ WH DW Y Br = A\\n\\nF79 ~ | fe\\nA B c D E F G H yingta 1of2 a oy > xX ov\\n\\n55 13 Tristan Aretz t.aretz@tum.de\\n56 21 Markus Langst ge47roz@mytum.de\\n57 17 Tuna Seckin ge85fon@mytum.de\\n58 16 Cong Thanh Dang ge42xag@tum.de\\n59 17 Fabian Basso fabian.basso@tum.de\\n60 17 Lara Lechner lara.lechner@tum.de\\n61 16 Puntika Leepagorn go82zur@mytum.de\\n62 16 Perapat Taytad go82voq@mytum.de\\n63 14 Isabel Giray isabel.giray@tum.de\\n64 shahd ibrahim shahd.ibrahim@tum.de\\n65 11 Daphné Baudeu go46ziw@mytum.de\\n66 7 Rebeka Luknarova luk@in.tum.de\\n67 7 Elias Albrecht elias.albrecht@tum.de\\n68 8 Antonia Saile ge72lec@mytum.de\\n69 7 Mathias Nguyen ge28sij@mytum.de\\n70 13 Arina Medvedeva go25zad@mytum.de\\n71 1 Helena Melcher ge63sak@mytum.de\\n72 7 Jean-Pasqual Sindermann jeanpasqual.sindermann@tum.de\\n73 3 Norman Roggendorf ge26jup@mytum.de\\n74 6 Kimia (Zahra) — Ebrahimi kimia.ebrahimi@tum.de\\n7 18 Christoph Albertin ge93jis@mytum.de\\n76 1 Daniel Lochert elizabeth.lochert@tum.de\\n77 10 Aman Nalakath aman.nalakath@tum.de\\n78\\n\" a\\n80 °\\n81\\n82\\n83\\n\\nSheet1 ~\\n\\n= zehn_lab v < Chromatin Loop Interac Giveaway - Plus New10 © thesis_papantonis_lab - a] Pastebin.com - #1 paste Startpage Search Resul Hf Installation — pyranges Cluster_access - Goog\\n',\n",
|
||
" 'UP (Otsu, @ bin): [[13, 209], [7, 215]], OR=1.91, p=2.52e-01\\nDOWN (Otsu, @ bin): [[4, 78], [@, 82]], OR=inf, p=1.20e-01\\nUP (Li, @ bin): [[26, 196], [11, 211]], OR=2.54, p=1.52e-02\\nDOWN (Li, @ bin): [{5, 77], [3, 79]], OR=1.71, p=7.20e-01\\n\\n',\n",
|
||
" \"~*~\\n\\n@ Safari File Edit View History Bookmarks Develop Window Help @ ne) ¥@6© €& @) F Q ® Fri21.Nov 14:15\\neecax m&-< ‘on © {1| @ 2g pax-db.org Ga co Oo +\\n{0.0} Yes. The paper states that t... iG] geckopy/geckopy/experimen... FE] geckopy — geckopy 0.0.1 do... a= PaxDb - Help 2 https://pax-db.org/download... a PaxDb - Download iN} FragPipe workflows | FragPipe\\n\\npaxdb®° PaxDb: Protein Abundance Database\\n\\ndary\\nPio,\\n\\nx protein(s) id/name\\n\\nPaxDb Downloads\\n\\nAccessory files\\n\\ne All datasets can be found here paxdb-abundance-files.zip (~31MB).\\n\\n¢ Protein sequences fasta file can be found here paxdb-protein-sequences.zip (~498MB).\\n¢ Mapped peptides files can be found here paxdb-mapped_peptides.zip (~193MB).\\n\\n¢ Orthologs list can be found here paxdb-orthologs.zip (~25MB).\\n\\ne UniProt mappings can be found here paxdb-uniprot-links.zip (~11MB).\\n\\ne Files from previous PaxDB versions can be found here: /downloads/\\n\\nPer-species abundance files\\n\\nES -\\n\\nSpecies Datasets J?\\nHomo sapiens 375\\nMus musculus 175\\n\\nDOWNLOAD\\n\\nCOMPUTE+;\\n\\nREQUEST+*;\\n\\nDownload\\n\\n9606.zip\\n\\n10090.zip\\n\\nWHAT'S NEW4;\\n\\nHELP\\n\\n\",\n",
|
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" 'In [424]:\\n\\nstr(combined.TCR_p3)\\n\\nList of 1\\n$ P3_S1:\\'data.frame\\': 2471 obs. of 13 variables:\\n\\n»+$ barcode : chr [1:2471] \"P3_S1_AAACCTGAGTACGACG-1\" \"P3_S1_AAACCTGCAACACGCC-1\" \"P3_S1_AAACCTGCAGGCGATA-1\" \"P3_S\\n1_AAACCTGCATGAGCGA-1\" ...\\n\\n++$ sample : chr [1:2471] \"P3_S1\" \"P3_S1\" \"P3_S1\" \"P3_S1\" ...\\n\\n»2$ TCR1 : chr [1:2471] \"TRAV25.TRAJ2@.TRAC\" \"TRAV38-2/DV8.TRAJ52.TRAC\" \"TRAV12-1.TRAJ9.TRAC\" \"TRAV12-1.TRAJ9.\\nTRAC\" ...\\n\\n»+$ cdr3_aal: chr [1:2471] \"CGCSNDYKLSF\" \"CAYRSAQAGGTSYGKLTF\" \"CVVSDNTGGFKTIF\" \"CVVSDNTGGFKTIF\" ...\\n\\n»+$ cdr3_nt1: chr [1:2471] \"TGTGGGTGTTCTAACGACTACAAGCTCAGCTTT\" \"TGTGCTTATAGGAGCGCGCAGGCTGGTGGTACTAGCTATGGAAAGCTGA\\nCATTT\" \"TGTGTGGTCTCCGATAATACTGGAGGCTTCAAAACTATCTTT\" \"TGTGTGGTCTCCGATAATACTGGAGGCTTCAAAACTATCTTT\" ...\\n\\n»2$ TCR2 : chr [1:2471] \"TRBV5-1.None.TRBJ2-7.TRBC2\" \"TRBV10-3.None.TRBJ2-2.TRBC2\" \"TRBV9.None. TRBJ2-2.TRBC2\"\\n“TRBV9.None. TRBJ2-2.TRBC2\" ...\\n\\n»+$ cdr3_aa2: chr [1:2471] \"CASSLTDRTYEQYF\" \"CAISEQGKGELFF\" \"CASSVRRERANTGELFF\" \"CASSVRRERANTGELFF\" ...\\n\\n»+$ cdr3_nt2: chr [1:2471] \"TGCGCCAGCAGCTTGACCGACAGGACCTACGAGCAGTACTTC\" \"TGTGCCATCAGTGAACAGGGGAAAGGGGAGCTGTTTTTT\"\\n\"TGTGCCAGCAGCGTAAGGAGGGAAAGGGCGAACACCGGGGAGCTGTTTTTT\" \"TGTGCCAGCAGCGTAAGGAGGGAAAGGGCGAACACCGGGGAGCTGTTTTTT\" ...\\n\\n»+$ CTgene : chr [1:2471] \"TRAV25.TRAJ20.TRAC_TRBV5-1.None. TRBJ2—7.TRBC2\" \"TRAV38-2/DV8.TRAJ52.TRAC_TRBV10-3.Non\\ne. TRBJ2-2.TRBC2\" \"TRAV12-1. TRAJ9. TRAC_TRBV9.None. TRBJ2—2.TRBC2\" \"TRAV12—1. TRAJ9. TRAC_TRBV9.None. TRBJ2—2.TRBC2\" ...\\n\\n»e$ CTnt : chr [1:2471] \"TGTGGGTGTTCTAACGACTACAAGCTCAGCTTT_TGCGCCAGCAGCTTGACCGACAGGACCTACGAGCAGTACTTC\" \"TGTGCT\\nTATAGGAGCGCGCAGGCTGGTGGTACTAGCTATGGAAAGCTGACATTT_TGTGCCATCAGTGAACAGGGGAAAGGGGAGCTGTTTTTT\" \"TGTGTGGTCTCCGATAATACTGGA\\nGGCTTCAAAACTATCTTT_TGTGCCAGCAGCGTAAGGAGGGAAAGGGCGAACACCGGGGAGCTGTTTTTT\" \"TGTGTGGTCTCCGATAATACTGGAGGCTTCAAAACTATCTTT\\n_TGTGCCAGCAGCGTAAGGAGGGAAAGGGCGAACACCGGGGAGCTGTTTTTT\" ...\\n\\n»+$ CTaa : chr [1:2471] \"CGCSNDYKLSF_CASSLTDRTYEQYF\" \"CAYRSAQAGGTSYGKLTF_CAISEQGKGELFF\" \"CVVSDNTGGFKTIF_CASSVR\\nRERANTGELFF\" \"CVVSDNTGGFKTIF_CASSVRRERANTGELFF\" ...\\n\\n»+$ CTstrict: chr [1:2471] \"TRAV25.TRAJ20. TRAC; TGTGGGTGTTCTAACGACTACAAGCTCAGCTTT_TRBV5-1.None. TRBJ2-7. TRBC2; TGCGC\\nCAGCAGCTTGACCGACAGGACCTACGAGCAGTACTTC\" \"TRAV38-2/DV8.TRAI52. TRAC; TGTGCTTATAGGAGCGCGCAGGCTGGTGGTACTAGCTATGGAAAGCTGAC\\n\\nATTT_TRBV10-3.None. TRBJ2—2.TRBC2;TGT\"| __truncated__ \"TRAV12-1.TRAJ9. TRAC; TGTGTGGTCTCCGATAATACTGGAGGCTTCAAAACTATCTT\\nT_TRBV9.None. TRBJ2—2. TRBC2; TGTGCCAGCAGCGTAAGGAGGGA\" | __truncated__ \"TRAV12—1. TRAJ9. TRAC; TGTGTGGTCTCCGATAATACTGGAGGC\\nTTCAAAACTATCTTT_TRBV9.None. TRBJ2—2. TRBC2 ; TGTGCCAGCAGCGTAAGGAGGGA\" | __truncated_...\\n\\n++$ chain : chr [1:2471] \"TRB\" \"TRB\" \"TRB\" \"TRB\" ...\\n\\n+e- attr(*, \"na.action\")= \\'omit\\' Named int [1:334] 5 27 33 36 47 53 65 66 80 88...\\n+e> attr(*, \"names\")= chr [1:334] \"9\" \"55\" \"66\" \"71\" ...\\n',\n",
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" '(jpn_aman_new) [a.nalakath@frontend ~]$ bcftools view -e \\'N_ALT=1! fg60_biallelic_snps.vcf.gz | grep -v \"4#\" | we -1\\n\\nQ\\n\\n(jpn_aman_new) [a.nalakath@frontend ~]$ bcftools view -e \\'N_ALT=1\\' ~/pop_gen_internship/ | grep -v \"*#\" | we -l\\n\\n-ipynb_checkpoints/ FG_6@.genotyped.SNP.and.INDEL.AN5.HardFilter.snpONLY.vcf pop_gen_internship-Copy1.ipynb\\n\\n(jpn_aman_new) [a.nalakath@frontend ~]$ bcftools view -e \\'N_ALT=1\\' ~/pop_gen_internship/FG_6@.genotyped.SNP.and.INDEL.AN5.HardFilter.snpONLY.vcf | grep -v \"*#\" | we -l\\n\\n18628\\n',\n",
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" 'vy 9 runo70\\n) run070-nsclc-3_RSEC_MolsPerCell_MEX.zip\\nrun070-nsclc-3_VDJ_D...inant_Contigs_AIRR.tsv\\n@, run070-nsclc-3_VDJ_perCell.csv\\nrun070-nsclc-3.h5mu\\nv 0 run071\\n} run071-nscle-4_RSEC_MolsPerCell_MEX.zip\\nrun071-nsclc-4_VDJ_D...inant_Contigs_AIRR.tsv\\nrun071-nsclc-4_VDJ_perCell.csv\\nrun071-nsclc-4.h5mu\\n\\nToday at 08:39\\n\\n25. March 2025 at 15:43\\n25. March 2025 at 16:08\\n25. March 2025 at 16:08\\n25. March 2025 at 16:09\\nToday at 08:41\\n\\n25. March 2025 at 15:46\\n25. March 2025 at 15:46\\n25. March 2025 at 15:46\\n25. March 2025 at 15:51\\n\\n17,6 MB\\n24,1 MB\\n\\n1MB\\n18,6 MB\\n\\n2,3 MB\\n3,1 MB\\n136 KB\\n4,3 MB\\n\\nFolder\\n\\nZIP archive\\n\\nPlain Text\\nComma...et (.csv)\\nDocument\\n\\nFolder\\n\\nZIP archive\\n\\nPlain Text\\nComma...et (.csv)\\nDocument\\n',\n",
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" \"Aman Shamil Nalakath\\nMon 3/3, 1:31 PM\\n\\nDear Dr. Papantonis,\\n\\nThe recommended duration for a thesis as per our program is 6 months, that is once registered, the submission is due within this period.\\nIf it is okay, I'd like to begin at the start of April.\\n\\nBest Regards,\\nAman\\n\",\n",
|
||
" 'In\\n\\nIn\\n\\n[119]:\\n\\n[120]:\\n\\ncombined_TCR <- c(combined.TCR_p3, combined. TCR_p4)\\n\\ncombined_seurat <- combineExpression(\\ncombined_TCR,\\ncombined_seurat,\\ncloneCall = \"strict\",\\nproportion = FALSE\\n)\\n\\nError in combineExpression(combined_TCR, combined_seurat, cloneCall = \"strict\",\\n\\nthere are groupings < 1\\nTraceback:\\n\\n1. stop(\"Adjust the cloneSize parameter - there are groupings < 1\")\\n\\n2. .handleSimpleError(function (cnd)\\n\\n-f\\n\\n. watcher$capture_plot_and_output()\\n\\ncnd <- sanitize_call(cnd)\\n\\nwatcher$push(cnd)\\n\\nswitch(on_error, continue = invokeRestart(\"eval_continue\"),\\n\\nstop = invokeRestart(\"eval_stop\"), error = NULL)\\n\\n. }, \"Adjust the cloneSize parameter - there are groupings < 1\",\\n\\nbase: : quote(combineExpression(combined_TCR, combined_seurat,\\ncloneCall = \"strict\", proportion = FALSE)))\\n\\n: Adjust the cloneSize parameter —\\n',\n",
|
||
" 'df = pd.DataFrame(text_scr)\\n\\ndf\\n[53] vY 0.0s\\n\\noO\\n0 Generate .pairs and bam files\\\\n\\\\nThe pairtools...\\n1 .\\\\n\\\\n@ Vivaldi File Edit View Bookmarks Mail T...\\n2 4. scRepertoire on patient 4\\\\n\\\\nIn [51]: libra...\\n3 jf====\\\\n\\\\n#\\\\n#\\\\n#\\\\n#\\\\n#\\\\n#\\\\n#\\\\n#\\\\n#\\\\n4\\\\ni\\\\n#\\\\n...\\n4 Dy\\\\n\\\\n# Import python package for working with...\\n',\n",
|
||
" 'In [14]: import matplotlib\\nmatplotlib.use(\\'agg\\') #| Or choose another valid backend such as \\'tkagg\\', ‘qt5agg\\', etc.\\n\\nIn [15]: %%sbash\\nfancplot --plot heatmap 1:0-307041717 8:0-181122637 /mnt/storage3/aman/wdbasejuicer_new/aligned/inter.hic\\n——\\nTraceback (most recent call last):\\n\\nFile \"/home/aman/.local/bin/fancplot\", line 261, in <module>\\nFancPlot()\\nFile \"/home/aman/.local/bin/fancplot\", line 80, in __init__\\nimport matplotlib\\nFile \"/home/aman/.local/lib/python3.10/site-packages/matplotlib/__init__.py\", line 1270, in <module>\\nrcParams[\\'backend\\'] = os.environ.get(\\'MPLBACKEND\\' )\\nFile \"/home/aman/. local/lib/python3.10/site-packages/matplotlib/__init__.py\", line 738, in __setitem_\\nraise ValueError(f\"Key {key}: {ve}\") from None\\nValueError: Key backend: \\'module://matplotlib_inline.backend_inline\\' is not a valid value for backend; supported va\\nlues are [\\'gtk3agg\\', \\'gtk3cairo\\', \\'gtk4agg\\', ‘gtk4cairo\\', \\'macosx\\', \\'nbagg\\', \\'notebook\\', ‘\\'qtagg\\', ‘qtcairo\\', \\'qt5ag\\ng\\', ‘qt5cairo\\', \\'tkagg\\', \\'tkcairo\\', \\'webagg\\', \\'wx\\', \\'wxagg\\', ‘wxcairo\\', ‘\\'agg\\', ‘cairo\\', \\'pdf\\', \\'pgf\\', \\'ps\\', ‘svg\\',\\n‘template’ ]\\n\\nCalledProcessError Traceback (most recent call last)\\n\\n/tmp/ipykernel_532279/1126558398.py in <module>\\n\\n----> 1 get_ipython().run_cell_magic(\\'bash\\', \\'\\', \\'fancplot --plot heatmap 1:0-307041717 8:0-181122637 /mnt/storage\\n3/aman/wdbasejuicer_new/aligned/inter.hic\\\\n\\')\\n',\n",
|
||
" '[amnala@base alignment]$ ls sorted.GLDS*\\n\\nsorted.\\nsorted.\\nsorted.\\nsorted.\\nsorted.\\nsorted.\\nsorted.\\nsorted.\\nsorted.\\nsorted.\\nsorted.\\nsorted.\\nsorted.\\nsorted.\\n\\nGLDS-251_rna—seq_13JUN2017HiSeq_Run_Samp\\nGLDS-251_rna—seq_13JUN2017HiSeq_Run_Samp\\nGLDS-251_rna-—seq_13JUN2017HiSeq_Run_Samp\\nGLDS-251_rna—seq_13JUN2017HiSeq_Run_Samp\\nGLDS-251_rna—seq_13JUN2017HiSeq_Run_Samp\\nGLDS-251_rna—seq_13JUN2017HiSeq_Run_Samp\\nGLDS-251_rna—seq_13JUN2017HiSeq_Run_Samp\\nGLDS-251_rna-—seq_13JUN2017HiSeq_Run_Samp\\nGLDS-251_rna—seq_13JUN2017HiSeq_Run_Samp\\nGLDS-251_rna—seq_13JUN2017HiSeq_Run_Samp\\nGLDS-251_rna-—seq_13JUN2017HiSeq_Run_Samp\\nGLDS-251_rna—seq_13JUN2017HiSeq_Run_Samp\\nGLDS-251_rna—seq_13JUN2017HiSeq_Run_Samp\\nGLDS-251_rna-—seq_13JUN2017HiSeq_Run_Samp\\n\\n_Camnala@base alignment]$\\n\\ne_111_UMISS_Hoeksema_ATCACG_L@@1_R1_001_1M.\\ne_111_UMISS_Hoeksema_ATCACG_L@@1_R1_001_1M.\\ne_114_UMISS_Hoeksema_CGATGT_L@0@1_R1_001_1M.\\ne_114_UMISS_Hoeksema_CGATGT_L@@1_R1_001_1M.\\ne_116_UMISS_Hoeksema_TTAGGC_L@@1_R1_001_1M.\\ne_116_UMISS_Hoeksema_TTAGGC_L@@1_R1_001_1M.\\ne_120_UMISS_Hoeksema_TGACCA_L@@1_R1_001_1M.\\ne_12@_UMISS_Hoeksema_TGACCA_L@0@1_R1_001_1M.\\ne_175_UMISS_Hoeksema_AGTTCC_L@@2_R1_001_1M.\\ne_175_UMISS_Hoeksema_AGTTCC_L@@2_R1_001_1M.\\ne_179_UMISS_Hoeksema_CCGTCC_L@@3_R1_001_1M.\\ne_179_UMISS_Hoeksema_CCGTCC_L@@3_R1_001_1M.\\ne_235_239_UMISS_Hoeksema_GTTTCG_L@@3_R1_001_1M.fastq.\\ne_235_239_UMISS_Hoeksema_GTTTCG_L@@3_R1_001_1M.fastq.\\n\\nfastq.\\nfastq.\\nfastq.\\nfastq.\\nfastq.\\nfastq.\\nfastq.\\nfastq.\\nfastq.\\nfastq.\\nfastq.\\nfastq.\\n\\ndam\\n\\nDam.\\n\\ndam\\n\\nDam.\\n\\nbam\\n\\nDam.\\n\\ndam\\n\\nDam.\\n\\ndam\\n\\nDam.\\n\\ndam\\n\\nDam.\\n\\nbai\\n\\nbai\\n\\nbai\\n\\nbai\\n\\nbai\\n\\nbai\\nbam\\n\\nbam. bai\\n\\n',\n",
|
||
" 'In [433]:\\n\\ncombined_TCR <- combineTCR(\\nlist(patient3 = $1, patient4 = S2),\\nsamples = c(\"patient3\", \"patient4\"),\\nremoveNA = FALSE,\\nremoveMulti FALSE,\\nfilterMulti = FALSE\\n\\n)\\n\\nError in ‘mutate()*:\\n\\ni In argument: ‘TCR1 = ifelse(...)>.\\nCaused by error:\\n\\n! object \\'chain\\' not found\\nTraceback:\\n',\n",
|
||
" '|. Vermeulen c. Pagés-Gallogo, M. Koster |, Kranendonk, MEG, Wesseling P, Verburg,N, de Witt Harner, P, Koo Ed\\nDankmeijer,L van der Lugt, J, van Boarsen, K, Hoving, EW, Tops, 88. and de Ridder, J, 2023 Uita-fast deep-iearned CNS\\ntumour classitication during surgery. Nature.\\n+ Previously avaliable ae preprint on mow\\n2 Pagis- Gallego, M.oncide Ridder, J, 2023, Comprchensive benchmark and architectural analysis of dsep learning\\nmodels for nanopore sequencing basecalling, Genome Bio,\\n3 Pedersen, B.S. and de Ridder, J, 2023. Echivar: compressed variant representation for tapi annotation and itering of\\n\\nSii#s and indiels, Muceic Actes Res.\\n\\n4 Kester, de Barbanson,B, tyubimove, A, Chen, LT, van der Schrier, V, Alemany, A, Median, D, Peterson-Maduro, J\\nDros, J do Ride\\n‘constructs parallel tumor evolution, Cel Genomics, 2(2),p100096,\\n\\n5.von Berg, J. Ten Dam. M, van der Laan, SiN. and de Ridder J, 2022 Poiarorphism enables discovery of shared! genetic\\nvariants across mutipie traits from GWAS summary statistics. Bainformaties.\\n\\n6.Danyi, A, Jager, M.and de Ridder, 4, 2021 Cancer Type Classification in tiguldl aigpsies Based on Sparse Mutational\\nZrofies Enabled through Data Augmentation and integration. tite ease!)\\n\\n7. Nioboer, MM. Nguyen, L.cnd de Ridder, J, 2021 Pectin pathosenic non-coding SVs dstypling he 29 genome in\\n1545 whole cancer genomes using mutipie instance leaning. Selontie Reports, Hi), ppI-Ta\\n\\n8. Mulet-Lazaro,R, van Herk S,Erpelinck C, Sindels, €, Sanders, MA, Vermeulen, Renkens,. Vik, ?, Melnick AM, do\\nRidder, J.and Rehi, M, 2021, tlele-spectc expression of GRTAZ due to epigenetic dysregulation in CE8PA double-mutant\\nAti loos,\\n\\nJ. and van Oudenaarden, A, 2022, Integration of multiple ineage measurements trom the same cel\\n\\n9. Mareozzi, A, Jager, M Elferink, M, Straver, R, van Ginko, JH, Peltenburg, 8, Chen, LT. Renkens,l, von Kull, J, Terhoard,\\nde Bree, R, Dovriose, LA, Wilems, SM, Kloosterman, W?, de Ridder, J, 2020. iccurate detection of circulating tumor\\n\\nDNA using nanopore consensus sequencing. pj Genom Med. 6,108.\\n10, Ubels, J, Schaefers,T. Punt, C, Gucheloar, HJ. and de Ridder, J, 2020, FOREST. c ransom iorest approach to presict\\ntreatment benefitin data trom (flied) clinical drug tials. Bioinformatics, 36, ppI6O-i609,\\n\\n1. Kuznior, A, Maassen J, Verhoeven, S, Santuari L. Shneider, C, Kloosterman, WP. and de Ridder, J, 2020, sv-cators 3\\nDighiy portable parallel worklow for structural variant detection in whole-genome sequence data. Peer, 8, pel.\\n\\n12. Vermeulen, C. Allahyar, A, Bouwman, 8A, Krier, PH, Verstegen, MJ, Geeven, , Valdes-Quezacla, C, Renkens,L,\\n‘Straver, R, Kloosterman, W.P. ondde Ridder, J, 2020.jull-contact iC Jong-molseule sequencing of complex proximity\\nLigation products to uncover local cooperative and competitive chromatin topologies. Nature protocols, 15(2), pp 364-\\nam,\\n\\n13.Ubets, 4, Sonneveld P, van Viet MH. and de Ridder J, 2020, Gene networks constructed thvough simulated weatment\\ntearing can predict proteasoms inhibitor benefit in muliple myeloma. Cnical Cancer Research, 26(22),pp\\'5952-586I\\n\\n1 Nlaboor, MAM. ond de Ridder, J, 2020. vi: predicting the pathogenic etfect of TAD boundany-dsrupting somatic\\nstructural variants through multiple instance learning Bioinformatics, 36, ppi692-698.\\n\\n15. Allahyar, A, Ubels, J. oncide Ridder, J, 2019. data-crven intoractome of syneraisie genes improves network-based\\ncancer outcome prediction. Plos computational biology, 15(2), psl006657,\\n16, Ubels, J, SonnevelP, van Beers, EH, Bro A, van Vliet, MH. and de Ridder, J, 2018 Predicting weaiment Benet in\\n\\n\"multiple myeloma through simulation of alternative treatment effects, Nature communications, 9(), PpI-10.\\n\\n17-Allahyar, A, Vermeulen, C_ Bounman, 8, Kiger, PH, Verstegen, MU, Geeven, G, van Kranenburg, M, Peterse,M,\\n‘Straver,R, Haathus, JH and Jali K, 2018, Enhancer hubs and! loop calsions identi trom single-allele topologies.\\nature genetics, 50(8), ppIsI-N60.\\n\\n18. Allahyar, A. ond De Ridder, J, 2015. Rl: network-based clossiier with application to breast cancer outcome preston\\nBioinformatics 3i(2), ppiat-iai,\\n\\n',\n",
|
||
" 'About Blog Examples Plugins Docs ©\\n\\n5e+4\\n\\nA 3e+4\\n\\nte+4\\n6et3\\n\\n3e+3\\n\\n1e+3\\n6e+2\\n\\n3e+2\\n\\nchr9_chr9.mcool\\n[Current data resolution: 5.12M],\\n',\n",
|
||
" '@ = Safari File\\n\\nEdit View\\n\\nHistory\\n\\n¥% © & @ &#\\n\\n>\\ncod\\n\\nQ\\n\\nS Mon3.Nov 14:17\\n\\n-\\neco -\\n\\nrp | A pipeline for...\\n\\nHUMAN CELL ATLAS,\\nDATA EXPLORER\\n\\n<\\n\\nQ tINIT tutorial...\\n\\nBookmarks Window Help\\n0O9eW¢s8\\nCe) The integrate... © Swagger UI\\n\\nexplore.data.humancellatlas.org\\n\\nce) Choose Expor...\\n\\nExplore > Export Selected Data > Download Selecte..\\n\\nDownload Selected Data Using “curl”\\n\\nio Census data...\\n\\nio The integrate...\\n\\nGea ©\\n\\n(=) HLCA/docs/fa...\\n\\ne Files from projects with access \"required\" will be excluded from this export.\\n\\nDownload via curt\\nSpecies\\n\\nMus musculus\\n\\nHomo sapiens\\n\\nFile Type\\nName\\nbai\\n\\nbam\\n\\ncmd.exe\\n\\nquest curl Command\\n\\nFile Count\\n\\n22.0k\\n\\n22.0k\\n\\n22\\n\\nFile Size\\n\\n39.15 GB\\n\\n3.98 TB\\n\\n24.66 GB\\n\\nThe generated curl command is compatible with the Bash shell on Mac and Linux systems,\\nand the Command shell on Windows systems, and will remain valid for seven days.\\n\\nCurrent Query\\n\\nAccess\\ntrue\\n\\nGenus Species\\nHomo sapiens\\n\\nPaired End\\ntrue\\n\\nNucleic Acid Source\\nsingle cell\\n\\nFile Source\\nDCP/2 Analysis\\n\\nFile Format\\nloom\\n\\nSelected Data Summary\\n\\nEstimated Cells\\n570.8k\\n\\nFile Size\\n24.66 GB\\n\\nFiles\\n22\\n\\nProjects\\n19\\n\\nSpecies\\nHomo sapiens\\n\\nDonors\\n45\\n\\nDisease Status (Donor)\\n4 disease statuses\\n\\nSpecimens\\n775\\n\\nDisease Status (Specimen)\\n3 disease statuses\\n\\nAnatomical Entity\\n12 anatomical entities\\n\\nOrgan Part\\n14 organ parts\\n\\nLibrary Construction Method\\n2 library construction methods\\n\\nPaired End\\ntrue\\n\\nDownloaded and exported data is\\n\\n@ ChatGPT - Dr...\\n\\nPastebin.com...\\n\\na\\n© @ +\\n\\nCe) Download Sel\\n\\nHelp & Documentation + e@\\n\\n(\\n',\n",
|
||
" 'BUSCO Results\\nMetric\\nC (Complete BUSCOs)\\n$ (Single-copy BUSCOs)\\nD (Duplicated BUSCOs)\\nF (Fragmented BUSCOs)\\nM (Missing BUSCOs)\\nn (Total BUSCO groups)\\nBUSCO Count Details\\nComplete BUSCOs (C)\\nSingle-copy BUSCOs (S)\\nDuplicated BUSCOs (D)\\nFragmented BUSCOs (F)\\n\\nMissing BUSCOs (M)\\n\\nAssembly Stati\\nMetric\\nNumber of scaffolds\\nNumber of contigs\\nTotal length\\nPercent gaps\\nScaffold N50\\n\\nContig N50\\n\\nValue Description\\n12.1% Percentage of complete orthologs found.\\n12.1% Percentage of complete orthologs in single copy.\\n0.0% Percentage of complete orthologs duplicated.\\n17% Percentage of orthologs partially found.\\n86.2% Percentage of orthologs missing.\\n16 Total number of BUSCO ortholog groups searched.\\nValue Description\\n“4 Total complete orthologs found.\\n“4 Total single-copy orthologs found.\\n0) Total duplicated orthologs found.\\n2 Total fragmented orthologs found.\\n100 Total orthologs not found.\\nValue Description\\n3 Total number of scaffolds in the assembly.\\n3 Total number of contigs in the assembly.\\n132,191 bp Length of the assembled genome.\\n0.000% Percentage of gaps in the assembly.\\n86 KB N50 value for scaffolds.\\n86 KB N50 value for contigs.\\n',\n",
|
||
" 'Sub-Direction Ideal Labs / Institutes\\n\\nFunctional gene regulation Koszul, Cavalli, Almouzni, Giorgetti, Hughes, van Steensel\\n\\nEvolution / Development Kind, de Laat, Marti-Renom, Tellier, Cavalli\\n\\nDisease biology Bienko/Crosetto, Bickmore, Minucci, Reinius, Nucleome Therapeutics\\n',\n",
|
||
" '% TADS\\n',\n",
|
||
" 'Contact Matrices:\\n\\nFig x: Visualization in Juicebox for two HiC datasets\\n\\nThe 10*10 chromosomes full contact matrix was visualized in Juicebox GUI app by importing files\\nlocally. The left panel shows the matrix from the cis-regulatory elements in Maize study and the one\\non the right is from (7).The right panel is chromosome one at resolution 500 kb. The 10*10\\nchromosomes full contact matrix was visualized in Juicebox GUI app by importing files locally. The\\n10*10 chromosomes full contact matrix was visualized in Juicebox GUI app by importing files locally.\\n',\n",
|
||
" \"Inquiry - Master's Thesis\\n\\nBoyan <boyan.bonev@helmholtz-munich.de> Reply all | v\\n\\nTue 1/28, 11:52 AM\\nAman Shamil Nalakath y¥\\n\\nInbox\\n\\nDear Aman,\\n\\nSorry for the delay and thank you for the application and the interest in my lab. Unfortunately, | don’t have space for\\nany more students at the moment. | wish you all the best in finding a suitable lab.\\n\\nBest, Boyan\\n\",\n",
|
||
" '- Import 2. Filter 3. Plot 4. Measure\\n\\nSave prompt =)\\nDo you want to save a snapshot of your work?\\nSelect an option: [ne\\nok\\n\\ncancel\\n32__roiset.zip attempt_32_\\ntimestamp_150.tif\\n\\n5. Save\\n',\n",
|
||
" '90)\\n\\nPercentage (2%)\\n\\n24.1\\n\\nCG DMR Annotation Enrichment Plot\\n\\n16.5\\n\\nMutant\\n\\nMutant\\n\\nTARO\\n\\nAnnotation Type\\n\\nPromoter\\nTe\\n\\nPercentage (2%)\\n\\n90)\\n\\n57.8\\n\\nCHG DMR Annotation Enrichment Plot\\n\\nMutant\\n\\nMutant\\n\\nTARO\\n\\nAnnotation Type\\n\\nPercentage (2%)\\n\\n30)\\n\\nCHH DMR Annotation Enrichment Plot\\n\\nAnnotation Type\\n\\nGene\\n\\nBi oes\\n\\nPromoter\\n\\nTe\\n\\n63.7\\n\\n44.5\\n\\n20.9\\n\\nMutant Mutant TAIRIO\\n',\n",
|
||
" '[pst14@node25 ~]$ head inp struct.map\\n\\n1 1:14 0 14\\n\\n1 1:15 0 15\\n\\n1 1:26 0 26\\n\\n1 1:41 0 41\\n\\n1 1:293 0 293\\n1 1:701 0 701\\n1 1:720 0 720\\n1 1:864 0 864\\n1 1:1060 0 1060\\n',\n",
|
||
" '87 (filtered) 2849 (unique) 2849 (total), PLL = 2.57 187 (filtered) 2849 (unique) 2849 (total), P2LL = 1.67\\n\\n=187 (filtered) 2849 (unique) 2849 (total), P2LL = 1.97\\nJ\\n\\n',\n",
|
||
" '@ Vivaldi File Edit View\\n\\nCAhicv <\\n\\n&\\n\\nBookmarks = Mail\\n\\nTools\\n\\nWindow Help\\n\\n@ &—& > QV 8B wwwocbi-nim.nih.govsra\\n\\nGRR National Center for Biotechnology Information\\n\\nAccess\\nPublic (4)\\n\\nSource\\nDNA (4)\\n\\nLibrary Layout\\nsingle (4)\\n\\nPlatform\\nIllumina (4)\\n\\nStrategy\\nother (4)\\n\\nData in Cloud\\nGS (4)\\nS3 (4)\\n\\nFile Type\\nfastq (4)\\n\\n0 @0e ®B\\n\\n© Research Papers List\\n\\n@® &@e ¥\\n\\nW @ Search Startpage\\n\\nSummary +\\n\\nAdvanced\\n\\nLinks from BioProject\\n\\nItems: 4\\n\\n1. 4 ILLUM NA (Illumina NovaSeq 6000) run:\\nAccession: SRX11070613\\n\\n2. 41ILLUM NA (Illumina NovaSeq 6000) run:\\nAccession: SRX11070612\\n\\n3. 4 ILLUM NA (Illumina NovaSeq 6000) run:\\nAccession: SRX11070611\\n\\n4. 41LLUM NA (Illumina NovaSeq 6000) run:\\nAccession: SRX11070610\\n\\nSummary +\\n\\nStartpage Search Result\\n\\n€ CTCF Footprinting Revez\\n\\n&) Distinct IL-1a-responsive\\n\\nSend to: +\\n\\n: 18.5M spots, 1.8G bases, 554Mb downloads\\n\\n: 13.4M spots, 1.3G bases, 400.7Mb downloads\\n\\n: 15.6M spots, 1.5G bases, 468Mb downloads\\n\\n: 14.7M spots, 1.5G bases, 434.9Mb downloads\\n\\nS RNA polymerase II is req\\n\\nFilters:\\n\\nFind related data\\n\\nDatabase:\\n\\nRecent activity\\n\\nQ_ SRALinks for BioProject (Select 672940) (4)\\nRNA polymerase II is necessary for spatial\\nchromatin reorganization following e) BioProject\\n\\nRNA polymerase II is necessary for spatial\\nchromatin reorganization following e: BioProject\\n\\nStartpage Search Result\\n\\na\\n\\nTurn Off Clear\\n\\nSRA\\n\\nSee more...\\n\\nD SRA Links for BioProject\\n\\n+\\n\\nThu 22. May 19:19\\n\\n°8 @®@aaue\\n\\na\\n\\n© (2 (Q Reset Cun 100% 19:19\\n',\n",
|
||
" 'Normalized PD coordinate intervals\\n\\n(patch B)\\n\\n[0.75, 1.00]\\n\\n(0.50, 0.75]\\n\\n[0.25, 0.50]\\n\\n[0.00, 0.25]\\n\\nlm Stage 2V\\nStage 2-1V\\nmm Stage 2-I\\n\\n00 02 a4 6 os Lo\\n\\nAvg. max/mid ratio\\n\\n14\\n\\n',\n",
|
||
" \"= An official website of the United States government Here's how you know V\\n\\nNational Library of Medicine\\n\\nNational Center for Biotechnology Information\\n\\nBLAST ® » blastp suite » results for RID-PVJDBRCW013 Home Recent Results Saved Strategies Help\\nSave Search Search Summary v @ How to read this report? BLAST Help Videos ‘Back to Traditional Results Page\\nJob Title 2411:g91.t1 Filter Results\\nRID PVJDBRCW013 Search expires on 12-28 04:24am Download All\\n\\nOrganism only top 20 will appear exclude\\nResults for 1:lellQuery_7981391 2411:g1.t1(314aa) v i) UL\\n\\n——_ Type common name, binomial, taxid or group name\\n\\nProgram BLASTP @ Citation v\\n\\n+ Add organism\\nDatabase nr See details v\\nQuery ID Icl|Query_7981391 Percent Identity E value Query Coverage\\nDescription 2411:g1.t1 to to to\\n\\nMolecule type amino acid | iter |\\nQuery Length 314\\n\\nOther reports Distance tree of results Multiple alignment MSA viewer @\\n\\nDescriptions Graphic Summary Alignments Taxonomy\\nSequences producing significant alignments Download v Selectcolumns Y Show | 100Y | @\\nselect all 100 sequences selected GenPept Graphics Distance tree of results Multiple alignment MSA Viewer\\nDescriptio Scientific Name Max Total Query E Per. Acc. |\\nuP fon cenine Score Score Cover value Ident Len Accession\\nv v v v v v\\nhypothetical protein RF2 [Diospyros sutchuensis] Diospyros sutchuensis 651 651 100% 0.0 100.00% 2286 YP _010511772.1\\nhypothetical chloroplast RF21 [Diospyros cathayensis] Diospyros cathayensis 651 651 100% 0.0 100.00% 2286 YP _009520195.1\\nhypothetical chloroplast RF2 [Diospyros maclurei] Diospyros maclurei 649 649 100% 0.0 99.68% 2286 YP _009628122.1\\nhypothetical chloroplast RF2 [Diospyros blancoi] Diospyros blancoi 649 649 100% 0.0 99.68% 2286 YP _009342891.1\\nYcf2 [Diospyros mespiliformis] Diospyros mespiliformis 649 649 100% 0.0 99.68% 2279 QWY85156.1\\n\\n\",\n",
|
||
" 'Principal component plot of the samples\\n\\nRelated to the distance matrix is the PCA plot, which shows the samples in the 2D plane spanned by their first two principal components. This\\ntype of plat is useful for visualizing the overall effect of experimental covariates and batch effects.\\n\\nplotPCA(vsd, intgroup=c(\"condition\", “type\"))\\n\\n.\\nbs © exe\\nFa © weated:single read\\n5 | © untreated:paired-end\\n8 © uneatedsingle2d\\nBe\\n\\ne\\n\\nItis also possible to customize the PCA plot using the ggplot function.\\n\\npcaData <- plotPCA(vsd, intgroup=c(\"condition\", \"type\"), returnData=TRUE)\\npercentVar <- round(100 x attr(pcaData, \"percentVar\"))\\nggplot(pcabata, aes(PC1, PC2, color=condition, shape=type)) +\\ngeom_point(size=3) +\\nxlab(pasteo(\"PCt:\\nylab(pasteo(\"Pc2:\\ncoord_fixed()\\n\\npercentVar (1),\\nspercentVar [2],\\n\\n5 © paires-ond\\nAh sigleread\\n\\ncondition\\n© unveatea\\n. © oated\\n\\nPCa: 29% variance\\n\\n',\n",
|
||
" 'Pooling single-cell samples to simulate pseudo-bulk\\n\\nThe single-cell data was pooled into pseudo-bulk RNA-Seq profiles for use with ftINIT/tINIT. We\\nchose a strategy that assigned each mRNA molecule equal importance, and therefore summed all\\nUMIs from all cells in a population into a single pseudo-bulk sample. The sample counts were then\\nscaled to a total sum of 10° (counts per million, CPM, normalization), except in cases where other\\nnormalization methods such as TMM or quantile normalization were applied.\\n',\n",
|
||
" \"[+externe Mail+] Master's thesis; 3D Genome\\n\\nre) To: Papantonis, Argyris <argyris.papantonis@med.uni-goettingen.de>\\n\\nDear Dr. Papantonis,\\n| still haven’t heard back from you. Would April be too soon to start?\\n\\nBest Regards,\\nAman\\n\\n© Show message history\\n\\n| send | Discard () jal ©\\nr@) Aman Shamil Nalakath\\nMon 3/3, 1:31 PM\\n\\nDear Dr. Papantonis,\\n\\n2\\n<\\n\\nThe recommended duration for a thesis as per our program is 6 months, that is once registered, the submission is due within this period.\\nIf it is okay, I'd like to begin at the start of April.\\n\\nBest Regards,\\nAman\\n\\nr@) Papantonis, Argyris <argyris.papantonis@med.uni-goettingen.de>\\nMon 3/3, 8:11 AM\\n\\nAman Shamil Nalakath ¥\\n\\nHi Aman,\\n\\nYou need to tell me what the expected duration by your program is and what would be your preferred starting date — we are flexible.\\nA.\\n\\nArgyris Papantonis, PhD\\n\\nProfessor for Translational Epigenetics & Genome Architecture,\\nInstitute of Pathology, University Medical Center Géttingen,\\nRobert-Koch-Str. 40, 37075 Géttingen, Germany\\n\\nTel.: +49 551 39 65734\\n\\nWeb: https://papantonislab.eu\\n\",\n",
|
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" \"Fig x: Visualization in Juicebox for two HiC datasets\\n\\nThe 10*10 chromosomes full contact matrix was visualized in Juicebox GUI app by importing files\\nlocally. The left panel shows the matrix from the cis-regulatory elements in Maize study and the one\\non the right is from (7). Even though the raw hic sequencing data was trimmed correctly the second\\ndataset showed poor quality as is evident from the figure. The noise was high and HiCCUPs couldn't\\nfind loops correctly.\\n\",\n",
|
||
" \"for i in xbowtie.vcf; do\\nveffilter -f 'QUAL / AO > 10' $i > $i.filt.vcf\\nveffilter -f 'QUAL / AO < 10' $i > $i. fail.vcf\\ndone\\n\",\n",
|
||
" '=187 (filtered) 2849 (unique) 2849 (total), P2LL = 2.54\\n\\nPee eae aa Te tae eae ee\\n\\n=187 (filtered) 2649 (unique) 2849 (total), P2LL = 2.57\\n\\n=187 (filtered) 2649 (unique) 2849 (total), P2LL = 1.67\\n\\nToae ea sap e aes aeP eg\\n\\n=187 (filtered) 2649 (unique) 2849 (total), P2LL = 1.97\\n\\n',\n",
|
||
" '',\n",
|
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" '@ = Terminal Shell Edit View Window Help (e) *- © € ©) F Q S Sun 23.Nov 15:14\\n\\n@ ee Documents — nano ~/.zshrc — 208x63\\n\\nexport PATH=\"/opt/homebrew/bin:$PATH\"\\n\\nexport PATH=\"$HOME/anaconda3/bin:$PATH\"\\n\\nexport PATH=\"/Users/aman/bin:$PATH\"\\n\\nexport LUA_CPATH=\"/opt/homebrew/1lib/lua/5.4/?.s0;;\"\\n\\n~\\n\\nalias tum_ngs=\\'ssh -L 9006:localhost:9006 a.nalakath@10.152.154.1\\'\\n\\nalias jlab=\\'ssh -L 9005:localhost:9005 aman@10.162.143.69\\'\\n\\nalias taltech=\\'ssh amnala@base.hpc.taltech.ee\\'\\n\\nalias frna_tum=\\'ssh master24@192.168.10.174\\'\\n\\nalias zehn_lab=\\'ssh -L 8785:localhost:8787 aman@1@.162.17.13\\'\\n\\nalias docker_clean_ps=\\'docker rm $(docker ps --filter=status=exited --filter=status=created -q)\\'\\nalias docker_clean_images=\\'docker rmi $(docker images -a --filter=dangling=true -q)\\'\\n\\nalias ssh_biodata=\\'ssh -L 8783:localhost:8783 -L 8899:localhost:8899 biodata’\\n\\neval $(thefuck --alias)\\n\\nexport PATH=\"/opt/homebrew/bin:$PATH\"export PATH=\"$PATH:$(go env GOPATH)/bin\"\\n\\n‘G Help ‘0 Write Out ‘F Where Is M-x Cut ‘T Execute *C Location M-U Undo M-A Set Mark M-] To Bracket M-B Previous <« Back ‘< Prev Word\\n\\n“xX Exit *R Read File ‘\\\\ Replace M-v Paste ‘J Justify */ Go To Line M-E Redo M-c Copy ‘B Where Was M-F Next >» Forward ‘> Next Word J\\neG\\n\\n',\n",
|
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" 'Article | Open access | Published: 14 June 2019\\n\\nChromatin interaction maps reveal genetic regulation\\nfor quantitative traits in maize\\n\\nYong Peng, Dan Xiong, Lun Zhao, Weizhi Ouyang, Shuangqi Wang, Jun Sun, Qing Zhang, Pengpeng\\n\\nGuan, Liang Xie, Wenqiang Li, Guoliang Li@, Jianbing Yan™ & Xingwang Li4\\n\\nNature Communications 10, Article number: 2632 (2019) | Cite this article\\n',\n",
|
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" 'Statistics of Read Pairs Alignment on Restriction Fragments\\n\\nInvalid_pairs\\n12408 Valid_interaction_pairs\\n\\nValid_interaction_pairs_FF\\nValid_interaction_pairs_RR\\n\\nValid_interaction_pairs_RF\\n\\nValid_interaction_pairs_FR\\n\\nMeal’ VUUINS\\n\\nFiltered_pairs\\n5e+07\\nDumped_pairs\\nSelf_Cycle_pairs\\nReligation_pairs\\n\\nSingle-end_pairs\\n\\nDangling_end_pairs\\n\\n| Saas | |\\n\\nOe+00\\n\\nAll Pairs Valid 3C Pairs Invalid 3C Pairs\\ndata\\n',\n",
|
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" '@ = Safari File Edit View History Bookmarks Window Help @&@ eH O\\n\\nSe Q S$ Mon13. Oct 17:40\\n\\nr\\n\\n@eceo« M- < > on OoBi@se quillbot.com Bae ©\\ning in all your favorite apps with QuillBot for macOS\\n\\n@ QuillBot oaciuw Al Detector 53} Apps andextensions v @&\\n\\n& Perfect you!\\n\\nParaphr English French Spanish German All Vv\\n\\nCY\\nModel Version: v5.7.1\\n\\nGramm disorders. W <~ Share\\n\\nar Ch... Genome remodeling has been reported to show the hallmarks of cancer first Feedback\\n\\nKy explained by 2% ra)\\n\\n& Download\\n\\nA Pas Hanahan et. al(47). Changes were seen to affect proliferation, genome stability, History\\na tumor of text is likely Al @)\\nPlagiari suppression, metastasis, and cellular plasticity(48). Large scale chromatin OQ auiliBot\\nsm t... changes such as\\n® compartment switching, TAD disruption, and enhancer hijacking reshape\\nAltium genome organization\\n® in cancer, sustaining oncogenic transcriptional programs. Such disorganization\\nAl Chat is not only a by Al Human\\ni 8\\nAl imag product of mutations but can also act as a driver of tumorigenesis, its Al-generated (0) © 1%\\ne Gen... diversification, and .\\nnee therapy resistance(49). Al-generated & Al-refined (0) 1%\\nHuman-written & Al-refined (0) 0%\\n\\ncy developmental\\nuilgot genes thereby controling ciferentiation, stem cell plastic, andthe eel Humansarten 98%\\norm. fycle(50), They silence\\n\\ngenes through chromatin compaction, deposition of repressive histone marks\\nsuch as.\\n\\nH3K27me3, H2AK119u), and inhibition of transcriptional initiation or\\nelongation(51). Activating\\n\\nmarks keep chromatin open and facilitate the recruitment of factors such as\\nRNA polymerase II,\\n\\nwhereas repressive marks promote compaction by recruiting silencing\\ncomplexes like PRC1,\\n\\nPRC2, and HP1 (1). Misregulation of Polycomb function disrupts these)\\n\\ninthe absence\\n\\nof genetic mutations. Histone modifications regulate transcription by creating\\nbinding sites for\\n\\nprotein complexes(52).\\n\\nPolycomb establishes long-range contacts through H3K27me3-marked loop\\nanchors that\\n\\nconnect loci in the same compartments and usually span many megabases,\\nlargely\\n\\nindependent of CTCF and TADs(53). These contacts rely on EZH2 occupancy at\\nRNA-binding\\n\\nsites of PRC2, which nucleates and spreads H3K27me3 to create anchors for\\nthese long\\n\\nchromatin contacts(53). A study on neuronal cells observed that polycomb\\nestablishes\\n\\nextensive long range interactions, including trans contacts, and unlike CTCF-\\ncohesin loops,\\n\\nthese contacts cluster repressed and bivalent loci into multi-locus networks,\\ncreating a distinct\\n\\nranraccive lavar(SA\\\\ Alen nalucamh damaine can accambla inta randancate-\\n\\n3,373 Words if) cp @ Analysis complete\\nWant your text to sound more authentic? Refine with Paraphraser\\n\\nUnderstanding your results\\n',\n",
|
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" 'A Sequenced\\nHi-C Reads\\n\\nAlignment and\\nChimera Handling Merge Sort\\n\\nSS Sass = SS\\n—_ oo i\\n—\\nSSS oo\\na\\n\\noe OT\\n\\nDuplicate\\nremoval\\n\\nMap creation\\n\\ni\\n—————\\n\\n',\n",
|
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" '@ Vivaldi File Edit View\\n\\nBookmarks\\n\\n> Q ®@ Wed 15. Oct 13:18\\n\\nTools Window Help S\\n\\nai)\\n\\n=— > QQ. VU B docs.google.com/document/d/1ppXg... W @ Search Startpage\\n\\nNalakath_master_thesis_agbio * ra)\\n\\nFile Edit View Insert Format\\n\\nQo ee GBA F 90%\\n\\n<\\n\\nDocument tabs +\\n\\n1\\nr\\n\\nMachine learning analy...\\n\\nClassification model\\n\\n2\\nr\\n\\nRegression Model\\n\\nStatistical validation an...\\n\\n3\\nrt\\n\\nResults\\n\\n(i) Genome architec...\\n\\n(ii) Transcriptomic a...\\n\\n4\\ni\\n\\n(iii) Integrative analy...\\n\\nDiscussion\\n\\n5\\nrf\\n\\nConclusion\\n\\nAcknowledgments\\n\\n6\\ni\\n\\nList of Abbreviations\\n\\nAppendix\\n\\n| References\\n\\n7\\nH\\n\\nDeclaration of Authorship\\n\\nS Workspaces v\\n\\n0 @0e 8B\\n\\nv\\n\\nB Nalakath_master_thesis_<\\n\\nNormal text v Arial ’\\n\\nStartpage Search Results\\n\\nExtensions Zotero Help\\n\\n-({nj+ > Bgru\\n\\n5 2n non Sin tn Men hn Si en Sn en inn nen inn en nnSZ\\n\\n53. Kraft K, Yost KE, Murphy SE, Magg A, Long Y, Corces MR, et al. Polycomb-mediated\\ngenome architecture enables long-range spreading of H3K27 methylation. Proc Natl Acad Sci.\\n2022 May 31;119(22):e2201883119.\\n\\n54. Pletenev IA, Bazarevich M, Zagirova DR, Kononkova AD, Cherkasov AV, Efimova Ol, et\\nal. Extensive long-range polycomb interactions and weak compartmentalization are hallmarks of.\\nhuman neuronal 3D genome. Nucleic Acids Res. 2024 June 24;52(11):6234-52.\\n\\n55. Akilli N, Cheutin T, Cavalli G. Phase separation and inheritance of repressive chromatin\\ndomains. Curr Opin Genet Dev. 2024 June;86:102201.\\n\\n56. Piunti A, Shilatifard A. The roles of Polycomb repressive complexes in mammalian\\ndevelopment and cancer. Nat Rev Mol Cell Biol. 2021 May;22(5):326—-45.\\n\\n57. Doyle EJ, Morey L, Conway E. Know when to fold ‘em: Polycomb complexes in\\noncogenic 3D genome regulation. Front Cell Dev Biol. 2022 Aug 29;10:986319.\\n\\n58. Parreno V, Martinez AM, Cavalli G. Mechanisms of Polycomb group protein function in\\ncancer. Cell Res. 2022 Mar;32(3):231-53.\\n\\n59. Shi Y, Wang X xi, Zhuang Y wen, Jiang Y, Melcher K, Xu HE. Structure of the PRC2\\ncomplex and application to drug discovery. Acta Pharmacol Sin. 2017 July;38(7):963—76.\\n\\n60. Liu KL, Zhu K, Zhang H. An overview of the development of EED inhibitors to disable the\\nPRC2 function. RSC Med Chem. 2022;13(1):39-53.\\n\\n61. Parreno V, Loubiere V, Schuettengruber B, Fritsch L, Rawal CC, Erokhin M, et al.\\nTransient loss of Polycomb components induces an epigenetic cancer fate. Nature. 2024 May\\n16;629(8012):688-96.\\n\\n62. Lee W, Teckie S, Wiesner T, Ran L, Prieto Granada CN, Lin M, et al. PRC2 is recurrently\\ninactivated through EED or SUZ12 loss in malignant peripheral nerve sheath tumors. Nat\\nGenet. 2014 Nov;46(11):1227-32.\\n\\n63. Dexter DL, Spremulli EN, Fligiel Z, Barbosa JA, Vogel R, VanVoorhees A, et al.\\nHeterogeneity of cancer cells from a single human colon carcinoma. Am J Med. 1981\\nDec;71(6):949-56.\\n\\n64. Cytion [Internet]. [cited 2025 Oct 3]. DLD-1 Cell Line: Applications and Insights of\\n\\nColorectal Cancer Research. Available from:\\n\\nN_ DiffDomain enables identi Using figures in thesis\\n\\nO———= 100 %\\n\\nQua\\n\\ns 0 @\\n\\n13:18\\n',\n",
|
||
" 'Aman\\n_——\\n',\n",
|
||
" '82 zehn_s <- FindNeighbors(zehn_s, dims = 1:10) # construct a KNN graph based on the euclidean distance in PCA spa\\n83 zehn_s <- FindClusters(zehn_s, resolution = 0.5) # roup cells together, with the goal of optimizing the standar«\\n\\n85 head(Idents(zehn_s), 10)\\n\\n86:1 (Top Level) +\\n\\nConsole’ Terminal Background Jobs\\n\\nR~R4.4.2 - ~/\\n\\nElapsed time: @ seconds\\n> head(Idents(zehn_s))\\n289640 749304 4140304 6146148 7127271 7979596\\n1) 7) 7) ) 1) 1)\\nLevels: 0\\n> head(Idents(zehn_s), 5)\\n289640 749304 4140304 6146148 7127271\\n1) 7) 7) ) 1)\\nLevels: 0\\n> head(Idents(zehn_s), 10)\\n289640 749304 4140304 6146148 7127271 7979596 8859261\\n7) 1) ) 7) 1) ) 1)\\nLevels: 0\\n>\\n\\n9160359 11832300 11874623\\n\\n1)\\n\\n1)\\n\\n1)\\n\\nR Script\\n\\nte\\n',\n",
|
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" 'Gi e dq e bOO a a ‘ele) qo PDF|\\nF talte Pla A: a gene set e pantherdb.org/ge A agriGO - re agriGO - term de\\nCp) ot se € pola tN Q\\npeed Dia ported Fro ported Fro Online Bewerbung Q API Do qg g to pe p a Pastebin.co\\nGraph font size (pt): O7 O8 Og @10 O11 O12 ale =\\nGO flash Chart ©&\\nSelect Category MPLE\\nBiological Process O cellular Component O Molecular Function\\nAdvanced Parameter Settings\\nBar style: @Glass Bar O Filled Bar O3D Bar O Cylinder Bar i\\nQuery bar color: Bg/Ref bar color: [HEX format only] [ default ] ;\\nX legend content: @Go annotation O GO accession font: tab | I. I |,\\nEMER EER LARA LRR A\\nX legend rotation: [270 to 315 is suggested] SPESPIEPEFAAAEFREAAA GSS\\nDetail information ©\\nYou can [ rowse in tree traversing mode ] [ & Browse all GO terms ] [ & Download ]\\nOr select from following significant terms to [ Draw graphical results ;3; ] [ Create bar chart jij; ] [Scatter Plots analysis «| ]\\nOcoterm Ontology Description Number in input list Number in BG/Ref p-value FDR\\n© 60:0010200 | P response to chitin 21 134 1.5e-20 1.5e-17\\n© G0:0015979 || P photosynthesis 25 253 8e-20 4e-17\\n0 G0:0010243 P response to organonitrogen compound 21 166 8.1e-19 2.7e-16\\nO60:1901698 | P response to nitrogen compound 21 264 4.5e-15 1.1e-12\\n0 G0:0019684 P photosynthesis, light reaction 11 134 1.3e-08 2.6e-06\\n0 Go0:0006091 P generation of precursor metabolites and energy 16 364 3.1e-08 5.2e-06\\n0 60:0009769 P photosynthesis, light harvesting in photosystem II 5 10 6.2e-08 8.9e-06\\n© G0:0009719 P response to endogenous stimulus 35 1732 1.1e-07 1.4e-05\\nO 60:0010033 P response to organic substance 37 2023 5.3e-07 6e-05\\nOGo:1901700 P response to oxygen-containing compound 31 1557 9.1e-07 9.2e-05\\n© G0:0009765 | P photosynthesis, light harvesting 5 44 3.2e-05 0.0029\\n© G0:0009145 P purine nucleoside triphosphate biosynthetic process 5 46 3.9e-05 0.0033\\n\\n00 & &\\n\\nele)\\n\\n>\\na\\n\\n',\n",
|
||
" \"0@°8@\\nW PICO 5.09\\n\\nDocus_tag\\nKBOCNLJJ_00001\\nKBOCNLIJJ_00002\\nKBOCNLIJJ_00003\\nKBOCNLJJ_00004\\nKBOCNLJJ_00005\\nKBOCNLIIJ_00006\\nKBOCNLJJ_00007\\nKBOCNLJJ_00008\\nKBOCNLJJ_00009\\nKBOCNLJJ_00010\\nCRISPR\\nKBOCNLJJ_00011\\nKBOCNLJJ_00012\\nKBOCNLIJJ_00013\\nKBOCNLIJJ_00014\\nKBOCNLIJJ_00015\\nKBOCNLIJJ_00016\\nKBOCNLIJJ_00017\\nKBOCNLJJ_00018\\nKBOCNLIJJ_00019\\nKBOCNLJJ_00020\\nKBOCNLIJJ_00021\\nKBOCNLIJ_00022\\nKBOCNLIJJ_00023\\nKBOCNLIJJ_00024\\nKBOCNLIJJ_00025\\nKBOCNLIJJ_00026\\nKBOCNLJJ_00027\\nKBOCNLJJ_00028\\nKBOCNLIJJ_00029\\nKBOCNLIJJ_00030\\nKBOCNLIJJ_00031\\nKBOCNLIJJ_00032\\nKBOCNLIJJ_00033\\nKBOCNLIJIJ_00034\\nKBOCNLIJJ_00035\\nKBOCNLIJIJ_00036\\nKBOCNLIJJ_00037\\nKBOCNLJJ_00038\\nKBOCNLIJ_00039\\nKBOCNLIJJ_00040\\nKBOCNLIJJ_00041\\nKBOCNLIJ_00042\\nKBOCNLJJ_00043\\nKBOCNLIJ_00044\\nKBOCNLJJ_00045\\nKBOCNLIIJ_00046\\nKBOCNLIJJ_00047\\nKBOCNLIJJ_00048\\nKBOCNLIJJ_00049\\nKBOCNLJJ_00050\\nKBOCNLIJJ_00051\\nKBOCNLIIJ_00052\\nKBOCNLIJJ_00053\\nKBOCNLIJJ_00054\\nKBOCNLJJ_00055\\nKBOCNLIIJ_00056\\n\\nWie) Get Help\\nWed Exit\\n\\nftype\\nCDS\\nCDS\\nCDS\\nCDS\\nCDS\\ncDS\\ncDS\\nCDS\\nCDS\\nCDS\\n763\\nCDS\\nCDS\\ncDS\\ncDS\\nCDS\\nCDS\\nCDS\\nCDS\\nCDS\\nCDS\\ncDS\\ncDS\\nCDS\\nCDS\\nCDS\\nCDS\\nCDS\\nCDS\\ncDS\\ncDS\\nCDS\\nCDS\\nCDS\\nCDS\\nCDS\\nCDS\\ncDS\\ncDS\\nCDS\\nCDS\\nCDS\\nCDS\\nCDS\\nCDS\\ncDS\\ncDS\\nCDS\\nCDS\\nCDS\\nCDS\\nCDS\\nCDS\\ncDS\\ncDS\\nCDS\\nCDS\\n\\nlength_bp\\n\\n1545 cysI_1\\n735 cysH_1\\n2667 ygcB_1\\n1509 casA_1\\n483 casB_1\\n1092 casC_1\\n675 casD_1\\n600 casE_1\\n918 ygbT_1\\n285 ygbF_1\\n1038\\n\\n909 cysD_1\\n1428 cysN\\n606 cysC\\n324 ygbE\\n312 ftsB\\n711 ispD\\n480 ispF\\n1050 truD\\n762 surE\\n627 pem\\n1140 nlpD_1\\n993 rpoS\\n1365 ygbN\\n777 otni\\n639 otnc\\n372 otnK_1\\n834 otnK_2\\n909 1tnD\\n768 glcR\\n657 pphB\\n2562 mutS\\n135\\n\\n354\\n\\n2079 fhlA\\n1011 hypE\\n1122 hypD\\n273 hypC\\n873 hypB\\n351 hypA\\n462 hycA\\n612 hyfA_1\\n1827 ndhB_1\\n924 hycD\\n1710 hycE\\n543 ndhI_1\\n768 hycG_1\\n411\\n\\n471 hycI\\n1425 bglH_1\\n1458 bglF_1\\n1014 ascG\\n528 hyfA_2\\n2253 hypF\\n1134 norw\\n1440 norv\\n\\nn\\n\\n8.1.2 COG@155\\n8.4.8 COG@175\\ntbo=\\n\\nge\\n1.\\n1.\\n3. -- C0G1203\\n\\n3.1.-.-\\n- c0G1518\\n\\n2.7.7.4 COG0175\\n2.7.7.4 CO0G2895\\n2.7.1.2\\n\\nc0G2919\\n\\n7\\nCOGQ496\\n\\nC0G0739\\nCOG@568\\nC0G2610\\n\\nC0G1349\\n3.1.3.16\\nC0GE249\\n\\nCO0G3604\\n4.2.1.- C0G0@309\\nCOGe409\\nC0G0298\\nC0G378\\nC0G@375\\n\\n1.-.-.- C0G1142\\n7.1.1.-\\n\\nCOGe65e\\n\\nC0G3261\\n7.1.1.-\\n\\nC0G3260\\n3.4.23.51\\n3.2.1.86\\n\\nC0G1263\\nCO0G1609\\n1.-.-.- C0G1142\\n6.2.-.- C0G0068\\n\\n1.18.1.-\\nCOGQ426\\n\\nWe) WriteOut\\nWe) Justify\\n\\nEC_number CoG\\n\\naman — nano ./Downloads/assignment/Ecoli_hifi/Ecoli_hifi_genome.tsv — 208x63\\n\\n/Downloads/as\\n\\nproduct\\n\\nSulfite reductase [NADPH] hemoprotein beta-component\\nPhosphoadenosine phosphosulfate reductase\\nCRISPR-associated endonuclease/helicase Cas3\\nCRISPR system Cascade subunit CasA\\n\\nCRISPR system Cascade subunit CasB\\n\\nCRISPR system Cascade subunit CasC\\n\\nCRISPR system Cascade subunit CasD\\n\\nCRISPR system Cascade subunit CasE\\nCRISPR-associated endonuclease Cas1\\nCRISPR-associated endoribonuclease Cas2\\n\\nhypothetical protein\\nSulfate adenylyltransferase subunit 2\\nSulfate adenylyltransferase subunit 1\\nC0G@529 Adenylyl-sulfate kinase\\nInner membrane protein YgbE\\nCell division protein FtsB\\nC0G1211 2-C-methyl-D-erythritol 4-phosphate cytidylyltransferase\\nC0G@245 2-C-methyl-D-erythritol 2,4-cyclodiphosphate synthase\\nCOG@585 tRNA pseudouridine synthase D\\n5'/3'-nucleotidase SurE\\nC0G2518 Protein-L-isoaspartate O-methyltransferase\\nMurein hydrolase activator NlpD\\nRNA polymerase sigma factor RpoS\\nInner membrane permease YgbN\\nC0G3622 2-oxo-tetronate isomerase\\n\\n3-oxo-tetronate 4-phosphate decarboxylase\\nC0G3395 3-oxo-tetronate kinase\\nC0G3395 3-oxo-tetronate kinase\\n\\nL-threonate dehydrogenase\\nHTH-type transcriptional repressor GlcR\\nC0G@639 Serine/threonine-protein phosphatase 2\\nDNA mismatch repair protein MutS\\nhypothetical protein\\nhypothetical protein\\nFormate hydrogenlyase transcriptional activator FhlA\\nCarbamoyl dehydratase HypE\\nHydrogenase maturation factor HypD\\nHydrogenase maturation factor HypC\\nHydrogenase maturation factor HypB\\nHydrogenase maturation factor HypA\\nFormate hydrogenlyase regulatory protein HycA\\nHydrogenase-4 component A\\nNAD(P)H-quinone oxidoreductase subunit 2, chloroplastic\\nFormate hydrogenlyase subunit 4\\nFormate hydrogenlyase subunit 5\\nNAD(P)H-quinone oxidoreductase subunit I, chloroplastic\\nFormate hydrogenlyase subunit 7\\nhypothetical protein\\nCOG@68@ Hydrogenase 3 maturation protease\\nC0G2723 Aryl-phospho-beta—D-glucosidase BglH\\nPTS system beta-glucoside-specific EIIBCA component\\nHTH-type transcriptional regulator AscG\\nHydrogenase-4 component A\\nCarbamoyltransferase HypF\\nC0G1251 Nitric oxide reductase F1Rd-NAD(+) reductase\\nAnaerobic nitric oxide reductase flavorubredoxin\\n\\nWs) Read File Way Prev Pg\\nWi] Where is WAY Next Pg\\n\\nAKI\\nAU\\n\\nnment/Ecoli_hifi/Ecoli_hifi_genome.tsv\\n\\nCut Text\\nUnCut Text\\n\\nme Cur Pos\\nWay To Spell\\n\",\n",
|
||
" 'Patch A\\nepidermis\\n\\nPatch B\\nepidermis\\n',\n",
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" '# Renaming columns to match scRepertoire expectations\\n\\n19 colnames(S1) <- cC\\n\\n20 \"cell_id\", \"total_read_count\", \"total_moLecule_count\",\\n\\n21 \"v_call\", \"j_call\", \"c_gene\", \"cdr3_nt\", \"cdr3\",\\n\\n22 \"alpha_gamma_read_count\", \"alpha_gamma_molecule_count\",\\n\\n23 \"beta_v_gene\", \"d_call\", \"beta_j_gene\", \"beta_c_gene\",\\n\\n24 \"beta_cdr3_nt\", \"beta_cdr3\", \"beta_read_count\", \"beta_molecule_count\",\\n\\n25 \"paired_chains\", \"cell_type\", \"high_quality\"\\n\\n26 (+)\\n\\n27\\n\\n28 # adding a column for ##Locus\\n\\n29 Si$locus <- \"TRA\"\\n\\n30\\n\\n31 $1 <- S1[, cC\"cell_id\", \"v_call\", \"j_call\", \"d_call\", \"cdr3\", \"cell_type\", \"Locus\")]\\n\\n32 contig_list <- list(S1)\\n\\n33 contig_list <- loadContigsCinput = contig_list, format = \"BD\")\\n\\n34\\n\\n35:1 (Top Level) = R Scrip\\nConsole’ Terminal Background Jobs cl\\n\\nR~ R4.4.2 - ~/\\n\\n)\\n\\n\"v_call\",\\n\"alpha_gamma_read_count\", \"“alpha_gamma_moLecule_count\",\\n\\n\"beta_v_gene\", \"d_call\", \"beta_j_gene\", \"beta_c_gene\",\\n\\n\"beta_cdr3_nt\", \"beta_cdr3\", \"beta_read_count\", \"beta_molecule_count\",\\n\"paired_chains\", \"cell_type\", \"high_quality\"\\n\\nj_call\", \"c_gene\", \"cdr3_nt\", \"cdr3\",\\n\\nS1$locus <- \"TRA\"\\n\\nS1 <- S1[, cC\"cell_id\", \"v_call\", \"j_call\", \"d_call\", \"cdr3\", \"cell_type\", \"Locus\")]\\ncontig_list <- list(S1)\\n\\ncontig_list <- loadContigsCinput = contig_list, format = \"BD\")\\n\\nrror in “[.data.frame*Cdf[[i]], , cC\"cell_id\", \"locus\", \"v_call\", \"d_call\",\\n\\nundefined columns selected\\n\\n+\\n+\\n+\\n+\\n+\\n+\\n> # adding a column for ##locus\\n>\\n>\\n>\\n>\\nE\\n',\n",
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" 'Lipid biosynthesis\\n\\nplastid\\n\\nFatty acid modification\\n— Lipid biosynthesis\\nKASIII DIS/KASI KASII\\n\\nC2 —> C4 —> > > C16 —c1s7\\n\\nTE\\nTE LACS\\nLACS\\n\\nC16-CoA = 18-CoA\\n\\n|\\n\\nER\\nRAM2/GPAT6\\nC16-CoA + glycerol ————>. sn2-MAG\\n',\n",
|
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" '@ Vivaldi File Edit View Bookmarks Mail Tools Window Help @a ® @@éd6dr+ez@oee ©) F Q ® SatDec7 9:24\\n\\nie taltech v Compartmentalization of L The histone H3 variant H3 How to Apply i Summer 2025 Main C-RVEN EME CiClwimms Ea Summer 2025 Main\\n\\na) <& > OQ. YU &B selapp.imp.ac.at = ~ Q@ Search Google ’ Lb ¢é®é oOo et eG CO &\\n\\nY Speed Dial ¥ Imported From... Y Imported From Online Bewerbung QGIS API Docume. qgis - Trying to pe... New Script - Earth... Pastebin.com - #1... TargetP 2.0- DTU... https://www.mood... OnePlus12R revie... Whois “Indian\"in.. vA »\\n\\n— —— option.\\nWas this part or your degree or © You must select a valid 4\\n. . please choose v . &\\nextracurricular activity?* option.\\nif other, please specify o &\\n“PF add Research Experience &)\\ncA\\nScientific interests and motivation* .\\nWe consider this section to be the most cr)\\nimportant! Please write a short essay 2s You have to enter &\\ndescribing why you chose to study biology or something in field\\na related field. Which area(s] of biology do you ‘Scientific interests and es\\nfind most interesting or challenging and what motivation’ until end of\\ndo you expect from the summer school deadline. @\\nprogram in Vienna? (we expect approx. 500\\n\\nwords]\\n\\nN\\n<9\\n\\nHonours, Scholarships, Awards @\\nPlease list any honours or awards that you\\nfeel relevant to this application, including the\\ndate. w\\nZ\\nWill you be able to attend the complete © You must select a valid\\n. please choose v ,\\nduration?* option.\\nFavourite labs ©\\n© You must select a valid &\\n\\na Favourite lah [1.)* nlease choose YY DD\\n',\n",
|
||
" 'Usage\\n\\nData preparation\\n\\nna sreparaton many inves: dowsing le erasing Hi: coract mati om fan gneaing\\nbat fom Hi cantet mati H-C aa douncaded am pe Ivnunctinn nh goyesiaerisee a?\\n\\nExtracting HI-C contact matrix from.hic tle\\nfrequent fe\\nNest tne patho the np a att esi tha Geta, Cala KRcbzere.h ie: Thea eis the path\\n\\nsoe Genser celle beers e\\n\\n‘Generating sub-matrx from HI-C contact matrix\\n\\n“The proces cits the hic contact mae f ech chrmceameint mtn submatices. May the path othe\\n‘put anit flesin the Gatopyatx stall aarplaah fie, where teint les the up fe er the\\nprovou ten PATH i the rot recta of the Fequence- matic fi and\\n\\nsee cetnpystria cr a appch e\\n\\nModel trai\\n\\n‘ring samp\\n\\nGet the norm _factor le, nteraction_trequency tle\\nSyouhave nt norm fact fe ard rat requncy fia pleac rn the flowing command, the generat\\n\\nGet the training positive samples\\n\\nchioho@hedpe ies the downed asses the plement fe Anu\\n\\nGot the training negative samples\\n\\nMerge postive samples and negative samples\\n\\nHere youneedto update the path th conasponig le and\\npytun merge. pritive nantive.oy o\\n\\nGet Taln-valdation-test sample\\n\\nHere youneedto update the path th onasponig le and\\n\\nyan trating. trasmal.ty o\\n\\n‘Training\\n\\neven fran tte 1 eidation#ste | e\\n\\nUse CGLoop to predict chromatin loops\\n* e\\n\\nClustering\\n“The chstrngmethadinpeslachihios Ju. combalbeslachy wae wed for hates seeing\\n\\nacm e\\n\\nUpdtethe hi apts theo le path andthe output path, an the fle donna rom\\n\\nits oth comfriapestachThon un.\\n\\ntee peatncuclusteren e\\n\\nOutput file format\\n\\n',\n",
|
||
" 'PANTHER\\n\\nClassification System\\n\\nthe mission of the FANT AEN Knowleagebase Is to support biomedical and other researcn by providing\\ncomprehensive information about the evolution of protein-coding gene families, particularly\\nprotein phylogeny, function and genetic variation impacting that function. Learn more\\n\\nPANTHER19.0 Released. Click for more details.\\n\\nsearch keyword Allv\\n\\nogni comate\\n\\nCurrent Release: PANTHER 19.0 | 15,683 family phylogenetic trees | 144 species | News\\n\\nPANTHER GENE LIST @) Customize Gene list Click to view Enhancer Data @)\\n\\nConvert List to: | -Select- v\\n\\nSend list to:\\n\\n-Select-\\n\\nv\\n\\nWhole genome function views\\n\\nDisplay:\\n\\nHits 1-30 of 224\\n\\nGene ID\\n\\nitems per page Refine Search\\n\\n1. ARATH|TAIR=locus=2201542|UniProtKB=Q9C8Y1\\n\\n2. ARATH|TAIR=locus=2146975]|UniProtKB=Q4V3D6\\n\\n3. ARATH|TAIR=locus=2026042|UniProtkKB=Q9C684\\n\\n4. ARATH|TAIR=locus=504954695]UniProtKB=P56773\\n\\n5. ARATH|TAIR=locus=2092880|UniProtKB=Q9LJD9\\n\\n6. ARATH|TAIR=locus=504954632|UniProtkKB=P62100\\n\\nMapped IDs\\n\\nAT1G66400\\n\\nAT5G25260\\n\\nAT1G51090\\n\\nATCG00720\\n\\nAT3G13520\\n\\nATCGO0080\\n\\nGene Name\\nGene Symbol\\n\\nPersistent id\\n\\nOrthologs\\n\\nProbable calcium-binding protein CML23\\nCML23\\n\\nPTNO00095717\\n\\northologs\\n\\nFlotillin-like protein 2\\nFLOT2\\nPTNO00351791\\northologs\\n\\nHeavy metal transport_detoxification\\nsuperfamily protein\\n\\nAtig51090\\n\\nPTN000517376\\n\\northologs\\n\\nCytochrome b6\\npetB\\nPTN0O01453610\\northologs\\n\\nArabinogalactan protein 12\\nAGP12\\nPTNO02147968\\northologs\\n\\nPhotosystem II reaction center protein I\\npsbI\\nPTNO02183701\\northologs\\n\\nPANTHER Family/Subfamily\\n\\nCALCIUM-BINDING PROTEIN CML23-RELATED\\n\\n(PTHR10891:SF794),\\n\\nFLOTILLIN-LIKE PROTEIN 1-RELATED (PTHR13806:SF31)\\n\\nHEAVY METAL TRANSPORT DETOXIFICATION\\n\\nSUPERFAMILY PROTEIN (PTHR47488:SF3)\\n\\nCYTOCHROME B (PTHR19271:SF16)\\n\\nARABINOGALACTAN PROTEIN 12 (PTHR34114:SF10)\\n\\nPHOTOSYSTEM II REACTION CENTER PROTEIN I\\n\\n(PTHR35772:SF1),\\n\\nF=3)\\nPANTHER Protein Class\\n\\ncalmodulin-related\\n\\nSpecies\\n\\nArabidopsis\\nthaliana\\n\\nArabidopsis\\nthaliana\\n\\nArabidopsis\\nthaliana\\n\\nArabidopsis\\nthaliana\\n\\nArabidopsis\\nthaliana\\n\\nArabidopsis\\nthaliana\\n',\n",
|
||
" 'Input VCF Samples Records No-ALTs SNPs MNPs Indels Others Multiallelic Sites M.SNP Sites\\n\\nsample1_bowtie.vcf 1 5675 0 3800 1198 359 330 14 0\\nsample2_bowtie.vcf 1 6789 0 3713 2252 318 514 14 0\\nsample3_bowtie.vcf 1 128 0 92 21 10 5 0 0\\nsample4_bowtie.vcf 1 2407 0 1886 196 260 77 13 0\\n\\nsample5_bowtie.vcf 1 8273 0 5442 2201 272 360 5 0\\n',\n",
|
||
" 'GSM4284451: HiChIP B73 leaf H3K27me3; Zea mays; OTHER\\n\\n1 ILLUMINA (NextSeq 500) run: 373.8M spots, 56.5G bases, 19.6Gb downloads\\nAccession: SRX7630540\\n\\nGSM4284450: HiChIP B73 leaf H3K4me3;_Zea mays; OTHER\\n\\n1 ILLUMINA (NextSeq 500) run: 326.8M spots, 49.4G bases, 17.1Gb downloads\\nAccession: SRX7630539\\n\\nGSM3398051: HiC maize Leaf-HiC rep2; Zea mays; _Hi-C\\n\\n1 ILLUMINA (NextSeq 500) run: 528.9M spots, 80.4G bases, 30.8Gb downloads\\nAccession: SRX4727418\\n\\nGSM3398050: HiC maize Leaf-HiC rep1; Zea mays;_Hi-C\\n1 ILLUMINA (NextSeq 500) run: 89.8M spots, 13.7G bases, 4.5Gb downloads\\nAccession: SRX4727417\\n',\n",
|
||
" 'Extracted Data Preview (Before Mapping):\\n\\nEmpty DataFrame\\n\\nColumns: [IDENTIFIER, level1, level2, level3, level4, level5, level6, level7, level8, protscriber, swissprot]\\nIndex: []\\n\\nSuccessfully mapped transcripts to genes: 0\\n\\nUnmapped transcripts (IDENTIFIER is NaN after merge): @\\n\\nFinal DataFrame after Mapping:\\n\\nEmpty DataFrame\\n\\nColumns: [level1, level2, level3, level4, level5, level6, level7, level8, protscriber, swissprot, IDENTIFIER]\\nIndex: []\\n\\nTotal valid gene rows: @\\n\\n',\n",
|
||
" 'Mean Methylation Levels - CHH Context\\n\\nMean Methylation Levels - CHG Context\\n\\nMean Methylation Levels - CG Context\\n\\n10.\\n\\n19107 wow now uo,\\n\\n0.00:\\n\\nToro7 vow now Ueor,\\n\\noo\\n\\ntoro von anoy ueoy,\\n\\noo\\n\\nFile\\n\\nFile\\n\\nFile\\n\\n',\n",
|
||
" 'Mean Methylation Levels - CG Context\\n\\nMean Methylation Levels - CHG Context ‘Mean Methylation Levels — CHH Context\\n0.00)\\n\\n0.00: in\\n$ $ $\\n\\nFile\\n\\n8\\n\\nMean Methylation Level\\n\\nMean Methylation Level\\nMean Methylation Level\\n\\nEH\\n\\nFile\\n\\nFile\\n',\n",
|
||
" '. 7 sad\\n\\nEthylene induces expression of ACS genes during ripening\\n\\nACS ACO\\n\\nSAM LEACSS ACC — > C2H, — Perception\\nDS\\nLEACS1A —\\n4 LEACS4 =e)\\nLEACS2\\n\\nDevelopmentally\\nregulated\\n\\nBrigitte Poppenberger (TUM) Plant Cell teaching tool\\n',\n",
|
||
" '@ Terminal Shell Edit View Window Help SU GB O+ 8 © & DW ® F Q B®B SatFeb15 12:38\\n\\nee@ \"> aman — root@888302f1121e: / — ssh -L 9005:localhost:9005 aman@10.162.143.69 — 208x63\\n\\n(aman_fithic2) aman@unicorn:~/fihic_bias/fit_2$ cd\\n(aman_fithic2) aman@unicorn:~/fihic_bias$ mkdir fit_3_25000\\n(aman_fithic2) aman@unicorn:~/fihic_bias$ cd fit\\n\\nfit_2/ fit_3_25000/ fithic_res/\\n\\n(aman_fithic2) aman@unicorn:~/fihic_bias$ cd fit\\n\\nfit_2/ fit_3_25000/ fithic_res/\\n\\n(aman_fithic2) aman@unicorn:~/fihic_bias$ cd fit\\n\\nfit_2/ fit_3_25000/ fithic_res/\\n\\n(aman_fithic2) aman@unicorn:~/fihic_bias$ cd fit_3_25000/\\n(aman_fithic2) aman@unicorn:~/fihic_bias/fit_3_25000$ 1s\\n(aman_fithic2) aman@unicorn:~/fihic_bias/fit_3_25000$ cd\\n(aman_fithic2) aman@unicorn:~/fihic_bias$ fithic -i fithic.interactionCounts.gz -f fithic.fragmentMappability_filtered.gz -o fit_3_25000 -r 25000\\n\\nGIVEN FIT-HI-C ARGUMENTS\\n\\nReading fragments file from: fithic.fragmentMappability_filtered.gz\\nReading interactions file from: fithic.interactionCounts.gz\\nOutput path being used from fit_3_25000\\n\\nFixed size option detected... Fast version of FitHiC will be used\\nResolution is 25.@ kb\\n\\nNo bias file\\n\\nThe number of spline passes is 1\\n\\nThe number of bins is 100\\n\\nThe number of reads required to consider an interaction is 1\\n\\nThe name of the library for outputted files will be FitHiC\\n\\nUpper Distance threshold is inf\\n\\nLower Distance threshold is @\\n\\nOnly intra-chromosomal regions will be analyzed\\n\\nLower bound of bias values is 0.5\\n\\nUpper bound of bias values is 2\\n\\nAll arguments processed. Running FitHiC now...\\n\\nReading the contact counts file to generate bins...\\n\\n',\n",
|
||
" 'fecha. x1\\n# juicer_tools version 2.20.00\\n\\n10\\n10\\n10\\n10\\n10\\n10\\n10\\n10\\n10\\n10\\n10\\n10\\n10\\n10\\n10\\n10\\n10\\n10\\n10\\n10\\n10\\n10\\n\\n2765000\\n2770000\\n5555000\\n5695000\\n6095000\\n6100000\\n8660000\\n10165000\\n10310000\\n13070000\\n13795000\\n15730000\\n18680000\\n39615000\\n68055000\\n68165000\\n70185000\\n76000000\\n76000000\\n76000000\\n76005000\\n76080000\\n\\nx2 chr2 yl y2\\n2770000 10 2855000\\n2775000 10 2855000\\n5560000 10 5600000\\n5700000 10 5745000\\n6100000 10 6205000\\n6105000 10 6200000\\n8665000 10 8710000\\n10170000 10\\n10315000 10\\n13075000 10\\n13800000 10\\n15735000 10\\n18685000 10\\n39620000 10\\n68060000 10\\n68170000 10\\n70190000 10\\n76005000 10\\n76005000 10\\n76005000 10\\n76010000 10\\n76085000 10\\n\\nname\\n\\nscore strand1 strand2 color\\n\\n2860000 .\\n2860000 .\\n5605000 .\\n5750000 .\\n6210000 .\\n6205000 .\\n8715000 .\\n\\n10215000\\n10415000\\n13140000\\n13875000\\n15875000\\n18980000\\n39665000\\n68110000\\n68210000\\n70275000\\n76075000\\n76080000\\n76295000\\n76080000\\n76300000\\n\\n10220000\\n\\n10420000\\n13145000\\n13880000\\n15880000\\n18985000\\n39670000\\n68115000\\n68215000\\n70280000\\n76080000\\n76085000\\n76300000\\n76085000\\n76305000\\n\\n0,0,0\\n0,0,0\\n0,0,0\\n0,0,0\\n0,0,0\\n0,0,0\\n0,0,0\\n\\n14.\\n13.\\n19.\\n16.\\n25.\\n25.\\n16.\\n\\n@®e00000\\n\\nobserved\\n2.8484473\\n2.8019228\\n3.4499938\\n3.304496\\n7.614149\\n8.526771\\n3.6395729\\n0,0,0 22.\\n0,0,0 17.\\n0,0,0 15.\\n0,0,0 16.\\n0,0,0 12.\\n0,0,0 13.\\n0,0,0 15.\\n0,0,0 15.\\n0,0,0 12.\\n0,0,0 12.\\n0,0,0 14.\\n0,0,0 19.\\n0,0,0 13.\\n0,0,0 19.\\n0,0,0 28.\\n\\nS®STZ2R2R00R0 0079 FZ9FGOOO\\n\\nexpectedBL expectedDonut\\n\\n1.5692596 =1.5375078\\n1.7968416 2.2269118\\n3.9897149 2.2235892\\n3.0060778 2.7725399\\n5.428852 5.5150676\\n6.1781116 8.0178995\\n2.602374 4.4186635\\n\\n5.3042207\\n- 966549\\n\\n-4758284\\n- 233974\\n\\n-8419898\\n- 3655438\\n-6407552\\n-0511525\\n-5277498\\n-6813624\\n-1110115\\n- 2621875\\n-4505644\\n-6013317\\n-5075128\\n\\nWERNAWNNWWWNHARA\\n\\n4.\\n- 2396107\\n- 7605457\\n-45474 3\\n-4406276\\n- 9449823\\n- 956256\\n\\n- 3884587\\n- 0007367\\n-3635445\\n- 6045394\\n-5926347\\n- 1455631\\n«7987475\\n- 3968344\\n\\nWERNANNWWNHREN WR W\\n\\nROAR Wr w\\n\\n467083\\n\\nexpectedH\\n\\n- 0206969\\n- 318586\\n\\n- 7350693\\n-8232822\\n\\n-937725 9.\\n-44848 5.0 4.\\n6.\\n5.\\n1.\\n-2508504 3.\\n1.\\n-8126484\\n- 2533255\\n«7155979\\n- 9768908\\n-604569\\n. 1888733\\n- 2330713\\n- 6866648\\n-4135013\\n- 4300237\\n\\nBAIN AYWNNWBwWR\\n\\n4\\n4\\n- 7603672 Bo\\n5\\n8\\n\\n9828734\\n510576\\n4248123\\n\\n7314589\\n\\nC2020000\\nBOAONNBRANB\\n\\nexpectedV\\n\\n3\\n3\\n3\\n2\\n1\\n1\\n2\\n2\\n2\\n7\\n\\nC2020000\\n\\noN RwWwR\\n@®e0000\\nGor aanwsar\\n\\n6.0\\n\\n- 8588386\\n-0334935\\n2.\\n64701996.\\n2.\\n-638855\\n-2118154\\n-4243634\\n-6311734\\n-893925\\n- 0509053\\n- 7284713\\n-8052623\\n-817095\\n-032841\\n\\n3047068\\n\\n9420846\\n\\neeo0000\\n\\nbinBL — binDonut binH binv fdrBL fdrDonut fdrH fdrv\\n\\n@.007231753 3.511131E-5 1.9548146E-5 ©.006704267\\n©.02108529 0.001280647 0.004917699 0.0043853577\\n1.0508959E-4 6.092941E-4 6.452669E-7 1.3179086E-4\\n@.004152807 4.5260452E-4 6.2398956E-4 0.022592077\\n©.0010238877 1.0042442E-5 1.7484246E-5 2.9945571E-5\\n©.014633766 1.0042442E-5 @.01187411 0.012233345\\n\\n-004152807 4.5260452E-4 ©.022048308 0.022592077\\n\\n7.0 6. 7.0 6.373815E-4 2.3349234E-5 ©.009383285 7.008117E-4\\n\\n-0 0.05373638 8.387228E-4 0.05196979 0.008875866\\n\\n-0 7.359654E-6 5.599989E-5 2.8592735E-6 3.250222E-4\\n-024700472 0.0023998255 ©.0042031608 ©.0041892575\\n©.05408059 0©.014277156 0.0038882105 0.051242866\\n-07148571 ©.001280647 9.5542724E-4 @.07015614\\n-011898292 0.0016150654 @.01145778 0.011900691\\n\\n- 0028305573 ©.0065909075 @.01145778 0.011900691\\n-05408059 0©.03499214 0.053481553 0.051242866\\n-05408059 ©.014277156 0.053481553 0.003837532\\n-007231753 0.004752364 0.027641792 7.202292E-4\\n\\n- 0099850055 6.092941E-4 0.08652892 1.2947746E-5\\n-005758649 0.0047651175 0.020639945 0.018980222\\n\\n- 0016764826 6.092941E-4 0.08652892 1.2947746E-5\\n-0440846E-10 5.906181E-11 3.4802757E-5 2.6119373E-5\\n\\nSCOSTOZTR0R0DRDR00R000 000\\nCOOROAHOARARHHNNOBNO\\nSFZ0Z200000 070070000\\nORRRONKRUAHAKROOYWO\\nS220 0000000\\n\\nSC2OZT0TR0R0R0DRZ20 0000 0\\nN®2SZZ7ZRZROZ00\\n\\nDAARWARWAHANWAN GY\\n',\n",
|
||
" '[pst14@frontend ref_gen]$ head -n 3@ alignment_stats_5.txt | grep *SN | cut -f 2-\\nraw total sequences:\\nfiltered sequences:\\nsequences: 359012\\nis sorted: 1\\n\\n1st fragments: 179506\\nlast fragments: 179506\\n\\nreads\\nreads\\nreads\\nreads\\nreads\\nreads\\nreads\\nreads\\n\\nMapped: 356885\\nmapped and paired:\\nunmapped: 2127\\nproperly paired:\\npaired: 359012\\nduplicated:\\n\\nMQe: 3635\\n\\nQC failed:\\n\\nnon-primary alignments:\\n\\ntotal\\ntotal\\ntotal\\nbases\\nbases\\nbases\\nbases\\n\\nlength: 46565041\\nfirst fragment len\\nlast fragment leng\\nMapped: 46334456\\nmapped (cigar):\\ntrimmed: 0\\nduplicated:\\n\\nmismatches: 1083361\\nCpst14efrontend ref_gen]$\\n\\n359012\\n7)\\n\\n354954 # paired-end technology bit set + both mates mapped\\n\\n278242 # proper-pair bit set\\n# paired-end technology bit set\\n\\n(7) # PCR or optical duplicate bit set\\n# mapped and MQ=@\\n\\nQ\\n\\n7)\\n\\n# ignores clipping\\ngth: 23283802 # ignores clipping\\n\\nth: 23281239 # ignores clipping\\n# ignores clipping\\n45188724 # more accurate\\n\\nQ\\n# from NM fields\\n\\n',\n",
|
||
" 'reslFC <- 1fcshrink(ddsHTSeq, coef=\"condition_0G_vs_16\", type=\"apeg]m™)\\nreslFc\\n\\nres\\n\\nresordered <- restFc[order(resLFcSpvalue),)\\nresordered\\n\\num(restrcSpadj < 0.1, na.rm=TRUE)\\nsummary (resLFC)\\n\\nres0S <- results(ddsHTseq, alpha=0.05)\\nsummary (res0S)\\n\\nsum(resOSSpadj < 0.05, na.rm=TRUE)\\nplowma(res, ylim=c(-2,2))\\n\\nplotma(restec, ylim-c(-2,2))\\n',\n",
|
||
" 'an.ipynb & prediction (1).csv x\\n\\nrs nloads > B diction (1).csv\\n\\n1 hr sl e1 Chr s2 e2 prob interacted\\n\\n2 2 22526446 22528946 2 22814566 22817066 0.07493626 0\\n=] 1 =266059450 266061950 1 268070606 268073106 @.00105671 0\\n4 7 147503328 147505828 8 128360720 128363220 0.11050791 0\\n5 6 83428606 83431106 6 83561424 83563924 @.05532939 0\\n6 2 136764680 136767180 2 194169328 194171828 0.02850886 0\\n7 4 69078758 69081258 B73V4_ctgl23 40538 © 43038 :««8.97231692 1\\n8 3 59710226 59712726 9 103061696 103064196 @.79198885 1\\n9 2 238896326 238898826 7 180405930 180408430 0.91551584 1\\n10 6 43225786 43228286 8 58523256 58525756 @.41623008 0\\n11 2 112828 115328 9 8854860 8857360 0.48401883 0\\n\\n12 1 56612510 56615010 2 123349824 123352324 @.91257286 1\\n13 1 4743676 4746176 2 29714932 29717432 @.00028278 @\\n\\n14-2 183204290 183206790 10 «29734998 =-29737498 += 0.16519122 0\\n15 5 94957556 94960056 8 106032644 106035144 0.01354055 0\\n16 1@ 101151746 101154246 10 117103810 117106310 @.04067870 0\\n17 7 86905866 86908366 7 87323180 87325680 :0.96810138 1\\n18 9 157235660 157238160 9 157246142 157248642 @.00085247 0\\n19 9 97811166 97813666 9 97873470 97875970 0.28599653 0\\n20 «7 «36804356 «= «36806856 © 7 ©«=—«3 7915560 «37918060 © —-0.00084740 0\\n2 2 201394662 201397162 2 201951932 201954432 @.42819530 0\\n22 5 146959838 146962338 5 147308546 147311046 + 0.01540282 0\\n23 1 138982386 138984886 1 139298800 139301300 @.00286249 0\\n24 2 228932330 228934830 2 «242882804 242885304 0.00006837 0\\n25 3 169303632 169306132 3 170629794 170632294 0.00313403 0\\n26 5 173224780 173227280 «9 14831228 14833728 @.50576568 1\\n27 10 103275036 103277536 10 103935806 103938306 0.76628095 1\\n28 3 60123814 60126314 «3-6 1538358 «61540858 —-0.90330076 1\\n29 6 92918356 92920856 6 92926284 92928784 0.00082037 0\\n30 2 164106720 164109220 4 189484316 189486816 0.41017136 0\\n31 5 180621622 180624122 = 180767554 180770054 @.00002511 8\\n32 3 94155899 94158390 © 4 «= 71318030 © 71320530 ©=—0.02578691 0\\n33 10 106788698 106791198 10 123073314 123075814 0.31578454 0\\n34 6 22939020 22941520 6 22992588 22995088 @.00065063 0\\n35 9 24786776 24789276 9 24842666 24845166 + -0.00202687 0\\n36 3 45261222 45263722 3 45953012 45955512 @.00176121 0\\n37 1 121210690 121213199 1 126284536 126287036 © 0.99455833_ 1\\n38 3 124491786 124494286 4 220772328 220774828 @.06378701 0\\n39 2 138884816 138887316 2 149911138 149913638 @.00028917 0\\n40 8 173874918 173877418 9 149947670 149950170 0.12730879 0\\n41 7 61406108 + 61408608 += 7-«491334040 «91336540 «0. 00237168 0\\n42 4 =201057508 201060008 864 212441548 212444048 0.63278008 1\\n431 259884132 259886632 1 260219904 260222404 0.00110413 0\\n\\n',\n",
|
||
" '/mnt/storage3/aman/wdbasejuicer_new/aligned$ samtools flagstat merged_dedup.bam\\n1145414295 + @ in total (QC-passed reads + QC-failed reads)\\n\\n1095625712 + @ primary\\n\\n49788583 + @ secondary\\n\\n@ + @ supplementary\\n\\n45529887 + @ duplicates\\n\\n45128804 + @ primary duplicates\\n\\n1136307238 + @ mapped (99.20% : N/A)\\n1086518655 + @ primary mapped (99.17% : N/A)\\n1095625712 + @ paired in sequencing\\n\\n547812856 + @ read1\\n\\n547812856 + @ read2\\n\\n@ + @ properly paired (0.00% : N/A)\\n\\n1082345910 + @ with itself and mate mapped\\n\\n4172745 + @ singletons (@.38% : N/A)\\n\\n665445414 + @ with mate mapped to a different chr\\n182215303 + @ with mate mapped to a different chr (mapQ>=5)\\n',\n",
|
||
" 'PRELIMINARY TITLE IN GERMAN (EXCEPT FOR ENGLISCH DEGREE PROGRAMS A GERMAN TITLE IS\\nMANDATORY FOR THESIS REGISTRATION)\\n\\nThe final title will be requested again when uploading the thesis. Please pay attention to spelling, italics etc. when uploading. PLEASE CHECK\\nCAREFULLY IF EVERYTHING IS ENTERED CORRECTLY.\\n\\nB I U S xX, x® | fh Source\\n\\nQ\\n\\nThis CKEditor 4.14.0 version is not secure.\\n\\nConsider , 4.25.1-Its.\\n\\nPRELIMINARY TITLE IN ENGLISH (MANDATORY FOR THESIS REGISTRATION)\\n\\nThe final title will be requested again when uploading the thesis. Please pay attention to spelling, italics etc. when uploading. PLEASE CHECK\\nCAREFULLY IF EVERYTHING IS ENTERED CORRECTLY.\\n\\nB I U S xX, x® | fh Source\\n\\nQ\\n\\nThis CKEditor 4.14.0 version is not secure.\\n\\nConsider , 4.25.1-Its.\\n',\n",
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" \"In [712]: |# checking metadata first\\nhead (colnames (patient3_transform@meta. data) )\\n\\n‘orig.ident': 'nCount_RNA': 'nFeature_RNA'- 'percent.mt': 'nCount_SCT'- 'nFeature_SCT'\\n\",\n",
|
||
" 'Read Counts\\n\\nStatistics of Read Alignments — R1 Tags\\n\\n6e+08\\n\\n4e+08\\n\\n2e+08\\n\\nOe+00\\n\\nNot aligned (%)\\n\\n6% 6%\\n30.3 %\\n94%\\n63.6 %\\ndata\\nTrimmed read Mapping (%) Full read mapping (%)\\n\\nStatistics of Read Alignments — R2 Tags\\n\\n6e+08\\n\\n4e+08\\n\\nRead Counts\\n\\n2e+08\\n\\nOe+00\\n\\nAligned reads (%)\\n\\n7% 7%\\n30.2 %\\n93 %\\n62.8 %\\ndata\\n',\n",
|
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" 'STUDY INFORMATION SYSTEM Quick links” Give feedback! @ ENGIEST Q Search for study programme or course\\n\\nAman Shamil Nalakath Usi-io\\nTak General information y | My study information v acd | TALLINNA, —, ®\\n245633LV v\\n\\nMy study information / Student performance records /\\n\\nStudent Code\\nAman Shamil Nalakath 245633LV\\nECTS credits as of 14.01.2025 Grade point average\\n6.00 5.000\\nBy semesters All\\n- 2024/2025 Autumn\\nCourse title Course code ECTS C/E Grade Date Lecturer All right? Remarks\\nBioinformatics II LKGOO50 6.0 E 5 23.12.2024 Airi Rump yes -\\n\\nTotal: 6.0 ECTS GPA: 5.00\\n',\n",
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" 'b # Separate upregulated and downregulated genes\\nupregulated_genes <- deg_df %>%\\nfilter(log2FoldChange > 1)\\ndownregulated_genes <- deg_df %>%\\nfilter(log2FoldChange < -1)\\n\\nenrich_up <- enricher(\\n\\ngene = upregulated_genes,\\npvalueCutoff = 0.1,\\npAdjustMethod\\nminGSSize = 10,\\nmaxGSSize = 500,\\nqvalueCutoff = 0.2,\\nTERM2GENE = tempset)\\n\\nenrich_down <- enricher(\\ngene = downregulated_genes,\\npvalueCutoff = 0.1,\\npAdjustMethod \"BH\",\\nminGSSize = 10,\\nmaxGSSize = 500,\\nqvalueCutoff = 0.2,\\nTERM2GENE = tempset)\\n\\n[9] v 0.7s\\n\\n--> No gene can be mapped....\\n\\n--> Expected input gene ID: HORVU.MOREX. r3.7HGQ752800, HORVU.MOREX. r3.5HG@422710, HORVU.MOREX. r3.5HG0422720, HORVU.MOREX. r3.7HG@740470, HORVU.MOREX. r3.2HGQ102060, HORVU.MOREX. r3.2HG0111040\\n--> return NULL...\\n\\n--> No gene can be mapped....\\n\\n--> Expected input gene ID: HORVU.MOREX. r3.3HGQ322690,HORVU.MOREX. r3.2HG@102080, HORVU.MOREX. r3.3HG0324450, HORVU.MOREX. r3.6HG@577620, HORVU.MOREX. r3.5HG0422720, HORVU.MOREX. r3.5HG0486780\\n\\n-—-> return NULL...\\n',\n",
|
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" 'Initial Assumptions\\n\\n1. Genotypic frequencis in the parent population are\\n+P for homozygous dominant (AA),\\n+ H ter heterozygous (Aa),\\n\\n+ Q tor homozygous recessive (aa). These frequencies satsty P+ H+ Q =\\n\\n2. Allele frequencies are:\\n+ forthe dominant allel (4),\\n\\n+ qforthe recessive allele (a). These satisy p-+-q = 1, where\\n\\n1 1\\nP=P4SH and q=Q4 5H.\\n\\nProgeny Genotypes\\n\\n‘The offspring genotypes are determined by random mating, and ther frequencies ar calculated\\nusing probabilties of inheriting each alle\\n\\n1. Progeny genotype AA:\\n+ Ad aries when\\n+ AAx AAsfrequency P*,\\n+ AA x Aa: trequency PH,\\n+ Ae Aas trequeney HEP\\n+ dng these gives the total fequency for AA\\nPty pHs tt\\nrr\\n2. Progeny genotype Aa:\\n+ AA x Aa: frequency PH,\\n+ Aa Aas frequency $1,\\n+ Aa x aa: frequency HQ.\\n\\n1+ Adding these gives the total requency for Aa:\\n\\napa s bit HQ\\n23 Progeny genotype aa:\\n\\n+ aa x aa: frequency Q*,\\n\\n+ Aa aa: reqency HQ,\\n\\n+ Aa x Aa: frequency $12\\n\\n+ Adding these gives the total frequency for aa\\n\\nQn + br\\n\\nSimplifying Using Allele Frequencies\\n1. Substiutng p= P+ $H andq = Q+\\n+ For Ad: = (P 4 4a)\\n+ For Aa: 2pq = 2(P + $41)(Q + 4)\\n+ Foraasa? = (Q+ HH)\\n\\n2. These terms directly correspond to the bottom row ofthe table, representing the expected\\nproportions of genotypes in the next generation.\\n',\n",
|
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" 'Research Experience (2)\\nInstitution*\\n\\nStreet*\\n\\nPostal Code*\\n\\nCity*\\n\\nCountry*\\n\\nProject title/Research Area*\\nSupervisor\\n\\nTitle O\\n\\nFirstname* ©)\\n\\nLastname* ©)\\n\\nEmail address ©\\n\\nWill this supervisor provide a reference?\\nDate from*\\n\\nDate to*\\n\\nTechnical Skills Acquired*\\n\\nWere you working full-time or part-time?*\\n\\nGermany\\n\\nChromatin loops in Maize Genome\\n\\nyes v\\n\\n2024-09-16\\n\\n2025-02-17 =\\n\\nfull-time v\\n',\n",
|
||
" 'Db at\\n\\nfor i in xbowtie.vcf; do\\nvt peek \"$i\"\\necho “Analysis complete for $i\\necho \"\"\\n\\ndone\\n\\n[22] bash\\n\\npeek v@.5\\n\\noptions: input VCF file samplel_bowtie.vcf\\n\\nstats: no. of samples\\nno. of chromosomes : 21\\n\\n= Micro variants\\n\\nno. of SNP : 3784\\n2 alleles : 3784 (2.28) [2629/1155]\\nno. of MNP : 332\\n2 alleles : 331 (1.95) [465/238]\\n3 alleles : 1 (inf) [5/0]\\nno. of INDEL : 353\\n2 alleles : 353 (1.08) [183/170]\\nno. of SNP/MNP : 11\\n2 alleles : 11 (@.83) [5/6]\\nno. of SNP/INDEL : 86\\n2 alleles : 82 (@.82) [37/45] (1.00) [41/41\\n3 alleles : 4 (1.00) [2/2] (4.00) [4/1\\nno. of MNP/INDEL : 40\\n2 alleles : 38 (@.54) [40/74] (@.65) [15/23]\\n3 alleles : 2 (6.00) [6/1] (2.00) [2/1\\n\\n',\n",
|
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" '@FastQC Report\\n\\nSummary\\n\\nQeasic Statistics\\nOre base sequence quality\\n\\nOber sequence quality scores\\n\\nOber base sequence content\\nQeer sequence GC content\\nOeer base N content\\n\\nQ sequence Length Distribution\\nQseauence Duplication Levels\\nQoverrepresented sequences\\nQadapter Content\\n\\nOkmmer Content\\n\\nQbasic Statistics\\n\\na\\n\\nFilename\\n\\nFile type\\n\\nEncoding\\n\\nTotal Sequences\\n\\nSequences flagged as poor quality\\nSequence length\\n\\n%GC\\n\\nwood_sample_5_forward_paired. fq.gz\\nConventional base calls\\n\\nSanger / Illumina 1.9\\n\\n179506\\n\\n)\\n\\n30-150\\n\\n37\\n\\n@per base sequence quality\\n\\nQuality scores across all bases (Sanger / Illumina 1.9 encoding)\\n\\n40\\n\\n16\\n\\n14\\n12\\n10\\n\\noN B&O\\n\\n12345 67 8 9 1519\\n\\n30-34 45-49 60-64 75-79 90-94 105-109 120-124 135-139 150\\n',\n",
|
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" 'Project 4: Phylogenetic Analysis\\n\\nPhylogenetic analysis is a crucial aspect of evolutionary biology and bioinformatics that\\ninvolves studying the evolutionary relationships among organisms. This project idea offers\\nopportunities for both undergraduate (UG) and postgraduate (PG) students to engage in\\nphylogenetic analysis, starting with constructing basic phylogenetic trees and progressing\\nto more complex methods.\\n\\nBioinformatics Project Ideas — Undergraduate Level: Construct a Simple\\nPhylogenetic Tree\\n\\nAt the undergraduate level, students can begin by constructing a basic phylogenetic tree\\nbased on a gene or protein sequence. This project provides a foundational understanding of\\nphylogenetics and evolutionary relationships.\\n\\nSteps for UG Students:\\n\\n1. Gene or Protein Selection: Choose a gene or protein of interest that is well-\\ndocumented and has sequences available for multiple organisms.\\n\\n2. Sequence Alignment: Align the sequences of the chosen gene or protein using\\nsoftware like ClustalW or MAFFT to identify conserved regions.\\n\\n3. Phylogenetic Tree Construction: Utilize software such as MEGA or PhyML to construct\\na phylogenetic tree based on the aligned sequences. Apply methods like neighbor-\\njoining or maximum parsimony.\\n\\n4. Tree Visualization: Visualize the phylogenetic tree, highlighting the evolutionary\\nrelationships among the organisms.\\n\\n5. Interpretation: Gain insights into the evolutionary history and relatedness of the\\norganisms based on the tree’s topology. Consider factors like branching patterns and\\nbranch lengths.\\n\\nPostgraduate Level: Complex Phylogenetic Analyses and Co-evolutionary Patterns\\n\\nBioinformatics Project Ideas — For postgraduate students, the project can advance to more\\ncomplex phylogenetic analyses, incorporating maximum likelihood methods and exploring\\nco-evolutionary patterns among genes or organisms.\\n\\nAdditional Steps for PG Students:\\n\\n1. Maximum Likelihood Analysis: Learn and apply maximum likelihood methods for\\nphylogenetic tree reconstruction, which offer more accurate models of sequence\\nevolution. Software packages like RAXML or PhyML can be used.\\n\\n2. Molecular Clock Analysis: Investigate the concept of molecular clocks to estimate\\ndivergence times between species. This involves incorporating evolutionary rates into\\nphylogenetic analyses.\\n\\n3. Co-evolutionary Analysis: Explore co-evolutionary patterns between genes, proteins,\\nor organisms using tools like Coevol or CAPS. Understand how changes in one\\ncomponent correlate with changes in another.\\n\\n4. Advanced Tree Visualization: Use advanced tree visualization tools to create\\ninformative and publication-quality figures. Highlight key evolutionary events or\\nrelationships.\\n\\n5. Biological Interpretation: Analyze the implications of the phylogenetic findings. How\\ndo the results contribute to our understanding of evolutionary processes, adaptations, or\\nco-evolutionary dynamics?\\n\\n6. Publication and Presentation: Encourage PG students to disseminate their findings\\nthrough research publications or presentations at scientific conferences, contributing to\\nthe field of evolutionary biology and phylogenetics.\\n\\nIn summary, phylogenetic analysis projects offer a captivating journey into the study of\\nevolutionary relationships among organisms. These projects provide valuable insights into\\nthe evolutionary history of genes, proteins, and species, and they equip students with\\nessential skills in bioinformatics and computational biology. Additionally, complex\\nphylogenetic analyses enable postgraduate students to explore cutting-edge methods and\\ncontribute to our understanding of co-evolutionary dynamics in biology.\\n\\nProject 5: Drug Discovery and Virtual Screening\\n\\nDrug discovery is a multidisciplinary field that combines biology, chemistry, and\\ncomputational methods to identify and design potential drug candidates. This project idea\\nprovides opportunities for both undergraduate (UG) and postgraduate (PG) students to\\nexplore the exciting world of drug discovery, starting with basic virtual screening\\nexperiments and progressing to advanced structure-based drug design.\\n\\nUndergraduate Level: Basic Virtual Screening\\n\\nAt the undergraduate level, students can start by learning about drug databases and\\nconducting basic virtual screening experiments to identify potential drug candidates. This\\nproject offers an introduction to the concepts and tools used in drug discovery.\\n\\nSteps for UG Students:\\n\\n1. Drug Database Exploration: Familiarize yourself with drug databases like PubChem or\\nDrugBank. Select a target protein of interest, preferably one with known drug-binding\\nsites.\\n\\n2. Ligand Preparation: Retrieve ligand molecules (small compounds) from the database\\nthat may potentially bind to your target protein. Prepare the ligands by removing any\\nirrelevant atoms or functional groups.\\n\\n3. Protein-Ligand Docking: Utilize software tools like AutoDock or PyRx to perform\\n',\n",
|
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" '.\\n\\n@ Vivaldi File Edit View Bookmarks Mail Tools Window’ Help\\n\\nCB —-7> a 98\\n\\ndeepl.com,\\n\\n+\\n\\n8 Translate files «~ DeepL Write\\n.pdf, .docx, .pptx *\" Al-powered edits\\n\\nx Translate text\\nA 35 languages\\n\\nGerman (detected) v\\n\\n>) DeepL Products v Solutions v Pricing Apps v\\n\\nLieber Herr Nalkath, x\\n\\nbitte entschuldigen Sie, dass ich erst\\nheute antworte.\\n\\nWare es moglich in der Kommenden Woche am\\nDienstag, den 28.10.2025, 13.30 Uhr zur\\n\\nUnterschrift vorbei zu kommen?\\n\\nViele GriiRel\\n\\ny NAN A\\n\\nDictionary\\n\\nClick on a word to look it up.\\n\\nS Workspaces v < Startpage Search Results\\n\\n0 @0e eB\\n\\n& masters-prephd - Milanote Startpage Search Results\\n\\nEnglish (American) v\\n\\nDear Mr. Nalkath,\\n\\nPlease excuse me for only responding today.\\nWould it be possible to come by next week on\\nTuesday, October 28, 2025, at 1:30 p.m. to sign\\n\\nthe document?\\n\\nBest regards,\\n\\nq) oP Oo «\\n\\nChatGPT | Smart, Fast & Fre« 0% Mail - aman.nalakath@tum.d\\n\\n1B)\\n\\nEditing tools\\n\\nfl Formality\\nQ Clarify\\nCustomizations\\nGlossaries\\n99 Style rules\\nPowered by\\n\\n& Language model\\n\\nq) ay Reset —() a 100 %\\n\\n@ Wed 22. Oct 07:24\\n\\nNext-gen v\\n\\niS) DeepL Trans >\\n\\neor em@\\n\\ne\\n\\nj« ie\\n\\nQua eZ? DB\\n\\n07:24\\n',\n",
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" '114\\n115\\n116\\n117\\n118\\n119\\n120\\n121\\n122\\n123\\n124\\n125\\n126\\n127\\n128\\n\\nidx <- identify(resSbasemean, resSlog2Foldchange)\\nrownames (res) [idx]\\n\\nplotcounts(ddsHTSeq, gene-which.min(resSpadj), intgroup=\"condition™)\\n\\nd <- plotcounts(ddsHTseq, gene=which.min(resSpadj), intgroup=\"condition\",\\nreturnData=TRUE)\\nVibrary(\"ggplor2”)\\nggplot(d, aes(x=condition, yecount)) +\\n‘geom_pofnt (position=position_jitter(w=0.1,h=0)) +\\nscale_y_logl0(breaks=c(25,100,400))\\n\\nwrite. csv(as. data. frame(resordered),\\nfile=\"condition_0G_results. csv\")\\n',\n",
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" '156,000 KB 155,000 KB 154,000 KB 153,000 KB 152,000 KB 151,000 KB\\n\\n157,000 KB\\n\\n',\n",
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" \"Ricci et al.\\n\\nPage 25\\n\\na d\\nCee ||\\n\\nACRE\\n\\naKcRs\\n\\nc\\n\\nFakes HOH | FexaTme? HEMP\\n\\n[i 1 |\\npose oll Bat,\\nLoop odes per aACR, Loop o2ges per ako\\n\\neo oO ie\\n\\noP\\note\\n\\nFig. 3 |. Hi-C and HiChIP identify dACR-gene interactions.\\n\\na, Contact matrix heat maps showing the dACR-gene interactions at th/ and ZmRap2.7. Red\\narrows indicate dACR-gene contacts. b, Percent of intergenic-gene loop edges overlapping\\ndACRs. ** denotes denotes p<< 2.2e-16 (Fisher's exact test, two sided). Leaf Hi-C n = 1,177\\ntotal loops (withi ingle biological replicate), H3K4me3 HiChIP n = 24,141, and\\nH3K27me3 HiChIP n = 18,106. c, Representative region containing various HiChIP loops\\n(top panel) and called loop numbers from Hi-C and HiChIP experiments (bottom panel). d-\\ne, Regions demonstrating dACR interaction hubs (dACR anchors in shaded blue regions).\\nWhite squares in heat maps indicate loops. f-g, Percentages of dACRs involved in multiple\\ndACR-gene loops, compared to a control of shuffled dACRs and loops. From a total 6,939\\ndACRs (excluding the transcribed group dACRs), 2,809 dACRs looped with >=1 genes in\\nH3K4me3-HiChIP while 2,001 dACRs looped with >=1 genes in H3K27me3-HiChIP. h,\\nThe percentages of dACR-gene loops in which the dACR resides either upstream or\\ndownstream of the target gene's promoter. dACR-gene pairs which were not crossing gene(s)\\nwere used for the analysis. i, virtual 4C intrachromosomal interaction signals at dACR\\nsummits and flanking regions. j, Top panel: a representative eQTL-gene pair (black curve)\\nconnected with Hi-C/HiChIP loops (red curves). Bottom panel: the percent of eQTL-gene\\npairs that were connected by loops (red line), compared to genomic-distance-constrained\\n\\ndACR-gene random permutations (blue dots). P-values were determined by a two-sided\\npermutation test (n=100).k, Enrichment of DAP-seq peaks of the same TF in both edges of\\nthe same loop (dACR-gene loops only). The Red line indicates a p-value of 0.01 (Fisher's\\nexact test, two-sided). 1, Expression of genes involved in different dACR-gene loops,\\nseparated by HiChIP loop type. n = the number of genes shown in the violin distribution.\\nThe box plot shows median and quartiles. For the Hi-C and HiChIP experiments in this\\nfigure, biological replicates were not performed.\\n\",\n",
|
||
" 't Notes\\n\\nGenotype Boron treatment\\nW@cr2267\\n\\nlow [medium RIGA]\\n@cR2262\\n\\n” Crop\\n\\nPhysiology\\n\\n',\n",
|
||
" 'In [414]: length(combined.TCR_p4) # Should be 1\\nnrow(combined.TCR_p4[[1]]) # Likely 0\\n\\n1\\n\\n641\\n\\nIn [409]: combined_TCR <- combineTCR(\\nlist(patient3 = combined.TCR_p3, patient4 = combined.TCR_p4),\\nsamples = c(\"patient3\", \"patient4\"),\\nremoveNA = FALSE,\\nremoveMulti FALSE,\\nfilterMulti = FALSE\\n',\n",
|
||
" '@ @ @ cool_custom_script.py Signin v\\n= cool_custom_script.py € >? untitled cool.py ~/Documents cool_custom_script.py\\ncool_custom_script.py 2 Q* I x\\n\\n23 with open(\\'/Users/aman/lat_chrom.sizes\\', \\'w\\') as fsize: r\\n\\n24 for chrom in hic.getChromosomes():\\n\\n25 fsize.write(f\"{chrom.name$\\\\t{chrom. length}\\\\n\")\\n\\n26 if chrom.name ~ \"All\":\\n\\n27 fsize.write(f\"{chrom.name$\\\\t{chrom. length}\\\\n\")\\n\\n28 d# Then write the counts in text file:\\n\\n29 # adjustment made here - aman\\n\\n30 file_path = os.path.expanduser(\\'~/Downloads/bedpe.txt\\')\\n31 with open(file_path, ‘w\\') as fo:\\n\\n32 for i in range(len(chrom_sizes) ):\\n33 for j in range(i, len(chrom_sizes)):\\n34 chrom1 = chrom_sizes.index[i]\\n35 chrom2 = chrom_sizes.index[j]\\n36 result = hicstraw.straw(data_type, normalization, hic_file, chrom1, chrom2, \\'BP\\', resolution)\\n37 for k in range(len(result) ):\\n38 start1 = result[k] .binx\\n39 start2 = result[k].binY\\n40 value = result[k].counts\\naman — python3 /Users/aman/Docum... * + 7\\n\\nFile doesn\\'t have the given chr_chr map 1_263\\nFile doesn\\'t have the given chr_chr map 1_265\\nFile doesn\\'t have the given chr_chr map 1_266\\nFile doesn\\'t have the given chr_chr map 1_267\\nFile doesn\\'t have the given chr_chr map 2_234\\nFile doesn\\'t have the given chr_chr map 2_246\\nFile doesn\\'t have the given chr_chr map 2_254\\nFile doesn\\'t have the given chr_chr map 2_265\\nFile doesn\\'t have the given chr_chr map 2_266\\nFile doesn\\'t have the given chr_chr map 2_267\\nFilter... z |\\n\\ntS = $& vY Click to restart and update Zed 29:34 Python 3.13.0(Venv) Python && + @% 4\\n',\n",
|
||
" '1. Import 2. Filter 3. Plot 4. Measure\\n\\nSave prompt\\n\\nal\\n\\nDo you want to save a snapshot of your work?\\n\\nSelect an option: |no\\n\\nCancel OK\\n\\n5. Save\\n',\n",
|
||
" 'About Blog Examples Plugins Docs ©\\n\\n5e+4\\nSet+4\\n\\nte+4\\n6et3\\n\\n3e+3\\n\\nte+3\\n6e+2\\n\\nSet+2\\n\\nte+2 =\\n60 =\\n\\nchr5_chr5.mcool\\n[Current data resolution: 5.12M],\\n',\n",
|
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" 'GQAAAGP RPP PPP PPP PRP PPP PPP PP RS\\n\\n«/16\\n-717\\n-717\\n-717\\n-718\\n-718\\n-718\\n-718\\n-718\\n-719\\n-719\\n-719\\n-719\\n-720\\n-720\\n-720\\n-720\\n+721\\n- 786\\n-814\\n+917\\n-969\\n-969\\n-340\\n-341\\n-342\\n-343\\n-346\\n\\ninflating: hiC-Pro-master/scripts/onlarget.py\\n\\ninflating: HiC-Pro-master/scripts/plot_hic_contacts.R\\n\\ninflating: HiC-Pro-master/scripts/plot_hic_fragment.R\\n\\ninflating: HiC-Pro-master/scripts/plot_mapping_portion.R\\n\\ninflating: HiC-Pro-master/scripts/plot_pairing_portion.R\\n\\ninflating: HiC-Pro-master/scripts/split_valid_interactions.py\\n\\ncreating: HiC-Pro-master/scripts/src/\\n\\nextracting: HiC-Pro-master/scripts/src/README\\n\\ninflating: HiC-Pro-master/scripts/src/build_matrix.cpp\\n\\ninflating: HiC-Pro-master/scripts/src/cutsite_trimming.cpp\\n\\ncreating: HiC-Pro-master/test-op/\\n\\ninflating: HiC-Pro-master/test-op/config_test_as.txt\\n\\ninflating: HiC-Pro-master/test-op/config_test_cap.txt\\n\\ninflating: HiC-Pro-master/test-op/config_test_dnase.txt\\n\\ninflating: HiC-Pro-master/test-op/config_test_latest.txt\\n\\ninflating: HiC-Pro-master/test-op/run-test-op.sh\\nfinishing deferred symbolic links:\\n\\nHiC-Pro-master/doc/themes/paris/logos -> ../../_static/logos/\\nmake -f ./scripts/install/Makefile CONFIG_SYS=./config-install.txt prefix=/opt/hicpro\\nmake[1]: Entering directory \\'/opt/HiC-Pro-master\\'\\n./scripts/install/install_dependencies.sh -c ./config-install.txt -p /opt/hicpro -o /opt/hicpro/HiC-Pro_3.1.@ -q\\nMake sure internet connection works for your shell prompt under current user\\'s privilege ...\\nStarting HiC-Pro installation !\\nDirectory /opt/hicpro does not exist!\\nExit - Error - unable to install/check dependancies !\\nmake[1]: **x* [scripts/install/Makefile:41: configure] Error 1\\nmake[1]: Leaving directory \\'/opt/HiC-Pro-master\\'\\nmake: **x* [Makefile:38: configure] Error 2\\n\\n40 | # Install HiC-Pro\\n\\n41 | >>> RUN cd /opt && \\\\\\n\\n42 | >>> wget https://github.com/nservant/HiC-Pro/archive/master.zip -O hicpro_latest.zip && \\\\\\n\\n43 | >>> unzip hicpro_latest.zip && \\\\\\n\\n44 | >>> cd HiC-Pro-master && \\\\\\n\\n45 | >>> make configure prefix=/opt/hicpro && \\\\\\n\\n46 | >>> make install && \\\\\\n\\n47 | >>> 1n -s /opt/hicpro/bin/HiC-Pro /usr/local/bin/HiC-Pro && \\\\\\n\\n48 | >>> rm -rf /opt/hicpro_latest.zip /opt/HiC-Pro-master\\n\\n49 |\\nERROR: failed to solve: process \"/bin/sh -c cd /opt && wget https://github.com/nservant/HiC-Pro/archive/master.zip -O hicpro_latest.zip && unzip hicpro_latest.zip && cd HiC-Pro-master && make\\nconfigure prefix=/opt/hicpro && make install && ln -s /opt/hicpro/bin/HiC-Pro /usr/local/bin/HiC-Pro && rm -rf /opt/hicpro_latest.zip /opt/HiC-Pro-master\" did not complete successfully: exit code:\\n\\n2\\n',\n",
|
||
" 'Development of binary vectors that separate\\nT-DNA and vir genes\\n\\nT-DNA and the vir genes can be located on separate\\nlasmids or replicons, making cloning easier\\n\\nT-DNA _L#\\n\\nThe smaller\\nplasmid is\\n\\nBecause the Ti\\nplasmid is so large,\\n\\na binary system introduced into\\nBinary was developed to Agrobacterium\\nvector allow gene cloning carrying a helper\\ninto a smaller plasmid with the\\nplasmid vir genes\\nManipulated\\nin E. coli\\n\\ntigitte Poppenberger (TUM) Hoekema ef al & Schilperoort, 1983, Nature\\n',\n",
|
||
" 'ressig <- subset(resordered, padj < 0.1)\\nressig\\n\\nwrite. csv(as. data. frame(ressig),\\nfile=\"condition_0G_sig_results. csv\")\\n',\n",
|
||
" 'we Jockerhub Q Search Docker Hub\\n\\nore / toluene123/rstudio / latest\\n\\nfe\\n2\\n\\ntoluene123/rstudio:latest\\n\\nINDEX DIGEST sha256:73334d399a99fdc72e5684040043f79bb49006402c7c4edec59f0a9e375441c2 oO\\n\\nOS/ARCH COMPRESSED SIZE LAST PUSHED TYPE MANIFEST DIGEST\\nlinux/arm64 725.61 MB about 1 hour by toluene123 Image sha256:3fc9f2388... oO\\nImage Layers Command\\n\\n1 ARG RELEASE 6B ARG RELEASE\\n\\n2 ARG LAUNCHPAD_BUILD_ARCH @B\\n\\n3 LABEL org.opencontainers.image.ref.name=ubuntu @B\\n\\n4 LABEL org.opencontainers.image.version=24.04 @B\\n\\n5 ADD file ... in / 27.56 MB\\n\\n',\n",
|
||
" '| Bioinformatics analysis of 3D genomics and epigenetic data in cancer cells\\nDegree program Agricultural Biosciences - Master\\n\\nSemester -\\n\\nOrganization / Chair Professur fiir Populations-Epigenetik und Epigenomik\\nExaminer Prof. Dr. Frank Johannes\\n\\nOrganization / Chair (external) Chair of Translational Epigenetics & Genome Architecture, Institute of\\nPathology, University Medical Centre Gottingen\\n\\nSupervisor (external) Dr. Argyris Papantonis\\nDate of issue of the thesis 30.04.2025\\n\\nDate of submission of the thesis 30.10.2025 - 23:59:59\\n\\nPlease upload your thesis before this time.\\n\\nThe first examiner has agreed to your application.\\n\\nPlease wait for the approval from the examination board.\\n\\nAs soon as both fields change to green, the registration of your thesis is approved and you will receive an\\nOfficial confirmation via email. If no approval is received within 4 weeks, please contact either your first\\nexaminer or the Campus Office examination team, depending on which authority does not provide its\\n\\nagreement. Your application will only be submitted to the examination board for a decision once your examiner\\nhave given the approval.\\n\\nCreated on: 24.03.2025 - 17:26:03\\n\\n',\n",
|
||
" \"@ Terminal Shell Edit View Window Help\\n\\n4) FS Q S Sun 29. Jun 12:54\\n\\neee ~~ aman — a.nalakath@node08:~ — ssh -L 9006:localhost:9006 a.nalakath@10.152.154.1 — 208x61\\n\\nLast login: Sun Jun 29 11:01:36 on ttys@ee\\n\\naman@Laptop-von-Aman ~ % tum_ngs\\n\\nDRA A AA AA A A RRA A RA AA RR RRA A RRR RRA A AR AR AR HAA RR A\\n* Welcome to PGEN cluster *\\nDRA AA A AR A AA A RRA A AA ARR A RA RR A RR RRA AR A RR AA A HRA RR A\\n\\nPlease use this node only to submit your jobs.\\nDon't use it for calculations or CPU/RAM intensive tasks!!!\\n\\nDARA A AAA AA A A AA A RA AR A RR RRA A RRR RR ARR A HRA HAR A HAA RR A\\n(a.nalakath@10.152.154.1) Password:\\n\\nKickstarted on 2018-12-07\\n\\nLast login: Wed Jun 25 08:01:17 2025 from 10.157.58.238\\n[a.nalakath@frontend ~]$ ssh node@s\\n\\nPassword:\\n\\nKickstarted on 2018-12-04\\n\\nLast login: Wed Jun 25 08:01:32 2025 from 10.152.154.1\\n[a.nalakath@node@8 ~]$ tmux ls\\n\\n@: 1 windows (created Sat Jun 21 08:01:35 2025)\\n[a.nalakath@nodees ~1$ ff\\n\\n\",\n",
|
||
" 'O©OMNOORWONHE\\n\\nRPRWWWBWWWAWAWAAWANNNNNNHNNNNNBP RRB REE E EB\\nSSOORYDAUHGTREANRPCHOCBRINDAGTRANKHDCOBRNAATARwWNHHRO\\n\\n[Gene IbaseMean log2FoldChai lfcSE\\n\\nAT5G03200.1 12.0375435\\nAT5G47220.1 153.161775\\nAT3G29000.1 166.002658\\nAT4G00650.1 11.2572169\\nAT3G44990.1 223.865324\\nAT2G05070.1 1709.86898\\nAT2G42530.1 475.913349\\nAT5G63350.1 54.9735521\\nAT5G39670.1 114.785786\\nAT1G61470.1 22.7198363\\nAT1G80840.1 663.247412\\nATSG61600.1 2684.37627\\nAT5G47230.1 577.821307\\nAT4G25470.1 59.6532479\\nAT4G27280.1 6988.80195\\nATSG46350.1 22.8465007\\nAT3G50330.1 127.604924\\nAT1G72910.1 104.192446\\nAT1G35210.1 37.1600234\\nAT3G50930.1 504.178099\\nAT1G29920.1 4226.08984\\nAT3G50800.1 191.046459\\nAT3G44260.1 2666.75503\\nAT2G38470.1 3185.46548\\nATSG05965.1 123.391355\\nAT1G18300.1 651.925875\\nAT1G73540.1 696.801829\\nAT1G73010.1 372.969509\\nAT3G50060.1 1432.98416\\nAT5G43620.1 102.636467\\nAT1G72240.1 211.107872\\nAT4G24570.1 2042.71238\\nAT1G18570.1 891.44892\\nAT1G72920.1 244.955336\\nAT5G54270.1 3911.89175\\nATSG50360.1 54.1660564\\nAT2G21640.1 71.9274916\\nAT3G18080.1 1328.31866\\nAT4G17490.1 585.637163\\nAT1G69150.1 49.7470799\\n\\n-2.6682843\\n-1.8601178\\n-1.8062287\\n-1.690889\\n-1.638667\\n-1.5918919\\n-1.5727324\\n-1.5622046\\n-1.54785\\n-1.5462705\\n-1.5451851\\n-1.5309698\\n-1.5199074\\n-1.5139612\\n-1.5055291\\n-1.4922792\\n-1.4696279\\n-1.4463622\\n-1.438611\\n-1.4275128\\n-1.4266917\\n-1.4114142\\n-1.3608498\\n-1.3528153\\n-1.3387104\\n-1.2088777\\n-1.1888251\\n-1.1866378\\n-1.1865197\\n-1.1222547\\n-1.1028749\\n-1.0718814\\n-1.0643248\\n-1.0378977\\n-1.037061\\n-1.0245886\\n-1.0115027\\n-0.9676685\\n-0.9623881\\n-0.9623193\\n\\n0.80337817\\n0.38746974\\n0.50966468\\n0.62988797\\n0.46976535\\n0.35359836\\n0.43674133\\n0.32251337\\n0.52518387\\n0.53417738\\n0.48328103\\n0.27696312\\n0.33678885\\n0.27534626\\n0.43854718\\n0.55642403\\n0.62060477\\n0.36396798\\n0.46985648\\n0.50825081\\n0.59035525\\n0.34451855\\n0.60481351\\n0.37150957\\n0.34105324\\n0.35361437\\n0.36221267\\n0.31167061\\n0.37388718\\n0.29956969\\n0.53182263\\n0.45937344\\n0.29330696\\n0.29823811\\n0.33187918\\n0.26714823\\n\\n0.3117438\\n0.23828361\\n0.48141073\\n0.48381667\\n\\ncondition_Og_sig_results +\\n\\npvalue\\n4.88E-05\\n7.40E-08\\n1.61E-05\\n0.00030804\\n1.98E-05\\n3.14E-07\\n1.33E-05\\n6.02E-08\\n0.0001157\\n0.00013985\\n5.41E-05\\n1.69E-09\\n2.94E-07\\n1.93E-09\\n2.43E-05\\n0.00026168\\n0.00054658\\n3.09E-06\\n7.92E-05\\n0.00017653\\n0.00049867\\n1.81E-06\\n0.00072039\\n1.04E-05\\n3.68E-06\\n2.54E-05\\n4.08E-05\\n5.93E-06\\n5.87E-05\\n7.47E-06\\n0.00105961\\n0.0006115\\n1.19E-05\\n2.02E-05\\n7.12E-05\\n5.53E-06\\n4.48E-05\\n2.15E-06\\n0.00124554\\n0.00195951\\n\\npadj\\n0.01039067\\n0.00013716\\n0.00534315\\n0.03198439\\n0.00609956\\n0.0004071\\n0.00454914\\n0.00013022\\n0.01737143\\n0.01973086\\n0.01077569\\n8.37E-06\\n0.0004071\\n8.37E-06\\n0.00702233\\n0.02912626\\n0.0474784\\n0.00191003\\n0.0126947\\n0.02246269\\n0.04493895\\n0.00138024\\n0.05467769\\n0.00413704\\n0.00217226\\n0.00702676\\n0.00961854\\n0.00284863\\n0.01095791\\n0.00323387\\n0.07089004\\n0.05023178\\n0.0042455\\n0.00609956\\n0.01184955\\n0.00276036\\n0.01037315\\n0.00154894\\n0.07891411\\n0.07891411\\n\\nGene\\n\\nAT5G03200.1\\nAT5G47220.1\\nAT3G29000.1\\nAT4G00650.1\\nAT3G44990.1\\nAT2G05070.1\\nAT2G42530.1\\nAT5G63350.1\\nAT5G39670.1\\nAT1G61470.1\\nAT1G80840.1\\nAT5G61600.1\\nAT5G47230.1\\nAT4G25470.1\\nAT4G27280.1\\nAT5G46350.1\\nAT3G50330.1\\nAT1G72910.1\\nAT1G35210.1\\nAT3G50930.1\\nAT1G29920.1\\nAT3G50800.1\\nAT3G44260.1\\nAT2G38470.1\\nAT5G05965.1\\nAT1G18300.1\\nAT1G73540.1\\nAT1G73010.1\\nAT3G50060.1\\nAT5G43620.1\\nAT1G72240.1\\nAT4G24570.1\\nAT1G18570.1\\nAT1G72920.1\\nAT5G54270.1\\nAT5G50360.1\\nAT2G21640.1\\nAT3G18080.1\\nAT4G17490.1\\nAT1G69150.1\\n\\nbaseMean\\n12.0375435\\n153.161775\\n166.002658\\n11.2572169\\n223.865324\\n1709.86898\\n475.913349\\n54.9735521\\n114.785786\\n22.7198363\\n663.247412\\n2684.37627\\n577.821307\\n59.6532479\\n6988.80195\\n22.8465007\\n127.604924\\n104.192446\\n37.1600234\\n504.178099\\n4226.08984\\n191.046459\\n2666.75503\\n3185.46548\\n123.391355\\n651.925875\\n696.801829\\n372.969509\\n1432.98416\\n102.636467\\n211.107872\\n2042.71238\\n891.44892\\n244.955336\\n3911.89175\\n54.1660564\\n71.9274916\\n1328.31866\\n585.637163\\n42.7470799\\n\\nlog2FoldChai lfcSE\\n\\n-2.6682843\\n-1.8601178\\n-1.8062287\\n-1.690889\\n-1.638667\\n-1.5918919\\n-1.5727324\\n-1.5622046\\n-1.54785\\n-1.5462705\\n-1.5451851\\n-1.5309698\\n-1.5199074\\n-1.5139612\\n-1.5055291\\n-1.4922792\\n-1.4696279\\n-1.4463622\\n-1.438611\\n-1.4275128\\n-1.4266917\\n-1.4114142\\n-1.3608498\\n-1.3528153\\n-1.3387104\\n-1.2088777\\n-1.1888251\\n-1.1866378\\n-1.1865197\\n-1.1222547\\n-1.1028749\\n-1.0718814\\n-1.0643248\\n-1.0378977\\n-1.037061\\n-1.0245886\\n-1.0115027\\n-0.9676685\\n-0.9623881\\n-0.9623193\\n\\n0.80337817\\n0.38746974\\n0.50966468\\n0.62988797\\n0.46976535\\n0.35359836\\n0.43674133\\n0.32251337\\n0.52518387\\n0.53417738\\n0.48328103\\n0.27696312\\n0.33678885\\n0.27534626\\n0.43854718\\n0.55642403\\n0.62060477\\n0.36396798\\n0.46985648\\n0.50825081\\n0.59035525\\n0.34451855\\n0.60481351\\n0.37150957\\n0.34105324\\n0.35361437\\n0.36221267\\n0.31167061\\n0.37388718\\n0.29956969\\n0.53182263\\n0.45937344\\n0.29330696\\n0.29823811\\n0.33187918\\n0.26714823\\n\\n0.3117438\\n0.23828361\\n0.48141073\\n0.48381667\\n\\npvalue\\n4.88E-05\\n7.40E-08\\n1.61E-05\\n0.00030804\\n1.98E-05\\n3.14E-07\\n1.33E-05\\n6.02E-08\\n0.0001157\\n0.00013985\\n5.41E-05\\n1.69E-09\\n2.94E-07\\n1.93E-09\\n2.43E-05\\n0.00026168\\n0.00054658\\n3.09E-06\\n7.92E-05\\n0.00017653\\n0.00049867\\n1.81E-06\\n0.00072039\\n1.04E-05\\n3.68E-06\\n2.54E-05\\n4.08E-05\\n5.93E-06\\n5.87E-05\\n7.47E-06\\n0.00105961\\n0.0006115\\n1.19E-05\\n2.02E-05\\n7.12E-05\\n5.53E-06\\n4.48E-05\\n2.15E-06\\n0.00124554\\n0.00195251\\n\\npadj\\n0.01039067\\n0.00013716\\n0.00534315\\n0.03198439\\n0.00609956\\n0.0004071\\n0.00454914\\n0.00013022\\n0.01737143\\n0.01973086\\n0.01077569\\n8.37E-06\\n0.0004071\\n8.37E-06\\n0.00702233\\n0.02912626\\n0.0474784\\n0.00191003\\n0.0126947\\n0.02246269\\n0.04493895\\n0.00138024\\n0.05467769\\n0.00413704\\n0.00217226\\n0.00702676\\n0.00961854\\n0.00284863\\n0.01095791\\n0.00323387\\n0.07089004\\n0.05023178\\n0.0042455\\n0.00609956\\n0.01184955\\n0.00276036\\n0.01037315\\n0.00154894\\n0.07891411\\n0.07891411\\n\\nGene baseMean\\n\\nAT3G54640.1 1159.98013\\nAT4G34980.1 880.981761\\nAT4G26840.1 1013.49548\\nAT3G50830.1 2417.75572\\nAT5G65390.1 4278.34133\\nAT2G27960.1 425.326988\\nAT3G05490.1 2358.3104\\nAT5G02380.1 15558.319\\nAT5G63500.1 186.668141\\nAT1G75270.1 1806.664\\nAT4G01070.1 480.0886\\nAT3G47690.1 230.534293\\nAT5G18690.1 357.796799\\nAT5G05370.1 496.370083\\nAT2G43340.1 848.309924\\nAT1G22500.1 211.161001\\nAT2G30930.1 4281.94377\\nAT3G01420.1 21194.8844\\nAT4G25890.1 550.041801\\nAT3G13520.1 5178.11722\\nAT3G23730.1 2212.77127\\nAT1G19050.1 114.496065\\nAT5G10625.1 793.288686\\nAT4G13235.1 180.222385\\nAT2G29995.1 163.294079\\nAT1G18980.1 59.8616453\\nAT4G16563.1 2112.94425\\nAT3G28345.1 7430.90634\\nAT3G11402.1 62.5335084\\nAT4G30670.1 1111.75515\\nAT1G80280.1 735.552787\\nAT1G67860.1 22.6730679\\nAT1G67030.1 409.98084\\nAT1G70985.1 54.5969304\\nATCG01020.: 465.930412\\nATMG00160. 291.564943\\nAT4G34790.1 38.7757913\\nAT2G14900.1 424.993275\\nAT1G03850.2 42.3584295\\nAT5G22890.1 257.644961\\n\\nlog2FoldChai lfcSE\\n\\n0.23654227\\n0.27526973\\n0.29505416\\n0.32162661\\n0.33011875\\n0.34600664\\n0.36918602\\n0.37049884\\n0.39091358\\n0.39615142\\n0.40161515\\n0.40818489\\n0.43996952\\n0.44934791\\n0.46686945\\n0.48710599\\n0.51850634\\n0.52297083\\n0.53074677\\n0.53862205\\n0.64129311\\n0.70600372\\n0.73491506\\n0.73947368\\n0.74960621\\n0.75259513\\n0.75347679\\n0.76505382\\n0.79625956\\n0.80175237\\n0.81414739\\n0.84311793\\n0.85077759\\n0.85572904\\n0.86241906\\n0.86516271\\n0.88953004\\n0.89658995\\n0.91103276\\n0.93010947\\n\\n0.10462025\\n0.11979566\\n0.11345621\\n0.12821642\\n0.14879047\\n\\n0.1324878\\n0.11211656\\n0.13808171\\n0.19101818\\n0.13408827\\n0.17149384\\n0.15058271\\n0.17081604\\n0.15378851\\n0.18220233\\n0.15280111\\n0.26325332\\n0.21818476\\n0.19510108\\n0.18656005\\n0.36252488\\n0.30929852\\n0.16668177\\n0.20185949\\n\\n0.3346916\\n\\n0.3162676\\n\\n0.3449532\\n0.18540801\\n0.32785262\\n0.39287331\\n0.21744616\\n0.43224567\\n0.27666168\\n0.46142961\\n0.26538857\\n0.50304297\\n0.42953157\\n0.25011734\\n0.39480633\\n0.29741509\\n\\npvalue\\n0.0014136\\n0.00108064\\n0.0004737\\n0.00055246\\n0.0010723\\n0.00040507\\n4.88E-05\\n0.00042061\\n0.00137669\\n0.00013544\\n0.00070514\\n0.00027677\\n0.00037504\\n0.00014028\\n0.0003768\\n5.91E-05\\n0.00131014\\n0.00050205\\n0.00023105\\n0.00014138\\n0.00170878\\n0.00063821\\n4.28E-07\\n9.61E-06\\n0.00067831\\n0.00049715\\n0.00075706\\n1.49E-06\\n0.0004574\\n0.00097369\\n6.76E-06\\n0.0013269\\n6.95E-05\\n0.00144979\\n3.89E-05\\n0.00165251\\n0.00099309\\n1.21E-05\\n0.00059654\\n5.72F-05\\n\\npadj\\n0.08695319\\n0.071559\\n0.04329667\\n0.0474784\\n0.07137105\\n0.03982865\\n0.01039067\\n0.04073932\\n0.08508586\\n0.01973086\\n0.05415382\\n0.02993144\\n0.03791088\\n0.01973086\\n0.03791088\\n0.01095791\\n0.08175146\\n0.04493895\\n0.02677435\\n0.01973086\\n0.09909318\\n0.05081765\\n0.00050534\\n0.00402461\\n0.05271761\\n0.04493895\\n0.05664181\\n0.0012053\\n0.042571\\n0.06722052\\n0.00302334\\n0.08240082\\n0.0117591\\n0.08792907\\n0.00961854\\n0.0974908\\n0.06783869\\n0.0042455\\n0.04938619\\n0.01095791\\n',\n",
|
||
" '100 MB\\n\\n200 MB\\n\\n100 MB\\n\\n200 MB\\n\\n300 MB\\n',\n",
|
||
" 'Reversible modificaion of reactive cysteine residues\\nunderlies redox dependent signalling\\n\\nS-glutathionylated\\nintermediate\\n\\nSH Ss SH SOH\\n\\nROS\\nRedox-sensitive Ro > Oxidation Re:\\n\\ntarget protein Reduction ———— :\\nThiolate anion LOS Sulphenic acid form\\n\\nCy: ‘Cys Intramolecular bond\\nformation\\nDisulphide form\\n',\n",
|
||
" 'eco (® &—& > QQ VU B packagemanager.posit.co/cran\\n\\nY Speed Dial v Imported From... Y Imported From... Online Bewerbung QGIS API Docu\\n\\n2025-01-24/\\n2025-01-25/\\n2025-01-26/\\n2025-01-27/\\n2025-01-28/\\n2025-01-29/\\n2025-01-30/\\n2025-01-31/\\n2025-02-01/\\n2025-02-02/\\n2025-02-03/\\n2025-02-04/\\n2025-02-05/\\n2025-02-06/\\n2025-02-07/\\n2025-02-08/\\n2025-02-09/\\n2025-02-10/\\n2025-02-11/\\n2025-02-12/\\n2025-02-13/\\n2025-02-14/\\n2025-02-15/\\n2025-02-16/\\n2025-02-17/\\n2025-02-18/\\n2025-02-19/\\n2025-02-20/\\n2025-02-21/\\n2025-02-22/\\n2025-02-23/\\n2025-02-24/\\n2025-02-25/\\n2025-02-26/\\n2025-02-27/\\n2025-02-28/\\n2025-03-01/\\n2025-03-02/\\n2025-03-03/\\n2025-03-04/\\n2025-03-05/\\n2025-03-06/\\n2025-03-07/\\n2025-03-08/\\n2025-03-09/\\n2025-03-10/\\n2025-03-11/\\n2025-03-12/\\n2025-03-13/\\n\\n',\n",
|
||
" '',\n",
|
||
" '[Lamnala@base prokka]$ cat Ecoli_hifi/Ecoli_hifi_genome.tsv | grep hypothetical\\n\\nKBOCNLJJ_00011 CDS 1038 hypothetical protein\\nKBOCNLJJ_00033 CDS 135 hypothetical protein\\nKBOCNLJJ_00034 CDS 354 hypothetical protein\\nKBOCNLJJ_00048 CDS 411 hypothetical protein\\nKBOCNLJJ_@0060 CDS 360 hypothetical protein\\nKBOCNLJJ_00084 CDS 336 hypothetical protein\\nKBOCNLJJ_@0096 CDS 345 hypothetical protein\\nKBOCNLJJ_00101 CDS 159 hypothetical protein\\nKBOCNLJJ_00109 CDS 792 hypothetical protein\\nKBOCNLJJ_00110 CDS 687 hypothetical protein\\nKBOCNLJJ_00111 CDS 333 hypothetical protein\\nKBOCNLJJ_00113 CDS 483 hypothetical protein\\nKBOCNLJJ_00114 CDS 792 hypothetical protein\\nKBOCNLJJ_00118 CDS 201 hypothetical protein\\nKBOCNLJJ_00119 CDS 483 hypothetical protein\\nKBOCNLJJ_00121 CDS 1704 hypothetical protein\\nKBOCNLJJ_00124 CDS 315 hypothetical protein\\nKBOCNLJJ_00125 CDS 468 hypothetical protein\\nKBOCNLJJ_00127 CDS 237 hypothetical protein\\nKBOCNLJJ_00128 CDS 702 hypothetical protein\\nKBOCNLJJ_00129 CDS 822 hypothetical protein\\nKBOCNLJJ_00133 CDS 1617 hypothetical protein\\nKBOCNLJJ_00134 CDS 2190 hypothetical protein\\nKBOCNLJJ_@0135 CDS 627 hypothetical protein\\n\\nKBOCNLJJ_90136 CDS 1410 hypothetical protein\\n',\n",
|
||
" '[2]\\n\\n[3]\\n\\n[s2]\\n\\n#Commenting this out to remove the long\\n\\n#conda activate aman_prokka\\n\\n#for i in xfilt.vcf; do\\n\\n# vcftools --vcf $i --FILTER-summary\\n#done\\n\\nresult. Command run successfully\\n\\n--out pass_output_$i\\n\\nbash\\n#Commenting this out to remove the long result. Command run successfully\\n#for i in xfail.vcf; do\\n# vcftools --vcf $i --FILTER-summary --out fail_output_$i\\n#done\\n#1s -ltrh\\nbash\\nDy Du tt\\ncat pass_output*\\nbash\\n\\n',\n",
|
||
" 'Input VCF File Total Variants SNPs Count Above SNPs Other Variants\\n\\nsample1_bowtie.vcf\\nsample2_bowtie.vcf\\nsample3_bowtie.vcf\\nsample4_bowtie.vcf\\n\\nsample5_bowtie.vcf\\n\\n5675\\n\\n6789\\n\\n128\\n\\n2407\\n\\n8273\\n\\n3795\\n\\n3710\\n\\n92\\n\\n1879\\n\\n5442\\n\\n160\\n\\n307\\n\\n0\\n\\n44\\n\\n273\\n\\n1364\\n\\n2455\\n\\n26\\n\\n229\\n\\n2286\\n',\n",
|
||
" 'Functional single-cell genomics of human cytomegalovirus infection.\\n\\n2022 Nature biotechnology(3)\\nPMID: 34697476\\n\\nHein Marco Y, Weissman Jonathan S\\n\\nOpenCell: Endogenous tagging for the cartography of human cellular organization.\\n\\n2022 Science (New York, N.Y.)(6585)\\nPMID: 35271311\\n\\nCho Nathan H, Cheveralls Keith C, Brunner Andreas-David, Kim Kibeom, Michaelis André C, Raghavan\\nPreethi, Kobayashi Hirofumi, Savy Laura, Li Jason Y, Canaj Hera, Kim James Y S, Stewart Edna M, Gnann\\nChristian, McCarthy Frank, Cabrera Joana P, Brunetti Rachel M, Chhun Bryant B, Dingle Greg, Hein Marco Y,\\nHuang Bo, Mehta Shalin B, Weissman Jonathan S, Gdmez-Sjéberg Rafael, Itzhak Daniel N, Royer Loic A, Mann\\nMatthias, Leonetti Manuel D\\n\\nMapping transcriptomic vector fields of single cells.\\n\\n2022 Cell(4)\\n\\nPMID: 35108499\\n\\nQiu Xiaojie, Zhang Yan, Martin-Rufino Jorge D, Weng Chen, Hosseinzadeh Shayan, Yang Dian, Pogson Angela\\nN, Hein Marco Y, Hoi Joseph Min Kyung, Wang Li, Grody Emanuelle |, Shurtleff Matthew J, Yuan Ruoshi, Xu\\nSong, Ma Yian, Replogle Joseph M, Lander Eric S, Darmanis Spyros, Bahar lvet, Sankaran Vijay G, Xing\\nJianhua, Weissman Jonathan S\\n',\n",
|
||
" \"What this suggests:\\n\\n>\\n\\nHigh Mapping Percentage: The mapping quality is quite high (99.20%), which is good.\\n\\nHowever, the properly paired issue (0%) should be looked into further.\\n\\nPaired Read Alignment Issues: The @% for properly paired reads suggests that the\\nalignment tool or the pairing information may not be correct. This is a crucial issue for Hi-C\\ndata since proper pairing indicates the correct relationship between paired-end reads. It's\\nworth verifying that the correct options are being used in the alignment step and whether the\\n\\npairing information is retained properly.\\n\\nInter-chromosomal Interactions: Given the significant number of reads with mates mapped\\nto different chromosomes, this aligns with your Hi-C analysis, which typically shows inter-\\nchromosomal interactions. However, excessive inter-chromosomal mapping could indicate a\\n\\nproblem if the number is unusually high.\\n\",\n",
|
||
" '',\n",
|
||
" 'GSM3398051: HiC maize Leaf-HiC rep2; Zea mays; Hi-C\\n\\n1 ILLUMINA (NextSeq 500) run: 528.9M spots, 80.4G bases, 30.8Gb downloads\\nAccession: SRX4727418\\n\\nGSM3398050: HiC maize Leaf-HiC rep1; Zea mays; Hi-C\\n\\n1 ILLUMINA (NextSeq 500) run: 89.8M spots, 13.7G bases, 4.5Gb downloads\\nAccession: SRX4727417\\n',\n",
|
||
" 'SINGLE CELL SEQUENCING\\n\\nSingle Cell Genome Sequencing Workflow\\n\\nLaser\\nCapture DNA Extraction\\nMicrodissection\\nEnvironment OR *DBay\\nSample\\n=» racs > © => veg ‘Sy\\nSingle “ee\\nOR Cell\\nOrgan Isolation\\nTissue Microfluidics\\nSNP/CNV/\\nCell Types 4m\\nIdentification TAAGCCATACC\\nAnalysis Sequencing Sequencing MDA\\n\\nLibrary\\n\\nTALL TALLINN UNIVERSITY OF TECHNOLOGY\\n\\nSingle Cell RNA Sequencing Workflow\\nRT& Second-strand\\n\\ni oii\\n\\nSolid Tissue Dissociation Single Cell Isolation RNA cDNA\\nwT v4 OR\\n\\n€ % Amplified mS s cad\\nSA\\n\\nRNA\\nCell Types 4\\nIdentification\\n\\nRT PCR\\nt Clustering My\\nfoc\\na ary\\n-_ ~— a -— piss\\nSingle-cell Sequencing Sequencing Library Amplified cDNA\\nExpression Profiles\\n\\n—_\\n',\n",
|
||
" \"Lipid transport from plants to arbuscular mycorrhiza fungi?\\n\\n@ carrot\\nfu @wWT\\nngus is-\\n9 13C,-Glucose Sn ; '\\nram2-\\nD. carota —no plent\\n\\nao wn\\nOo\\n\\n16:0 0.e [%]\\not NY ko\\noo oO 00 0\\n\\ncontrol roots AMroots extraradical hyphae\\n\\nL. japonicus a ;\\nKeymer, Pimprikar et al (2017), eLife\\n\\nCollaboration with\\nWolfgang Eisenreich Lab\\nTU Munich\\n\",\n",
|
||
" '',\n",
|
||
" '@FastQC Report\\n\\nSummary\\n\\nQbasic Statistics\\nOber base sequence quality\\n\\nOber sequence quality scores\\n\\nOber base sequence content\\nOrer sequence GC content\\nOber base N content\\n\\nQ)sequence Length Distribution\\n\\nQsequence Duplication Levels\\n\\nQ overrepresented sequences\\n\\nQadapter Content\\n1) Kmer Content\\n\\nQBasic Statistics\\n\\nMeasure Value\\n\\nFilename\\n\\nFile type\\n\\nEncoding\\n\\nTotal Sequences\\n\\nSequences flagged as poor quality\\nSequence length\\n\\n%6GC\\n\\nwood_sample_1_forward_paired. fq.gz\\nConventional base calls\\n\\nSanger / Illumina 1.9\\n\\n180416\\n\\n1)\\n\\n30-150\\n\\n36\\n\\nOper base sequence quality\\n\\nQuality scores across all bases (Sanger / Illumina 1.9 encoding)\\n\\n40\\n38\\n36\\n34\\n32\\n30\\n28\\n26\\n24\\n22\\n20\\n18\\n16\\n14\\n12\\n\\n10\\n',\n",
|
||
" 'In [33]:\\n\\ncoords, model = allel.pca(\\n\\ngn,\\nn_components=10,\\nscaler=\\'patterson\\',\\nploid\\n\\n)\\n\\n/data/proj2/home/students/pst14/.conda/envs/test_allel_env/lib/python3.9/site-packages/allel/stats/preprocessing.p\\ny:134: RuntimeWarning: invalid value encountered in divide\\n\\ngn /= self.std_\\nValueError\\n\\nInput In [33], in <cell line: 1>()\\n—s 1 coords, model = aUUeUIpEal\\n\\nTraceback (most recent call last)\\n\\nFile ~/.conda/envs/test_allel_env/Lib/python3.9/site-packages/allel/stats/decomposition.py:55, in pca(gn, n_compone\\nnts, copy, scaler, ploidy)\\n\\n52 model = GenotypePCA(n_components, copy=copy, scaler=scaler, ploidy=ploidy)\\n\\n54 # fit the model and project the input data onto the new dimensions\\n——> 55 coords =\\n\\n57 return coords, model\\n\\nFile ~/.conda/envs/test_allel_env/lib/python3.9/site-packages/allel/stats/decomposition.py:74, in GenotypePCA. fit_t\\nransform(self, gn)\\n73 def fit_transform(self, gn):\\n—> m4 uy sv\\n73 ou self.n_components]\\n76 uu 4 s{tself.n_components}\\n\\nFile ~/.conda/envs/test_allel_env/Lib/python3.9/site-packages/allel/stats/decomposition.py:91, in GenotypePCA._fit\\n(self, gn)\\n\\n88 n_samples, n_features = x.shape\\n\\n90 # singular value decomposition\\n—> 91u, s, v=\\n\\n93 # calculate explained variance\\n\\n94 explained_variance_ = (s %* 2) / n_samples\\n\\nFile ~/.conda/envs/test_allel_env/lib/python3.9/site-packages/scipy/linalg/_decomp_svd.py:108, in svd(a, full_matri\\nces, compute_uv, overwrite_a, check_finite, lapack_driver)\\n13 def svd(a, full_matrices=True, compute_uv=True, overwrite_\\n14 check_finite=True, lapack_driver=\\'gesdd\\'):\\n16 Singular Value Decomposition.\\n17\\n)\\n\\n=False,\\n\\n106\\n107 oy\\n~-> 108 al = _asarray_validated(a, check_finite=check_finite)\\n109 if len(a1.shape)\\n110 raise ValueError(\\'expected matrix\\')\\n\\nFile ~/.conda/envs/test_allel_env/Lib/python3.9/site-packages/scipy/_lib/_util.py:321, in _asarray_validated(a, che\\nck_finite, sparse_ok, objects_ok, mask_ok, as_inexact)\\n\\n319 raise ValueError(\\'masked arrays are not supported\")\\n320 toarray = np.asarray_chkfinite if check_finite else np.asarray\\n—> 31la=\\n\\n322 if not objects_ok:\\n323. if a.dtype is np.dtype(\\'0\\'):\\n\\nFile ~/.conda/envs/test_allel_env/Lib/python3.9/site-packages/numpy/1ib/_function_base_impl.py:649, in asarray_chkf\\ninite(a, dtype, order)\\n\\n647 a = asarray(a, dtype=dtype, order=order)\\n\\n648 if a.dtype.char in typecodes[\\'AllFloat\\'] and not np.isfinite(a).all():\\n—> 649 raise ValueError(\\n\\n650 “array must not contain infs or NaNs\")\\n\\n651 return a\\n\\nValueError: array must not contain infs or NaNs\\n\\n',\n",
|
||
" '40\\n\\n30\\n\\nValue\\n\\n10\\n\\nMetrics for 2V_COMPLETE\\n\\n16\\n\\n0-0.25\\n\\n65\\n\\n19\\n\\n0.25-0.50 0.50-0.75\\n\\nInterval\\n\\n92\\n\\n0.75-1.0\\n\\n(18\\n\\nMetric\\n\\n(0) area\\n\\n{7 Maxmin\\n',\n",
|
||
" 'Doctoral School\\n\\nStudent Life\\n\\no Welcome Doctorate Supervision/Practices Alumni/Career Ye\\n\\nYou are here: UNIL > Doctoral School of the Faculty of Biology and Medicine > Doctorate > PhD in Life Sciences > Fellowships > Procedure\\n\\nPhD in Life Sciences O\\nProcedure\\n\\nRegistration\\nThesis steps\\n\\nStudy Programs (ECTS) O\\n\\n. THANKS FOR YOUR INTEREST IN THE PHD FELLOWSHIPS!\\nFellowships O\\n\\nProcedure\\n\\nHost labs\\n\\nOn-line application\\n\\nAbout us/Former \"Fellows\"\\n\\nINFORMATION AND APPLICATION FOR THE 2026 PHD FELLOWSHIPS WILL BE AVAILABLE SOON\\nSTAY TUNED AND DON\\'T MISS THE UPDATES ON THIS PAGE!\\n\\nRules & Forms YOUR APPLICATION\\n\\nSTEP 1: Find a host lab\\n\\nContact\\nLooking for a thesis?\\n\\n¢ You have first to choose one laboratory, please browse \"Host labs\".\\n\\n¢ Contact thesis directors of interest from the Lausanne University and if they appreciate your\\nCV, ask them to send you a confirmation by email that she/he agrees to act as your potential\\nthesis director. She/he should confirm that you were in contact with her/him before applying\\nand should explain the reason why she/he selected you as a future doctoral student.\\n\\n¢ You should mention her/his name in your application form (see STEP 2).\\n\\nPhD in Neuroscience\\n\\nPhD in Nursing Sciences O\\n\\nPhD in the Humanities and Social\\nSciences of Medicine and Health O\\n\\nSTEP 2: Fill in the PDF application form\\n\\n¢ Download the \"Application form\" on your desktop and fill in it: It must be fully completed!\\nPlease note that it is possible to fill out the form directly on the PDF. /f you cannot write anything,\\n\\nplease look at the top of the internet page and click on \"open with another PDF viewer\". Alternatively, you could also\\n\\nprint the form, fill it out by hand (make sure it is readable) and scan it.\\nSTEP 3: Prepare your file\\nPrepare the following documents:\\nA. A complete Curriculum Vitae\\nB. Copy of your passport or identity card\\nC. Copies of your high school diploma\\n\\nD. A certified copy of the university diploma(s) and the diploma supplement (if available), your scores\\n(transcripts) obtained through exams passed at the university (originals + translated copies) (non-\\n\\ncertified copies are sufficient for candidates holding a Bachelor and a Master degree awarded by a Swiss university)\\n\\nC or D: Copies of certified transcripts, listing courses and grades: These transcripts should be translated (if not originally in\\n\\nEnglish) in French, German, Italian or English.\\n\\nE. We require three referees from preferably different departments and/or institutes/research\\ngroups. Each of them should provide a letter of recommendation. On the \"On-line Form\" you have\\nto fill in the three names of the referees you have chosen. As soon as you write their e-mail\\naddress, an e-mail will automatically be sent to them. They will then fill it in and send it back directly\\nto us (deadline for the referees : October 13, 2025). The recommendation letter should not come from the\\nhosting lab.\\n\\nF. Copy of the confirmation e-mail sent by the potential thesis director (see STEP 1).\\nSTEP 4: You are now ready to fill in the \"On-line Form\"!\\n\\n¢ Merge in a PDF file (no ZIP file) the following documents in this order: Application form, A, B, C, D and F. Name\\nof the file Name_First Name_Birth Date.pdf (e.g. Bond_James_1953.pdf) Max: 20 Mo (you can zip the document).\\n\\n© Advice: To merge PDF documents together, you can use this on-line platform @, the Adobe Acrobat XI Pro software\\nor any other software or platform you may find useful. Also, to transform jpeg documents in PDF, you can use this on-\\nline platform @, a PDF printer or any other method you may find useful. Please note that we will NOT take\\n\\nresponsibility for the use/misuse of any of these softwares and platforms.\\n\\n© Fill-in the \"On-line Form\" and submit (on-line) the requested documents (in the format described in STEP 2 and\\n3)\\n\\nBE CAREFUL\\n\\nYour application must be fully completed to be taken into account and evaluated.\\n\\nIf your application meets the requirements, you will be informed by December 2025 and you will be\\ninvited for an interview that will take place at the end of January 2026 in Lausanne. More information\\nwill be sent to you at that time.\\n\\nAPPLICATIONS SENT BY E-MAIL, BY POST, INCOMPLETE OR AFTER THE\\nDEADLINE WILL NOT BE CONSIDERED!\\n\\nDeadline for the application : October 6, 2025, midnight/Swiss time.\\nPlease pay ATTENTION to the TIME ZONE!\\n\\nFor information regarding admission at UNIL: please browse Admissions Service. Concerning the\\nrequired documents for admission and registration at UNIL, please note that the requirements of\\nAdmissions office outclass the requirements of this contest.\\n\\nAny queries ?\\n\\nContact : phdfellowships@unil.ch\\n\\nIMPORTANT ALERT\\n\\nThere is a potentially fraudulent website that copies the UNIL PHD Fellowships website. We\\nstrongly advise you to only use the official UNIL PhD Fellowships website for your application.\\nYour personal data and money may be at risk if you use unofficial websites and our University\\ndoes not accept any responsibility and consequences for applications made via unofficial\\nwebsites and agencies.\\n\\nFollow us: © in} 1}\\n\\nAny queries?\\n\\nDoctoral school\\n\\nPhD Fellowships\\n\\nUniversity of Lausanne\\n\\nFaculty of Biology and Medicine\\nAmphipdle - Office n°304\\n\\n1015 Lausanne/Switzerland\\n\\n+41 21 692 40 06\\nphdfellowships@unil.ch\\n\\nWant to start a PhD?\\n\\nPHD FELLOWSHIPS & POSITIONS\\n\\nin Life Sciences\\nStarting April 2\\n\\nShare:\\n\\nAmphipdle - Bureau 324 - CH-1015 Lausanne Contact Sitemap\\nSwitzerland Directories Archives\\nTel. +41 21 692 40 09 Legal information Edition\\n\\nunisante\\n\\nCentre universitaire de médecine générale\\net santé publique «Lausanne\\n\\nCc .uUlY\\n\\n_e)\\n85D\\n£0\\n3>\\n(J\\nW\\n\\n',\n",
|
||
" 'In [64]: contig_list <- lapply(contig_list, function(df) {\\ndf$cdr3_nt <- as.character(df$cdr3_nt)\\ndt\\n})\\n\\nIn [65]: combined.TCR <— combineTCR(contig_list,\\nremoveNA = FALSE,\\nremoveMulti = FALSE,\\nfilterMulti = FALSE)\\n\\n# output = a list of contig data frames that will be reduced to the reads associated with a single cell barcode. It |\\nhead(combined.TCR[[1]])\\n\\nError: Expecting a string vector: [type=integer; required=STRSXP].\\nTraceback:\\n\\n1. .constructConDfAndParseTCR(data2)\\n2. rcppConstructConDfAndParseTCR(data2 %>% dplyr::arrange(., chain,\\ncdr3_nt), unique(data2[[1]]))\\n3. stop(structure(list(message = \"Expecting a string vector: [type=integer; required=STRSXP].\",\\n. call = eval(expr, envir), cppstack = NULL), class = c(\"Rcpp::not_compatible\",\\n. \"C++Error\", “error”, \"condition\") ))\\n',\n",
|
||
" '0 10000 20000 30000 40000 50000\\nrank(mean)\\n\\n2.0-\\n\\n0.5-\\n\\n0.0-\\n\\n10000\\n\\n0 10000 20000\\nrank(mean)\\n\\n30000\\n\\n50000\\n\\n20000 30000\\nrank(mean)\\n\\ncount\\n\\n403\\n\\n55\\n\\n40000\\n\\n50000\\n\\ncount\\n\\n403\\n\\n55\\n',\n",
|
||
" 'Explanations of the 20 fields are as follows:\\n\\nchromosome = the chromosome that the loop is located on\\n\\nx1,x2 = the coordinates of the upstream locus corresponding to the peak pixel\\ny1,y2 = the coordinates of the downstream locus corresponding to the peak pixel\\ncolor = the color that the feature will be rendered as if loaded in Juicebox\\nobserved = the raw observed counts at the peak pixel\\n\\nexpected_[bottom_left, donut, horizontal, vertical] = the expected counts calculated using the [bottom_left, donut,\\nhorizontal, vertical] filter\\n\\nfdr_[bottom_left, donut, horizontal, vertical] = the q-value of the loop calculated using the [bottom_left, donut,\\nhorizontal, vertical] filter\\n\\nnumber_collapsed = the number of pixels that were clustered together as part of the loop call\\n\\ncentroid’ = the upstream coordinate of the centroid of the cluster of pixels corresponding to the loop\\n\\ncentroid2 = the downstream coordinate of the centroid of the cluster of pixels corresponding to the loop\\n\\nradius = the Euclidean distance from the centroid of the cluster of pixels to the farthest pixel in the cluster of pixels\\n',\n",
|
||
" 'Papantonis, Argyris <argyris.papantonis@med.uni-goettingen.de>\\nMon 3/3, 8:11 AM\\n\\nHi Aman,\\nYou need to tell me what the expected duration by your program is and what would be your preferred starting date — we are flexible.\\nA.\\n\\nArgyris Papantonis, PhD\\n\\nProfessor for Translational Epigenetics & Genome Architecture,\\nInstitute of Pathology, University Medical Center Géttingen,\\nRobert-Koch-Str. 40, 37075 Géttingen, Germany\\n\\nTel.: +49 551 39 65734\\n\\nWeb: https://papantonislab.eu\\n',\n",
|
||
" '# VCF analysis\\nfor i in xbowtie.vcf; do\\n\\ngrep -c \\'*##\\' \"$i\" # Count the lines starting with \\'##\\'\\n\\ngrep --color \\'*#CHROM\\' \"$i\" # Show the line starting with \\'#CHROM\\'\\ngrep -v \"*#\" -c \"$i\" ## Count the lines not starting with \\'#\\'\\n\\ngrep -v \"#\" \"$i\" | grep -c \"TYPE=snp\"\\n\\necho \"SNPS above\"\\n\\ngrep -v \"#\" \"$i\" | grep -c \"TYPE=mnp\"\\n\\ngrep -v \"#\" \"$i\" | grep -c \"TYPE=ins\"\\n\\ngrep -v \"#\" \"$i\" | grep -c \"TYPE=del\"\\n\\ngrep -v \"#\" \"$i\" | grep -c \"TYPE=complex\"\\n\\necho \"Analysis complete for $i\"\\n\\ndone\\n',\n",
|
||
" 'In [421]:\\n\\nIn [423]:\\n\\ncombined. TCR_p3 <- lapply(combined.TCR_p3, function(df) {\\ndf$chain <- \"TRB\"\\nreturn(df)\\n\\n})\\n\\ncombined.TCR_p4 <- lapply(combined.TCR_p4, function(df) {\\ndf$chain <- \"TRB\"\\nreturn(df)\\n\\n})\\n\\ncombined_TCR <— combineTCR(\\nlist(patient3 = combined.TCR_p3, patient4 = combined.TCR_p4),\\nsamples = c(\"patient3\", \"patient4\"),\\nremoveNA = FALSE,\\nremoveMulti = FALSE,\\nfilterMulti = FALSE\\n)\\n\\nError in ‘mutate()*:\\n\\ni In argument: “TCR1 = ifelse(...)*.\\nCaused by error:\\n\\n! object \\'chain\\' not found\\nTraceback:\\n',\n",
|
||
" 'Crosslinked —> Religated —>\\nchromatin fragments\\n» Ligation\\n\\n> Restriction site tt Pair of mate reads\\n\\n_ fitering & & Binning\\n\\n_ paired read ties\\n\\n— Alignment to\\nreference genome\\n\\nSequencing\\n\\n>\\n>\\n\\n>\\nFASTQ\\n\\n>\\n>\\n>\\n\\n>\\nFASTQ\\n\\nL eb ciocne J\\n\\nL cleo 4\\n\\nFig. 1 Hi-C data, from generation to contact matrix. The figure shows a\\nschematic representation of Hi-C data analysis, starting from a cartoon\\ndepicting cross-linked chromatin and a prototypic pair of mate reads\\npositioned on the restriction fragments from which they originate. Raw\\nsequencing paired-end reads (in FASTQ files) are aligned to the reference\\ngenome considering the mate reads independently. Then, aligned reads\\n\\n(in BAM files) are assigned to their fragment of origin and paired. The\\npaired reads are stored in a sorted file that can be in either plain text,\\nindexed text (pairix), or binary (e.g., HDF) formats, depending on the\\npipeline. Finally, after filtering and binning, the read counts are stored in\\ncontact matrix files, including plain text (e.g., 2D or sparse matrix) or\\nbinary (e.g., hic or cool) file formats\\n',\n",
|
||
" 'rw) Platform Solutions Resources Open Source Enterprise Pricing\\n\\n& ginkgobioworks / geckopy Public\\n\\n<> Code © Issues 5 3 Pullrequests 1 © Actions [FH Projects © Security | Insights\\n\\n{]) Files geckopy / tests / data / ecoli_proteomics_schmidt2016S5.tsv (0\\n\\nfe)\\n\\nP master ~ We can make this file beautiful and searchable if this error is corrected: No tabs found in this TSV file in line 0.\\n\\nQ ecoli x}\\n\\nyd carrascomj test: check experimental io works\\n\\n[} contributing.rst\\n[5 design.rst\\nPreview Code Blame 2059 lines (259 loc) - 63.9 KB\\n[5 index.rst\\nB installation.rst 1 ,uniprot, copies_per_cell,CV, stdev\\n2 @, POABT7, 2180, 10.69, 233.042\\n[) make.bat 3 1, POABV2, 2661, 10.33, 274.8813\\n. . 4 2, P36683,, 22844, 8.11, 1852 6483999999998\\n[5 project_overview.rst\\n5 3,P15254,1438,1.02,14.6676\\n1) relaxation.rst 6 4, P09831, 1363, 21.93, 298.9059\\n, 7 5, POAFGB, 2445, 9.05, 221.27250000000004\\n(5) requirements.txt 8 6, P@A9Q7, 4510, 11.05, 498.355\\n> De geckopy 9 7, POCEA7 , 149278, 2.92, 4358.9176\\n10 8, P25665,17413,8.05,1401.7465\\nv @ tests 44. 9, POAGFS, 19540, 1.93, 377.12199999999996\\n12 10, P@0968,1514,10.74,162.6036\\nv & data\\n13 11,P09373,5645,14.64,826.428\\n[} all_prot_encodings.xml 14 12, POAGYB, 14174, 2.21, 313 .24539999999996\\n15 13,P@5793,15604,6.0,936.24\\n[) chem_xref_seedset.tsv 16 14, P@A705, 1912,12.07, 230.7784\\nC) compartment_data.json 17 15, P@0864,1349, 20.43, 275.6007\\n18 16, POAGMB, 21360, 4.89, 1044.504\\n() ec_coli_core.xml 19 17, P@0957, 1133, 8.32, 94.26559999999999\\n. 20 18, P@0961, 1959, 8.44, 165.3396\\n[ eciML1515.xml.gz\\n21 19,P76558, 1504,9.51, 143. 0304000000001\\n[ eciML1515_seed.xml 22 20, POAG67, 10729, 1.98, 212.43419999999998\\n, : , 23 21,P07813,1202,3.59, 43.1518\\n| [) ecoli_proteomics_schmidt201... 24 22, P00561, 2108, 17.17, 361.94360000000006\\nD) lexicon.csv 25 23, POAFG3, 4195, 16.78, 703.921\\n26 24,PQ0956,1348,1.86, 25.0728\\n[5 lexicon_1515.csv 27 25,P25553,17217,1.8, 309. 90600000000006\\n. 28 26, PQ6959, 3567, 4.27, 152.31089999999998\\n(3 mmol_gdW_protemics.csv\\n29 27, P23847,8170,6.44,526.148\\n[) thermo_data.thermodb 30 28,P@7395, 2818, 7.27, 204. 8686000000001\\n; - 31 29,P21889, 1505,7.11, 107.0055\\n> test_integration 32 30, P7118, 828, 7.4, 61.272000000000006\\n\\n> O& test_unit 33 31,P10408,451,11.51,51.9101\\n',\n",
|
||
" '‘Samples — metre. 16.sut. genes — ety meget WTA gras — metyone mat genes\\n\\ngow\\ni\\n\\nFan\\nI.\\n\\n‘Samples — metyione 14. sued genes — metylame merged WT_Al genes ——metyame mart genes\\n\\n02\\n\\ni\\n\\n‘samples — meanest goes — matrfame mart WT_AL pines — metho. ntl genes\\n\\nTE -\\n',\n",
|
||
" 'jf====\\n\\n#\\n#\\n#\\n#\\n#\\n#\\n#\\n#\\n#\\n#\\ni\\n#\\n#\\n\\na\\n\\nAligned_sequences: 2\\n\\n1: Zm00001eb360660_RP\\n\\n2: Zm00001eb360660_RPHt4\\nMatrix: EBLOSUM62\\nGap_penalty: 10.0\\nExtend_penalty: 0.5\\n\\nLength: 100\\n\\nIdentity: 98/100 (98.0%)\\nSimilarity: 99/100 (99.0%)\\nGaps: 0/100 ( 0.0%)\\n\\nScore: 511.0\\n',\n",
|
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" 'Mean Methylation Levels - CG Context\\n\\nos:\\n\\nMean Methylation Level\\na\\n\\n0.00:\\n\\nMean Methylation Levels - CHG Context\\n0.00.\\n\\n&\\n\\nMean Methylation Level\\n\\n0.00:\\n\\nFile\\n\\n0.020\\n\\nMean Methylation Level\\n\\n0.05\\n\\n0.000\\n\\nMean Methylation Levels - CHH Context\\n\\n',\n",
|
||
" 'v Please select\\nChalmers tekniska hoegskola AB\\nGoteborgs Universitet\\nHandelshégskolan i Stockholm (HHS)\\nHégskolan i Halmstad\\nKarlstads universitet\\nKarolinska Institutet\\nKungliga Tekniska H6gskolan (KTH)\\nLinképings universitet (LiU)\\nLinnéuniversitetet\\nLulea tekniska universitet\\nLunds universitet\\nStockholms universitet\\nSveriges lantbruksuniversitet (SLU)\\nUmea universitet\\nUppsala universitet\\n',\n",
|
||
" '#bcftools\\n\\nfor i in xbowtie.vcf; do\\nbcftools stats \"$i\" | grep \"*SN\"\\necho \"Analysis complete for $i\"\\necho \"\"\\n\\ndone\\n',\n",
|
||
" '[pst14@frontend ref_gen]$ head -n 3@ alignment_stats_4.txt | grep *SN | cut -f 2-\\nraw total sequences:\\nfiltered sequences:\\nsequences: 361214\\nis sorted: 1\\n\\n1st fragments: 180607\\nlast fragments: 180607\\n\\nreads\\nreads\\nreads\\nreads\\nreads\\nreads\\nreads\\nreads\\n\\nMapped: 361188\\nmapped and paired:\\nunmapped: 26\\nproperly paired:\\npaired: 361214\\nduplicated:\\n\\nMQe: 139433\\nQC failed:\\n\\nnon-primary alignments:\\n\\ntotal\\ntotal\\ntotal\\nbases\\nbases\\nbases\\nbases\\n\\nlength: 46856942\\nfirst fragment len\\nlast fragment leng\\nMapped: 46855813\\nmapped (cigar):\\ntrimmed: 0\\nduplicated:\\n\\nmismatches: 765027\\nCpst14efrontend ref_gen]$\\n\\n361214\\n7)\\n\\n361164 # paired-end technology bit set + both mates mapped\\n\\n358896 # proper-pair bit set\\n# paired-end technology bit set\\n\\n(7) # PCR or optical duplicate bit set\\n# mapped and MQ=@\\n\\nQ\\n\\n7)\\n\\n# ignores clipping\\ngth: 23431233 # ignores clipping\\n\\nth: 23425709 # ignores clipping\\n# ignores clipping\\n46689594 # more accurate\\n\\nQ\\n# from NM fields\\n\\n',\n",
|
||
" '5.1 Raw Images of Ovules\\n\\nLearning to obtain 3D confocal images of ovules using the already established\\npipeline(4) was one of the tasks of this internship, Raw confocal images were\\nsuccessfully obtained for three ovules, showcasing the intricate details of the ovule\\nstructure. These images were collected using [mention the specific confocal microscopy\\n\\nThe\\nimages were further post-processed for creating 3D meshes, and the cellular properties\\nfrom them were extracted using the MGX platform.\\n\\n',\n",
|
||
" '.\\n\\n@ Vivaldi File Edit View Bookmarks Mail Tools Window Help 1) BE Beer rzr oe Ww s Q S$ Wed Jan 22 14:27\\n\\nS Workspaces v < Untitled Jupyter Notebook - Term Download File - Vertopal Variant calling - Wiki: pop © ChatGPT GIé Appendices | CITES + ry UW\\n\\na) — > QQ YW 6 cites.org = fl +» & Search Google ’ | Restart Required [) ® Co ee *# G C &\\n\\nY Speed Dial yY Imported From... Y Imported From... Online Bewerbung QGIS API Docume... qgis - Trying to pe... New Script- Earth... Pastebin.com-#1.. TargetP 2.0- DTU... https://www.mood... OnePlus 12R revie... Whois “Indian”in.. v A »\\n\\nFind in Page: q > xX Match Case al\\nvars. ampanihyensis, robinsoniiand\\nspirosticha} @\\nfh,\\nEuphorbia francoisii “\\n&)\\n\\nEuphorbia moratii {Includes the\\nvars. antsingiensis, bemarahensis &\\nand multiflora}\\n\\nEuphorbia parvicyathophora &\\nEuphorbia quariziticola oy\\nEuphorbia tulearensis as)\\nFAGACEAE\\nBeeches e\\n(QUereUS mongolica*> (Russian\\nFederation)\\nFOUQUIERIACEAE\\nOcotillos e\\nFouquieria columnaris**\\nFouquieria fasciculata\\nFouquieria purpusii\\nGERANIACEAE\\nGeraniums _|\\nMonsonia herrei (South Africa)\\nMonsonia multifida (South Africa)\\n&\\n\\na wg ”\\n',\n",
|
||
" '> res[res$padj < 0.05 && ! is.na]\\n\\n45] @ 0.0s\\nError in h(simpleError(msg, call)): error in evaluating the argument in selecting a method for function \\' \"length = 19068\\' in coercion to \\'logical(1)\\'\\nTraceback:\\n1. .handleSimpleError(function (cond)\\n\\n.Internal(C_tryCatchHelper(addr, 1L, cond)), \"\\'length = 19068\\' in coercion to \\'logical(1)\\'\",\\n. base: :quote(res$padj < 0.05 && !is.na))\\n\\n2. h(simpleError(msg, call))\\n\\n3. «handleSimpleError(function (cnd)\\n\\naf\\n\\n. watcher$capture_plot_and_output()\\n\\n. cnd <- sanitize_call(cnd)\\n\\n. watcher$push(cnd)\\n\\n. switch(on_error, continue = invokeRestart(\"eval_continue\"),\\n\\n. stop = invokeRestart(\"eval_stop\"), error = NULL)\\n\\n. }, \"error in evaluating the argument \\'i\\' in selecting a method for function \\'[\\': ‘length = 19068\\' in coercion to \\'logical(1)\\'\",\\n. base: :quote(h(simpleError(msg, call))))\\n',\n",
|
||
" 'In\\n\\n(111]:\\n\\nmerged_obj <- merge(x = patient3_transform, y = patient4_transform)\\nmerged_obj <- NormalizeData(merged_obj)\\n\\nmerged_obj <- FindVariableFeatures(merged_obj)\\n\\nmerged_obj <- ScaleData(merged_obj)\\n\\nmerged_obj <- RunPCA(merged_obj)\\n\\nError: SCT assay is comprised of multiple SCT models. To change the variable features, please set manually with Var\\niableFeatures<—\\nTraceback:\\n\\n1. FindVariableFeatures.Seurat (merged_obj)\\n\\n2. FindVariableFeatures(object = object[[assay]], selection.method\\nloess.span = loess.span, clip.max = clip.max, mean. function\\ndispersion. function = dispersion. function, num.bin = num.bin,\\nbinning.method = binning.method, nfeatures = nfeatures, mean.cutoff = mean.cutoff,\\ndispersion.cutoff = dispersion.cutoff, verbose = verbose,\\n\\nselection.method,\\nmean. function,\\n\\n3. FindVariableFeatures.SCTAssay(object = object[[assay]], selection.method = selection.method,\\nloess.span = loess.span, clip.max = clip.max, mean.function = mean.function,\\ndispersion. function = dispersion. function, num.bin = num.bin,\\nbinning.method = binning.method, nfeatures = nfeatures, mean.cutoff = mean.cutoff,\\ndispersion.cutoff = dispersion.cutoff, verbose = verbose,\\n4. stop(\"SCT assay is comprised of multiple SCT models. To change the variable features, please set manually with V\\nariableFeatures<-\",\\ncall. = FALSE)\\n5. «handleSimpleError(function (cnd)\\n-f\\nwatcher$capture_plot_and_output()\\ncnd <- sanitize_call(cnd)\\nwatcher$push(cnd)\\nswitch(on_error, continue = invokeRestart(\"eval_continue\"),\\n. stop = invokeRestart(\"eval_stop\"), error = NULL)\\n: }, \"SCT assay is comprised of multiple SCT models. To change the variable features, please set manually with Var\\niableFeatures<-\",\\nbase: :quote(NULL) )\\n',\n",
|
||
" 'e) Assign Genotype and Position\\n\\npython O Copy @ Edit\\nm[\"genotype2\"] = np.tile(np.arange(n_genotypes), n_genotypes)\\n\\npositions = [s.position for s in ts.sites()]\\nm[“position\"] = np.tile(positions, n_genotypes)\\n\\n* genotype2 = Second SNP position (for pairwise comparison).\\n\\n¢ positions = Actual physical positions of SNPs on the chromosome.\\n\\nf) Calculate Physical Distance Between SNPs\\n\\npython © Copy © Edit\\ndistance = pairwise_distances(np.array(positions) [None] .T)\\n\\npairdistances = pd.DataFrame(distance) .melt().value.tolist()\\n\\nm[“pairdistances\"] = pairdistances\\n\\nprint(\"Calculated pairwise distance metric...\")\\n',\n",
|
||
" 'In [6]: .libPaths(c(\"/home/aman/R/x86_64-pc-linux-gnu-library/4.4\", .libPaths()))\\nlibrary (Seurat)\\n\\nError in library(Seurat): there is no package called ‘Seurat’\\nTraceback:\\n\\n1. stop(packageNotFoundError(package, lib.loc, sys.call()))\\n',\n",
|
||
" 'In [23]:\\n\\nIn [24]:\\n\\nLibrary(scRepertoire)\\n$1 <- read.delim(\"/home/rstudio/rund71/run071-nsclc—4_VDJ_Dominant_Contigs_AIRR.tsv\", sep = \"\\\\t\", header = TRUE, str\\n\\ncontig_list <- list(S1)\\ncontig. list <- loadContigs(input\\nformat\\n\\nsi,\\n“AIRR\")\\n\\ncombined.TCR <- combineTCR(contig_list,\\nremoveNA = FALSE,\\nremoveMulti = FALSE,\\nfilterMulti = FALSE)\\n\\n# output = a List of contig data frames that will be reduced to the reads associated with a single cell barcode. It\\nhead(combined.TCR[[1]])\\n\\nError in ‘mutate()*:\\n\\n1 In argument: “TCR1 = ifelse(..\\nCaused by erroi\\n! object ‘chain’ not found\\nTraceback:\\n\\n1. .makeGenes(cellType = \"T\", out[[il])\\n\\n2. data2 %>% mutate(TCR1 = ifelse(chain ‘in’ c(\"TRA\", \"TRG\"), str_c(str_replace_na(v_gene),\\nstr_replace_na(j_gene), str_replace_na(c_gene), sep = \".\"),\\nNA)) %>% mutate(TCR2 = ifelse(chain %sin% c(\"TRB\", \"TRD\"),\\nstr_c(str_replace_na(v_gene), str_replace_na(d_gene), str_replace_na(j_gene),\\n\\n: str_replace_na(c_gene), sep \"), NA))\\n\\n3. mutate(., TCR2 = ifelse(chain %sin% c(\"TRB\", \"TRD\"), str_c(str_replace_na(v_gene),\\nstr_replace_na(d_gene), str_replace_na(j_gene), str_replace_na(c_gene),\\n\\n: sep = \".\"), NA))\\n\\n4, mutate(., TCR1 = ifelse(chain %in% c(\"TRA\", \"TRG\"), str_c(str_replace_na(v_gene),\\nstr_replace_na(j_gene), str_replace_na(c_gene), sep = \".\"),\\nNA))\\n\\n5. mutate.data.frame(., TCR1 = ifelse(chain %in% c(\"TRA\", \"TRG\"),\\n\\nstr_c(str_replace_na(v_gene), str_replace_na(j_gene), str_replace_na(c_gene),\\n\\nsep = \".\"), NA))\\n\\n. mutate_cols(.data, dplyr_quosures(...), by)\\n\\n+ withCallingHandlers(for (i in seq_along(dots)) {\\n\\npoke_error_context(dots, i, mask = mask)\\n\\ncontext_poke(\"column\", old_current_column)\\n\\nnew_columns <- mutate_col(dots[[i]], data, mask, new_columns)\\n\\n. }, error = dplyr_error_handler(dots = dots, mask = mask, bullets = mutate_bullets,\\nerror_call = error_call, error_class = “dplyr:::mutate_error\"),\\nwarning = dplyr_warning_handler(state = warnings _state, mask = mask,\\n\\n: error_call = error_call))\\n\\n8. mutate_col(dots{[[i]], data, mask, new_columns)\\n\\n9. mask$eval_all_mutate(quo)\\n\\n10. eval()\\n\\n11. ifelse(chain %sin% c(\"TRA\", \"TRG\"), str_c(str_replace_na(v_gene),\\nstr_replace_na(j_gene), str_replace_na(c_gene), sep = \".\"),\\nNA)\\n\\n12. chain Sin% c(\"TRA\", \"TRG\")\\n\\n13. .handleSimpleError(function (cnd)\\n\\nxo\\n\\nlocal_error_context(dots, frame[[i_sym]], mask = mask)\\nif (inherits(cnd, \"dplyr:::internal_error\")) {\\nparent <- error_cnd(message = bullets(cnd))\\n\\n}\\nelse {\\n\\nparent <- cnd\\n}\\n\\n. message <~ c(cnd_bullet_header(action), i = if (has_active_group_context(mask)) cnd_bullet_cur_group_label\\nQO)\\nabort(message, class = error_class, parent = parent, call = error_call)\\n- }, “object ‘chain’ not found\", base::quote(NULL) )\\n14, h(simpleError(msg, call))\\n15. abort(message, class = error_class, parent = parent, call = error_call)\\n16. signal_abort(cnd, .file)\\n17. signalCondition(cnd)\\n\\n',\n",
|
||
" 'DNACligase\\nDNA-Polymerase (Pola)\\n\\nstran 3\\n\\nTopoisomerase\\n',\n",
|
||
" '‘Mean Methylation Levels - CHH Context\\n\\nMean Methylation Levels - CHG Context\\n\\nMean Methylation Levels - CG Context\\n\\n0.03\\n\\n01s:\\n010.\\n\\nJere] uoneKueN UeEW\\n\\nJere\" uoneKue UeEW\\n\\nJere] uoneKueN UAW\\n\\n2.000\\n\\n0.00\\n\\n0.00\\n\\n',\n",
|
||
" 'non-phosphorylatable\\n\\nphosphomimetic\\n\\nTRS120-SA | - |\\nmstzosya_| - | -\\n\\nRS120-SBD\\nRS120-SyD\\nRS120-SaBD 1\\n\\nRS120-SayD\\nRS120-SByD\\n\\nRS120-SaByD\\n\\n',\n",
|
||
" 'Chromosomes\\n\\no\\n\\n8\\n\\nae\\n\\nShow\\n\\nObserved\\n\\nNormalization (Obs | Ctrl)\\n\\nNone ¢\\n\\nBala...\\n\\nResolution (BP)\\n\\nI rrdtdot ttt td\\n2.5MB 500KB 100KB 25KB 5KB 1KB 200BP\\n\\nOMB\\n\\n',\n",
|
||
" '» Inter-chromosomal ones q value <= 0.05\\n\\n#Including the contigs like B73V4_ctgx\\n\\nzcat FitHiC.spline_pass1.res20000.significances.txt.gz | awk \\'($1\\n($3 ~ /*[1-9]1$|*10$|*B73V4_ctg [1-9] $|*B73V4_ctg10$/) && \\\\\\n\\n($1 != $3) && ($7 <= 0.05)\\' | we -1\\n\\n485\\n\\n2\\n\\n/*[1-91$ |*10$ |*B73V4_ctg[1-9]$|*B73V4_ctg10$/) && \\\\\\n\\n#Not including the contigs like B73V4_ctgx\\nzcat FitHiC.spline_pass1.res20000.significances.txt.gz | awk \\'($1\\n\\n2\\n\\n/*[1-91$|*10$/) && ($3 ~ /*[1-9]$|*10$/) && ($1 != $3) && ($7 <= 0.05)\" |\\n137\\n\\nUsing q value <= 0.15\\n\\n#Including the contigs like B73V4_ctgx\\n\\nzcat FitHiC.spline_pass1.res20000.significances.txt.gz | awk ‘($1\\n($3 ~ /*[1-9]$|*10$|*B73V4_ctg[1-9]$|*B73V4_ctg10$/) && \\\\\\n\\n($1 != $3) && ($7 <= @.15)\\' | we -1 9 1)\\n\\n638\\n\\n2\\n\\n/*[1-9]$|*10$ |*B73V4_ctg[1-9]$|*B73V4_ctg10$/) && \\\\\\n\\n#Not including the contigs like B73V4_ctgx\\n\\nzcat FitHiC.spline_pass1.res20000.significances.txt.gz | awk ‘($1 ~ /*[1-9]$|%~10$/) && ($3 ~ /*[1-9]$|*10$/) && ($1 != $3) && ($7 <= @.15)\\' §\\n\\n2\\n\\n199\\n\\nIntra-chromosomal ones\\n\\n#Including the contigs like B73V4_ctgx\\n\\n#zcat FitHiC.spline_passl.res20000.significances.txt.gz | awk \\'($1 ~ /*[1-9]$|*10$|*B73V4_ctg[1-9]$|~*B73V4_ctg10$/) && \\\\\\n#($3 ~ /*[1-9]$|*10$|*B73V4_ctg[1-9]$|*B73V4_ctg10$/) && \\\\\\n\\n#($1 == $3) && ($7 <= @.05)\\' | we -L\\n\\n#Not including the contigs like B73V4_ctgx\\n\\nzcat FitHiC.spline_pass1. res20000.significances.txt.gz | awk \\'($1 ~ /*[1-9]$|*10$/) && \\\\\\n($3 ~ /*[1-9]$|*10$/) && \\\\\\n\\n($1 == $3) && ($7 <= 0.05)\\' | we -1\\n\\n89490\\n\\nFiltering the intra chromosomal loops Per chromosome\\n\\nzcat FitHiC.spline_pass1.res20000.significances.txt.gz | awk \\'($1 == $3) && ($1 ~ /*[1-9]$|*10$/) && ($7 <= @.05)\\' | cut -f1 | sort | unig -c\\n\\n14348\\n5945\\n10496\\n10772\\n10799\\n9160\\n6935\\n7334\\n6818\\n6883\\n\\nCOYHRHVAWNER\\n',\n",
|
||
" 'ST\\n\\n= (A, —Hs)\\n',\n",
|
||
" 'dispersion\\n\\n1e-07 1e-06 = 1e-05 te-04 = e038. e022. te-01— 1e+00—1e+01\\n\\n1-08\\n\\n© gene-est\\n© fitted\\n© final\\n\\n1e-01\\n\\nT\\n1e+00\\n\\nT\\n1e+01\\n\\nT\\nte+02\\n\\nmean of normalized counts\\n\\nT\\n1e+03\\n\\n1e+04\\n\\nT T\\n1e+05 1e+06\\n\\n',\n",
|
||
" 'Learning to obtain 3D confocal images of ovules using the already established\\npipeline(4) was one of the tasks of this internship, Raw confocal images were\\nsuccessfully obtained for three ovules, showcasing the intricate details of the ovule\\nstructure. These images were collected using [mention the specific confocal microscopy\\nsetup] under [specific conditions, e.g., magnification, laser wavelength, etc.]. The\\nimages were further post-processed for creating 3D meshes, and the cellular properties\\nfrom them were extracted using the MGX platform.\\n',\n",
|
||
" 'doi: 10.1126/science.1181369.\\n',\n",
|
||
" 'proper gradient placement.\\n\\nPublications/Conferences:\\n\\nDescription: Please list any publications and/or participation at international conferences (if any).\\n',\n",
|
||
" 'eitsupi on Jan 12, 2023 Member °°*\\n\\n| believe that the RStudio Server started by the /init command is intended to be run by the root user, so it does not work\\ncorrectly by a non-root user.\\n\\nWe have fixed this by copying your Dockerfile, inserting RUN echo \"server-user=$USER\" >> /etc/rstudio/rserver. conf\\nbefore CMD [\"/init\"] and building our own image.\\n\\n| don\\'t think you have to do it. A simple Dockerfile like the following should should be sufficient:\\n\\nFROM rocker/rstudio:4 (O\\nRUN echo \"server-user=$USER\" >> /etc/rstudio/rserver.conf\\n\\n©\\n',\n",
|
||
" '[21]\\n\\n(1]\\n\\nchmod +x run_spades.sh\\n\\nbash\\n\\n./run_spades.sh\\n\\nbash\\n\\n',\n",
|
||
" \"Only minor difference from the previous one ZN\\n\\ni\\n\\nthis is the documenatation for allel.pca function -\\n\\nallel.pca(gn, n_components=10, copy=True, scaler='patterson', ploidy=2) [source]\\nPerform principal components analysis of genotype data, via singular value decomposition.\\n\\nParameters:\\n\\ngn:array_like, float, shape (n_variants, n_samples)\\n\\nGenotypes at biallelic variants, coded as the number of alternate alleles per call (i.e., @ = hom ref, 1 = het, 2 = hom\\nalt).\\n\\nn_components:int, optional\\nNumber of components to keep.\\n\\ncopy:bool, optional\\nIf False, data passed to fit are overwritten.\\n\\nscaler:{‘patterson’, ‘standard’, None}\\nScaling method; ‘patterson’ applies the method of Patterson et al 2006; ‘standard’ scales to unit variance; None centers\\nthe data only.\\n\\nploidy:int, optional\\nSample ploidy, only relevant if ‘patterson’ scaler is used.\\n\\nReturns:\\ncoords:ndarray, float, shape (n_samples, n_components)\\nTransformed coordinates for the samples.\\n\\nmodel: GenotypePCA\\n\\nModel instance containing the variance ratio explained and the stored components (a.k.a., loadings). Can be used to\\nproject further data into the same principal components space via the transform() method.\\n\\nI was however able to run the pca with multiallelic variants also, in the previous steps.\\n\\n+\\na]\\n\",\n",
|
||
" '-hic & .cool/.mcool: Binary formats for Hi-C data\\nCompressed contact matrices at multiple resolutions\\n\\nGenomic intervals for binned data\\nInteraction frequencies between loci\\nSupports multiple bin sizes & corrections in one file\\n\\n',\n",
|
||
" '[22]\\n\\navt\\n\\nfor i in *bowtie.vcf; do\\nvt peek \"$i\"\\necho \"Analysis complete for $i\"\\necho \"\"\\n\\ndone\\n\\npeek v@.5\\n\\noptions:\\n\\nstats: no.\\nno.\\n\\nno.\\n\\nno.\\n\\nno.\\n\\nno.\\n\\nno.\\n\\nno.\\n\\ninput VCF file sample1_bowtie.vcf\\n\\nof samples : 1\\nof chromosomes : 21\\n\\nMicro variants =\\n\\nof SNP : 3784\\n2 alleles :\\n\\nof MNP : 332\\n2 alleles\\n3 alleles\\n\\nof INDEL : 353\\n2 alleles\\n\\nof SNP/MNP : 11\\n2 alleles\\n\\nof SNP/INDEL : 86\\n2 alleles\\n3 alleles\\n\\nof MNP/INDEL : 40\\n2 alleles\\n3 alleles\\n\\n3784 (2.28)\\n\\n331 (1.95)\\n1 (inf)\\n\\n353 (1.08)\\n\\n11 (0.83)\\n\\n82 (0.82)\\n4 (1.00)\\n\\n38 (0.54)\\n2 (6.00)\\n\\n[2629/1155]\\n\\n[465/238]\\n[5/0]\\n\\n[183/170]\\n\\n[5/6]\\n\\n[37/45] (1.00) [41/41]\\n[2/2] (4.00) [4/1]\\n\\n[40/74] (@.65) [15/23]\\n[6/1] (2.00) [2/1]\\n\\n',\n",
|
||
" 'specified_loop_list <Loop List> is an optional positional argument which should point to a Juicebox formatted\\nloop list. HICCUPS will then return enrichments for these specified loops for each resolution. Starting with version\\n1.12.03, the given pixels are post-processed at each resolution and the results are merged across resolutions. This will\\ncreate additional files in addition to the ones created in prior versions. CPU version only searches near the diagonal (in\\norder to run in a reasonable amount of time), so it will not include regions far from the diagonal.\\n\\n-m <int> Maximum size of the submatrix within the chromosome passed on to GPU (Must be an even number greater\\nthan 40 to prevent issues from running the CUDA kernel). The upper limit will depend on your GPU. Dedicated GPUs\\nshould be able to use values such as 500, 1000, or 2048 without trouble. Integrated GPUs are unlikely to run sizes\\nlarger than 90 or 100. Matrix size will not effect the result, merely the time it takes for hiccups. Larger values (with a\\ndedicated GPU) will run fastest.\\n\\n-c <String(s)> Chromosome(s) on which HiCCUPS will be run. The number/letter for the chromosome can be used\\nwith or without appending the \"chr\" string. Multiple chromosomes can be specified using commas (e.g. 1,chr2,X,chrY)\\n\\n-r <int(s)> Resolution(s) for which HiCCUPS will be run. Multiple resolutions can be specified using commas (e.g.\\n25000,10000,5000). Due to the nature of DNA looping, it is unlikely that loops will be found at lower resolutions (i.e.\\n50kB or 100kB) IMPORTANT: if multiple resolutions are used, the flags below can be configured so that different\\nparameters are used for the different resolutions.\\n\\n-k <NONE/VC/VC_SQRT/KR> Normalizations (case sensitive) that can be selected. Generally, KR (Knight-Ruiz) balancing\\nshould be used when available.\\n\\n-f <int(s)> FDR values actually corresponding to max_q_val (i.e. for 1% FDR use 0.01, for 10%FDR use 0.1). Different\\nFDR values can be used for each resolution using commas. (e.g \"-r 5000,10000 -f 0.1,0.15\" would run HiCCUPS at 10%\\nFDR for resolution 5000 and 15% FDR for resolution 10000)\\n\\n-p <int(s)> Peak width used for finding enriched pixels in HiCCUPS. Different peak widths can be used for each\\nresolution using commas. (e.g \"-r 5000,10000 -p 4,2\" would run at peak width 4 for resolution 5000 and peak width 2\\nfor resolution 10000)\\n\\n-i <int(s)> Window width used for finding enriched pixels in HiCCUPS. Different window widths can be used for\\neach resolution using commas. (e.g \"-r 5000,10000 -p 10,6\" would run at window width 10 for resolution 5000 and\\nwindow width 6 for resolution 10000)\\n\\n-t <floats> Thresholds for merging loop lists of different resolutions. Four values must be given, separated by\\ncommas (e.g. 0.02,1.5,1.75,2). These thresholds (in order) represent: > threshold allowed for sum of FDR values of the\\nhorizontal, vertical, donut, and bottom left filters (an accepted loop must stay below this threshold) > threshold ratio\\nthat both the horizontal and vertical filters must exceed > threshold ratio that both the donut and bottom left filters\\nmust exceed > threshold ratio that at least one of the donut and bottom left filters must exceed\\n\\n-d <ints> Distances used for merging nearby pixels to a centroid. Different distances can be used for each resolution\\nusing commas. (e.g \"-r 5000,10000 -d 20000,21000\" would merge pixels within 20kB of each other at 5kB resolution\\nand within 21kB at 10kB resolution.\\n\\n—-threads <int> Number of threads to use (HiCCUPS is multi-threaded). As of Juicer Tools Version 1.13.02, the\\ndefault number of threads used is 1. Passing in a value of 0 will result in the jar calculating the number of available\\nthreads. Passing in a value >0 will result in that value being used directly.\\n',\n",
|
||
" 'In\\n\\n[176]:\\n\\ncombined_seurat <- combineExpression(\\ncombined_TCR,\\ncombined_seurat,\\ncloneCall = \"strict\",\\ngroup.by = “orig. ident\",\\nproportion = FALSE\\n\\nError in combineExpression(combined_TCR, combined_seurat, cloneCall = \"strict\",\\n\\nthere are groupings < 1\\nTraceback:\\n\\n1. stop(\"Adjust the cloneSize parameter - there are groupings < 1\")\\n2. .handleSimpleError(function (cnd)\\n-{\\nwatcher$capture_plot_and_output()\\ncnd <- sanitize_call(cnd)\\nwatcher$push(cnd)\\nswitch(on_error, continue = invokeRestart(\"eval_continue\"),\\n5 stop = invokeRestart(\"eval_stop\"), error = NULL)\\n. }, \"Adjust the cloneSize parameter - there are groupings < 1\",\\nbase: : quote(combineExpression(combined_TCR, combined_seurat,\\n\\ncloneCall = \"strict\", group.by = “orig.ident\", proportion = FALSE)))\\n\\n: Adjust the cloneSize parameter —\\n',\n",
|
||
" \"this is the documenatation for allel.pca function -\\n\\nallel.pca(gn, n_components=10, copy=True, scaler='patterson', ploidy=2) [source] Perform principal components analysis of genotype data, via singular value\\ndecomposition.\\n\\nParameters: gn:array_like, float, shape (n_variants, n_samples) Genotypes at biallelic variants, coded as the number of alternate alleles per call (i.e., 0 = hom ref, 1 = het,\\n2 = hom alt).\\n\\nn_components:int, optional Number of components to keep.\\ncopy:bool, optional If False, data passed to fit are overwritten.\\n\\nscaler:{‘patterson’, ‘standard’, None} Scaling method; ‘patterson’ applies the method of Patterson et al 2006; ‘standard’ scales to unit variance; None centers the data\\nonly.\\n\\nploidy:int, optional Sample ploidy, only relevant if ‘patterson’ scaler is used.\\nReturns: coords:ndarray, float, shape (n_samples, n_components) Transformed coordinates for the samples.\\n\\nmodel:GenotypePCA Model instance containing the variance ratio explained and the stored components (a.k.a., loadings). Can be used to project further data into the\\nsame principal components space via the transform() method.\\n\\nEven though it mentions 'Genotypes at biallelic variants' | was however able to run the pca with multiallelic variants also, in the previous steps.\\n\",\n",
|
||
" '6]\\n\\nbr be\\ncd eee data && ls -ltrh\\nbash\\ntotal 1.5M\\n-rw-rw-r-- 1 pst14 pst14 298 Dec 18 19:35 multiqc.log\\n-rw-rw-r-— 1 pst14 pst14 1.5K Dec 18 19:35 fastqc-status—check—-heatmap. txt\\n-rw-rw-r-— 1 pst14 pst14 @ Dec 18 19:35 fastqc_top_overrepresented_sequences_table.txt\\n-rw-rw-r-- 1 pst14 pst14 6.5K Dec 18 19:35 fastqc_sequence_duplication_levels_plot.txt\\n-rw-rw-r-- 1 pst14 pst14 7.2K Dec 18 19:35 fastqc_sequence_length_distribution_plot.txt\\n—rw-rw-! 1 pst14 pst14 8.2K Dec 18 19:35 fastqc_per_base_n_content_plot.txt\\n-rw-rw-r-- 1 pst14 pst14 28K Dec 18 19:35 fastqc_per_sequence_gc_content_plot_Counts. txt\\n-rw-rw-r-- 1 pst14 pst14 47K Dec 18 19:35 fastqc_per_sequence_gc_content_plot_Percentages. txt\\n-rw-rw-r-- 1 pst14 pst14 5.3K Dec 18 19:35 fastqc_per_sequence_quality_scores_plot.txt\\n-rw-rw-r-- 1 pst14 pst14 19K Dec 18 19:35 fastqc_per_base_sequence_quality_plot.txt\\n-rw-rw-r-- 1 pst14 pst14 956 Dec 18 19:35 fastqc_sequence_counts_plot.txt\\n-rw-rw-r-- 1 pst14 pst14 2.0K Dec 18 19:35 multiqc_general_stats.txt\\n—rw-rw-! 1 pst14 pst14 28 Dec 18 19:35 multiqc_software_versions.txt\\n-rw-rw-r-- 1 pst14 pst14 5.0K Dec 18 19:35 multiqc_fastqc.txt\\n-rw-rw-r-- 1 pst14 pst14 3.7K Dec 18 19:35 multiqc_sources.txt\\n-rw-rw-r-- 1 pst14 pst14 62 Dec 18 19:35 multiqc_citations.txt\\n-rw-rw-r-- 1 pst14 pst14 1.3M Dec 18 19:35 multiqc_data.json\\n\\n',\n",
|
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" \"(jpn_aman_new) [a.nalakath@frontend ~]$ jupyter notebook --no-browser --port=9006\\n\\n2025-04-23\\n2025-04-23\\n2025-04-23\\n2025-04-23\\n2025-04-23\\n2025-04-23\\n2025-04-23\\n2025-04-23\\n2025-04-23\\n2025-04-23\\n2025-04-23\\n2025-04-23\\n2025-04-23\\n2025-04-23\\n2025-04-23\\n2025-04-23\\n2025-04-23\\n2025-04-23\\n2025-04-23\\n2025-04-23\\n2025-04-23\\n2025-04-23\\n\\nHHH HHH HH HHH AHHH SS SHH\\n\\n-182\\n134.198\\n14:25:34.\\n+203\\n+203\\n- 208\\n- 208\\n-821\\n-873\\n-876\\n-877\\n-887\\n-887\\n-888\\n- 000\\n- 009\\n-018\\n-010\\n-018\\n-018\\n:25:35.011\\n14:25:35.\\n\\n198\\n\\n289\\n\\nServerApp\\nServerApp\\nServerApp\\n\\njupyter_lsp | extension was successfully linked.\\njupyter_server_terminals | extension was successfully linked.\\njupyterlab | extension was successfully linked.\\n\\nJupyterNotebookApp] 'token' has moved from NotebookApp to ServerApp. This config will be passed to ServerApp. Be sure to update your config before our next release.\\nJupyterNotebookApp] 'password' has moved from NotebookApp to ServerApp. This config will be passed to ServerApp. Be sure to update your config before our next release.\\n\\nServerApp\\nServerApp\\nServerApp\\nServerApp\\nServerApp\\nServerApp\\n\\nServerApp.token config is deprecated in 2.0. Use IdentityProvider.token.\\nnotebook | extension was successfully linked.\\n\\nnotebook_shim | extension was successfully linked.\\n\\nnotebook_shim | extension was successfully loaded.\\n\\njupyter_lsp | extension was successfully loaded.\\njupyter_server_terminals | extension was successfully loaded.\\n\\nLabApp] JupyterLab extension loaded from /data/proj2/home/students/a.nalakath/.conda/envs/jpn_aman_new/lib/python3.10/site-packages/jupyterlab\\nLabApp] JupyterLab application directory is /data/proj2/home/students/a.nalakath/.conda/envs/jpn_aman_new/share/jupyter/lab\\nLabApp] Extension Manager is 'pypi'.\\n\\nServerApp\\nServerApp\\nServerApp\\nServerApp\\nServerApp\\nServerApp\\nServerApp\\nServerApp\\n\\njupyterlab | extension was successfully loaded.\\nnotebook | extension was successfully loaded.\\nServing notebooks from local directory: /data/proj2/home/students/a.nalakath\\nJupyter Server 2.15.@ is running at:\\nhttp://localhost:9006/tree\\nhttp://127.0.0.1:9006/tree\\nUse Control-C to stop this server and shut down all kernels (twice to skip confirmation).\\nSkipped non-installed server(s): bash-language-server, dockerfile-language-server-nodejs, javascript-typescript-langserver, jedi-language-server, julia-language-server, p\\n\\nyright, python-language-server, python-lsp-server, r-languageserver, sql-language-server, texlab, typescript-language-server, unified-language-server, vscode-css—languageserver-bin, vscode-html-languageserver\\n-bin, vscode—json—-languageserver—-bin, yaml—language-server\\n\",\n",
|
||
" 'File Edit\\n\\n@ Bandage\\n\\nView\\n\\nSelect\\n\\nOutput\\n\\nOB ew@eoer zt © €& Ce)\\n\\nHelp\\n\\n=> Q & ThuDec5 21:09\\n\\nBandage - /Users/aman/Downloads/sample692_spades_out/assembly_graph_after_simplification.gfa\\n\\nGraph information\\nNodes: 57\\n\\n(7) Edges: 11\\nTotal length: 186.910\\n\\nMore info\\n\\nGraph drawing\\n\\n©@ Scope: Entire graph\\n@ Style: O Single\\ne Draw graph\\n\\nGraph display\\ne Zoom: 103,5%\\n©@ Node width: 5,0\\n\\n@_ Random colours\\n\\nNode labels\\n\\nName\\nDepth\\nCSV data:\\n\\nCustom\\nLength\\n© ~ BLAST hits\\n\\n(7) Font\\n\\nText outline\\n\\nBLAST\\n\\n(7) Create/view BLAST search\\n@ Query:\\n\\na\\nv\\n\\nDouble\\n\\nFind nodes\\n\\n© Node(s):\\n\\n@ Match: O Exact Partial\\n\\nFind node(s)\\n\\nSelected nodes (48)\\n\\n85, 211, 409, 553, 1207, 7061,\\n27387, 37093, 52795, 53239,\\n73592, 80037, 85717, 86340,\\n89960, 103818, 104062, 105292,\\n\\nTotal length: 28.503 bp\\nMean depth: 10,6x\\n\\n@ — Setcolour Set label\\n\\nSelected edge\\n\\n80037+ to 80037+\\n\\n',\n",
|
||
" 'In (626):\\n\\nIn [627]:\\n\\nIn (628):\\n\\n(Clona uant combined. PRPS,\\nclonecall=\"stric\\nchain = \"both\",\\n\\nscale = TRUE)\\ni\\n[ i\\ni\\nQ\\n\\n‘Soret\\n\\n# a total number of clones by the number of instances within the sample or run\\nclona Abundance(combined.TCR_p4,\\n\\nclonetalt = \"gene\",\\n\\nscale = FALSE)\\n\\n& sane\\n\\\\\\nclonalLength( combined. TCR_p4,\\nclonecatt=\"aa\\'\\nchain = “both\\'\\n=\\nFa\\n8. Saree\\n: i\\n\\napply(contig_list_1, nrow)\\nsapply(contig_list_2, nrow)\\n\\nError in apply(contig_list_1, nrow!\\nTraceback:\\n\\nargument “FUN\" is missing, with no default\\n\\n1. match. fun(FUN)\\n2. .handleSimpleError(function (end)\\n',\n",
|
||
" 'Mean Methylation Level\\n\\nMean Methylation Levels ~ CG Context\\n\\nFle\\n\\nMean Methylation Level\\n\\nMean Methylation Levels — CHG Context\\n\\nFile\\n\\nMean Methylation Level\\n\\nMean Methylation Levels - CHH Context\\n\\nFile\\n\\n',\n",
|
||
" '[21]\\n\\n#bcftools\\n\\nfor i in xbowtie.vcf; do\\nbcftools stats \"$i\" | grep \"*SN\"\\necho \"Analysis complete for $i\"\\necho \"\"\\n\\ndone\\n\\nbash\\n\\n',\n",
|
||
" '# Create patns to Kaltisto abundance Titles\\n\\n© Celli cenit files <- file.path(\"/mnt/volume/data/group8/kallisto_output\", samples, “abundance.tsv\")\\n>* names(files) <- samples\\n\\nY SRR21866470\\n\\n= abundance.h5 # Check that all files exist\\nmissing files <- files[!file.exists(files)]\\n\\n= abund: ui\\nabungance.tsv if (length(missing_files) > 0) {\\n\\n{} run_info.json cat(\"Warning: The following abundance.tsv files are missing:\\\\n\")\\nY SRR21866471 print (missing_files)\\n= abundance.h5S /mnt/volume/data/group8/kallisto_output/SRR21866471 }\\nSs Cat (\"ALC abundance. tsv Tiles found. \\\\n\")\\n\\n= abundance.tsv }\\n\\n{} run_info.json\\n',\n",
|
||
" 'EXPRESSION ANALYSIS\\nRNA-SEQ\\n\\n= Multiple testing correction\\n= More comparisons -> more likely to have difference by chance\\n\\n= Already known from arrays\\n= 10 000s genes/transcripts\\n= 100 000s exons\\n\\n= RNA-seq makes even more prominent\\n= The whole transcriptome\\n= Almost infinite number of potential features\\n= Genes, transcripts, exons, junctions, retained introns, microRNAs,\\nIncRNAs, retroelements, etc...\\n= Bonferroni, Benjamini-Hochberg, Bioconductor multtest\\n= http://www.bioconductor.org/packages/release/bioc/html/multtest.html\\n',\n",
|
||
" 'Cannot Connect to R Session\\n\\nx) Could not connect to the R session on RStudio\\n\\nServer.\\n\\nUnable to connect to service (1)\\n\\n',\n",
|
||
" 'multiqe\\n\\nv1.25.2\\n\\nGeneral Stats\\n\\nFastQC.\\n\\nSequence Counts\\n\\nSequence Quality Histograms\\nPer Sequence Quality Scores\\nPer Base Sequence Content\\nPer Sequence GC Content\\n\\nPer Base N Content\\n\\nSequence Length Distribution\\nSequence Duplication Levels\\nOverrepresented sequences by sample\\nTop overrepresented sequences\\nAdapter Content\\n\\nStatus Checks\\n\\nSoftware Versions\\n\\nSequence Quality Histograms\\n\\nThe mean quality value across each base position in the read.\\n\\nPhred Score\\n\\n30\\n\\n25\\n\\n20\\n\\n15\\n\\n10\\n\\n20 bp\\n\\n40 bp\\n\\n60 bp\\n\\nFastQC: Mean Quality Scores\\n\\n20 samples\\n\\n0.\\n\\n80 bp\\n\\nPosition (bp)\\n\\n100 bp\\n\\n120 bp\\n\\n140 bp\\n\\n| @ Help\\n\\n| Export Plot\\n\\nCreated with MultiQc\\n\\n© 4 rk a > Toolbox\\n',\n",
|
||
" 'Metric\\n\\nMapping Speed (sec)\\nReads/sec\\n\\nMated Pairs (%)\\n\\nBad Pairs (%)\\n\\nInsert Size Avg (bp)\\nMapped Reads (%)\\nUnambiguous Reads (%)\\nAmbiguous Reads (%)\\nPerfect Best Site (%)\\nMatch Rate (%)\\n\\nError Rate (%)\\nSubstitution Rate (%)\\nDeletion Rate (%)\\n\\nInsertion Rate (%)\\n\\nSample 1\\n120.003\\n3006.85\\n68.70%\\n18.61%\\n295.56\\n92.07%\\n83.66%\\n8.41%\\n31.60%\\n~89.83%\\n65.68%\\n63.63%\\n9.28%\\n\\n11.45%\\n\\nSample 2\\n51.256\\n7094.55\\n40.87%\\n5.25%\\n294.45\\n57.43%\\n51.62%\\n5.81%\\n19.41%\\n~83.84%\\n66.02%\\n65.63%\\n9.96%\\n\\n19.66%\\n\\nSample 3\\n48.722\\n7620.41\\n18.80%\\n1.97%\\n307.14\\n30.16%\\n26.57%\\n3.59%\\n0.14%\\n~79.97%\\n99.55%\\n99.54%\\n15.25%\\n\\n24.52%\\n\\nSample 4\\n63.959\\n5647.55\\n98.20%\\n1.65%\\n301.55\\n99.92%\\n57.99%\\n41.93%\\n29.03%\\n~97.35%\\n70.94%\\n69.62%\\n7.19%\\n\\n6.54%\\n\\nSample 5\\n67.222\\n5340.70\\n75.49%\\n20.04%\\n295.35\\n97.47%\\n95.33%\\n2.14%\\n35.71%\\n~93.39%\\n60.92%\\n60.13%\\n447%\\n\\n6.80%\\n',\n",
|
||
" '(BR =— > QQ _ VW 8B wwwuser.gwdguser.de/~txie/polycomb/nadine_micro/\\n\\nOnline Bewerbung QGIS API Docume... qgis - Trying to pe... New Script - Ea . Pastebin.com - . TargetP 2.0 - .. https://\\n\\nIndex of /~txie/polycomb/nadine_micro\\n\\nParent Directory\\n\\nCPI_CTCF.bw_RPGC.bw\\n\\nCPI_CTCF seacr_top0.01.peaks.stringent.bed\\nC_CTCF.bw_RPGC.bw\\n\\nC_CTCF seacr_top0.01.peaks.stringent.bed\\n\\nC_ctcfbed.pdf\\n\\nEig_S0k.pee\\n\\nIS correlate _25kb.pdf\\n\\nInteraction Decay_10k.pdf\\n\\nInteraction Decay _5k.pdf\\n\\nRBP1-MNase-R1-filtered-25.0K_ over RBP1 25kb S_ 10-shifts local _rescaled.np.txt\\nRBP1-MNase-R1-filtered-5.0K_over CPI_ CTCF seacr_top0.01.peaks.stringent_10-shifts.np.txt\\nRBP1-MNase-R1-filtered-5.0K_over RBP1-ctrl 09 10-shifts.np.txt\\nRBP1-MNase-R1-filtered-5.0K_over RBP1-ctrl_mis5_10-shifts.np.txt\\nRBP1-MNase-R1-filtered-5.0K_over RBP1 0.9 5kb.cooltools_10-shifts.np.txt\\nRBP1-MNase-R1-filtered-5.0K_ over RBP1 juicer 0.9 10-shifts.np.txt\\nRBP1-MNase-R1-filtered-5.0K_over_ctrl-RBP1 09 _10-shifts.np.txt\\nRBP1-MNase-R1-filtered-5.0K_ over _ctrl-RBP1_ mis5S_10-shifts.np.txt\\nRBP1-MNase-R1-filtered.mcool\\n\\nRBP1-ctrl_09.bedpe\\n\\nRBP1-ctrl_09.chrbedpe\\n\\nRBP1-ctrl_mis10.cooltools1\\n\\nRBP1-ctrl_mis5.cooltools\\n\\nRBP1.25kb.direc_125000.tsv\\n\\nRBP1.25kb.insul_125000.tsv\\n\\nRBP1.25kb.insul_score_125000.bw\\n\\nRBP1 hic\\n\\nRBP1 0.9 5kb.cooltools\\n\\nRBP1_ 0.9 5kb.cooltools.postproc\\n\\nRBP1_ 0.9 5kb.cooltools.postproc.1D.txt\\n\\nRBP1_25kb.bedli\\n\\nRBP1_25kb.bedot\\n\\nRBP1_25kb.tsv\\n\\nRBP1 25kb_ S.bedli\\n\\nRBP1_SOkb.cis.bw\\n\\nRBP1_50kb.cis.lam.txt\\n\\nRBP1_S0kb.cis.vecs.tsv\\n\\nRBP1_5kb.exp\\n\\nRBP1 juicer _0.9.chrloops\\n\\nRBP1 juicer _0.9.chrloops.1D.txt\\n\\nRBP1 juicer _0.9.loops\\n\\nall.bw\\n\\nall.bw.pdf\\n\\nall.bw.png\\n\\nall_10k.sum\\n\\ncooltool.pdf\\n\\ncooltool_self.pdf\\n\\ncpi_ctcf.pdf\\n\\neel RANI\\n\\nD1 Gltaxend OO NY nue tel OELK CIN chifte anal\\n\\nenaled nen tet\\n\\n',\n",
|
||
" '17\\n\\n18 # Rename columns to match scRepertoire expectations\\n19 colnames(S1) <- cC\\n20 \"cell_id\", \"total_read_count\", \"total_moLecule_count\",\\n21 \"v_call\", \"j_call\", \"c_gene\", \"cdr3_nt\", \"cdr3\",\\n22 \"alpha_gamma_read_count\", \"alpha_gamma_molecule_count\",\\n23 \"beta_v_gene\", \"d_call\", \"beta_j_gene\", \"beta_c_gene\",\\n24 \"beta_cdr3_nt\", \"beta_cdr3\", \"beta_read_count\", \"beta_molecule_count\",\\n25 \"paired_chains\", \"cell_type\", \"high_quality\"\\n26 (+)\\n27\\n28 # Select the key columns for scRepertoire\\n29 S1 <- S1[, cC\"cell_id\", \"v_call\", \"j_call\", \"d_call\", \"cdr3\", \"cell_type\")]\\n3@ contig_list <- list(S1)\\n31 contig_list <- loadContigsCinput = contig_list, format = \"BD\")\\n32\\n32:1 (Top Level) +\\nConsole’ Terminal Background Jobs\\n\\nR~ R4.4.2 - ~/\\n\\n+)\\n\\n> # Select the key columns for scRepertoire\\n> $1 <- S1[, cC\"cell_id\", \"v_call\", \"j_cal1\", \"d_call\", \"cdr3\", \"cell_type\")]\\n\\n> contig_list <- lList(S1)\\n\\n> contig_list <- loadContigsCinput = contig_list, format = \"BD\")\\nError in *[.data.frame*Cdf[[iJ], , cC\"cell_id\", \"locus\", \"v_call\", \"d_call\",\\n\\nundefined columns selected\\n> # Select the key columns for scRepertoire\\n\\n> S1 <- S1[, cC(\"cell_id\", \"v_call\", \"j_call\", \"d_call\", \"cdr3\", “cell_type\")]\\n\\n> contig_list <- lList(S1)\\n\\n> contig_list <- loadContigsCinput = contig_list, format = \"BD\")\\nError in *[.data.frame*Cdf[[iJ], , cC\"cell_id\", \"locus\", \"v_call\", \"d_call\",\\nundefined columns selected\\n\\n',\n",
|
||
" 'Usefulness of crosses\\n\\nSelection of Parents\\n\\nU, = Cj +R;\\n\\nm midparent value, perfect predictor of cj with additive gene action\\n\\n4 and absence of epistasis\\n\\n0.7 ¢ (0.8\\n\\nVinyl\\nRij = iho,\\n\\ni prediction difficult\\n\\n',\n",
|
||
" '#List of data frames example\\n\\nLibrary(scRepertoire)\\n\\nS1 <- read.csv(\"/home/rstudio/run071_VDJ_perCell.csv\")\\ncontig_list <- list(S1)\\n\\ncontig_list <- LoadContigsCinput = contig_list, format = \"BD\")\\n\\nOuRWNP\\n\\n6:1 (Top Level) +\\n\\nConsole’ Terminal Background Jobs\\n\\nR~R4.4.2 - ~/\\n\\ncould not find function \"LoadContigs\"\\n> #List of data frames example\\n> library(scRepertoire)\\nAttaching package: ‘scRepertoire’\\nThe following object is masked _by_ ‘.GlobalEnv’:\\ncontig_list\\n> contig_list <- loadContigsCinput = contig_list, format = \"BD\")\\n\\nError in “[.data.frame*Cdf[[iJ], , cC\"cell_id\", \"locus\", \"v_call\", \"d_call\"\\nundefined columns selected\\n',\n",
|
||
" '© multiqe\\n\\nv1.25.2\\n\\nGeneral Stats\\n\\nFastQC.\\n\\nSequence Counts\\nSequence Quality Histograms\\n\\nPer Sequence Quality Scores\\n\\nPer Base Sequence Content\\n\\nPer Sequence GC Content\\n\\nPer Base N Content\\n\\nSequence Length Distribution\\nSequence Duplication Levels\\nOverrepresented sequences by sample\\nTop overrepresented sequences\\nAdapter Content\\n\\nStatus Checks\\n\\nSoftware Versions\\n\\nSequence Length Distribution (92)\\n\\nThe distribution of fragment sizes (read lengths) found. See the FastQC help\\n\\nRead Count\\n\\nSequence Duplication Levels (ZS\\n\\nThe relative level of duplication found for every sequence.\\n\\n% of Library\\n\\n45k\\n\\n40k\\n\\n35k\\n\\n30k.\\n\\n25k\\n\\n20k\\n\\n1k\\n\\n10k\\n\\n5k\\n\\n100%\\n\\n80%\\n\\ng\\n\\ng\\n\\n40 bp\\n\\n60 bp\\n\\nFastQC: Sequence Length Distribution\\n\\n20 samples\\n\\nExport Plot\\n\\n80 bp 100 bp 120 bp 140 bp.\\nSequence Length (bp) Created with Muiac\\n@Help\\nExport Plot\\nFastQC: Sequence Duplication Levels\\n20 samples\\n>100 >500 >ik >Sk >10k+\\n\\n7 8\\n\\neo\\n\\n9\\n\\nPasenileaeien | aces\\n\\nOF YY Tootbox\\n\\nroe\\n\\no 7\\n',\n",
|
||
" '> Science. 2009 Oct 9;326(5950):289-93. doi: 10.1126/science.1181369.\\n\\nComprehensive mapping of long-range interactions\\nreveals folding principles of the human genome\\n\\nErez Lieberman-Aiden ’, Nynke L van Berkum, Louise Williams, Maxim Imakaev, Tobias Ragoczy,\\nAgnes Telling, Ido Amit, Bryan R Lajoie, Peter J Sabo, Michael O Dorschner, Richard Sandstrom,\\nBradley Bernstein, M A Bender, Mark Groudine, Andreas Gnirke, John Stamatoyannopoulos,\\nLeonid A Mirny, Eric S Lander, Job Dekker\\n\\nAffiliations + expand\\nPMID: 19815776 PMCID: PMC2858594 DOI: 10.1126/science.1181369\\n\\nAbstract\\n\\nWe describe Hi-C, a method that probes the three-dimensional architecture of whole genomes by\\ncoupling proximity-based ligation with massively parallel sequencing. We constructed spatial\\nproximity maps of the human genome with Hi-C at a resolution of 1 megabase. These maps confirm\\nthe presence of chromosome territories and the spatial proximity of small, gene-rich chromosomes.\\nWe identified an additional level of genome organization that is characterized by the spatial\\nsegregation of open and closed chromatin to form two genome-wide compartments. At the\\nmegabase scale, the chromatin conformation is consistent with a fractal globule, a knot-free,\\npolymer conformation that enables maximally dense packing while preserving the ability to easily\\nfold and unfold any genomic locus. The fractal globule is distinct from the more commonly used\\nglobular equilibrium model. Our results demonstrate the power of Hi-C to map the dynamic\\nconformations of whole genomes.\\n',\n",
|
||
" 'Observed Contact Count\\n\\n10!\\n\\n—@- Median Contact Count\\n-@- Mean Contact Count\\ninterquartile Range (IQR)\\n\\n- $_$__—___—_.\\n\\nBinned Median and Mean Contact Counts with IQR\\n\\n10°\\n\\nGenomic Distance (bp)\\n\\n',\n",
|
||
" '[19]\\n\\n#ALL vcf files\\nls -ltrh *bowtie.vcf\\n\\nbash\\n\\n-rw-rw-r-— 1 pst14 pst14 2.3M Jan\\n-rw-rw-r-— 1 pst14 pst14 2.8M Jan\\n-rw-rw-r-— 1 pst14 pst14 61K Jan\\n-rw-rw-r-— 1 pst14 pst14 988K Jan\\n-rw-rw-r-— 1 pst14 pst14 6.3M Jan\\n\\n25 17:59\\n25 18:07\\n25 18:12\\n25 18:17\\n25 18:21\\n\\nsamplel_bowtie.\\nsample2_bowtie.\\nsample3_bowtie.\\nsample4_bowtie.\\nsample5_bowtie.\\n\\nvef\\nvef\\nvef\\nvef\\nvef\\n\\n',\n",
|
||
" 'RAB-A‘1b is differentially enriched in TRS120-\\nSaByA and -SaByD interactomes\\n\\nusny\\nEPSILON1-COP & EPSILON2-COP\\nAPL27\\n\\neL2ay\\n\\nTRS120-SapyA\\n‘TRS120-SapyD\\n\\nzos\\n1 on\\nDAPOC2\\n\\nJenner en\\nUACS4 8 LACSS\\n\\nFPS!\\nACLAS\\nSHO\\n\\nALATS:\\n\\n9-1 & EL\\n\\nGL1By & EL18Z\\n\\n—— =\\nProtein kinase supertamily protein we\\nAMK2 Dl\\n050 02 0.00 025 0.50\\n\\nmean\\ncompared to TRS120-WT\\n\\n',\n",
|
||
" 'Yes, the statement implies that an advantageous allele is lost most of the time, especially when its,\\ninitial frequency is low.\\n\\nProbability of Fixation\\nThe fixation probability of an advantageous allele in a population is approximately:\\nPix © 2hs\\n\\nwhere:\\n\\n+h: Dominance coeffi\\n\\nant (h = 0.5 for additive alleles).\\n\\n+ 8: Selection coefficient (strength of selection).\\n\\nThis represents the probability that an advantageous allele will reach fixation (frequency = 1) in the\\npopulation, starting from a single copy.\\n\\nWhy is the advantageous allele often lost?\\n\\n+ Infinite populations, genetic drift causes random fluctuations in allele frequencies.\\n\\n+ When an advantageous allele starts at a low frequency (e.g., a single copy), drift dominates\\nselection, leading to a high probability of loss.\\n\\nial Frequency:\\n\\n+ Ifthe advantageous allele appears as a single mutation, i\\ndiploid population of size N)\\n\\nial frequency is\\n\\n+ Even with selection, the allele can be lost due to random chance.\\n\\n+ Fora strongly advantageous allele (s is large), the fixation probability increases.\\n\\n= However, for most realistic values of s, fixation probabilities are still low, especially in small\\npopulations.\\n\\nKey Insight\\n\\nEven though an advantageous allele has a higher chance of fixation compared to a neutral or\\ndeleterious allele, it\\n\\nis still more likely to be lost in the early generations due to genetic drift, unless it\\nis strongly favored or the population size is large.\\n\\nWould you like a mathematical or simulated example to demonstrate this?\\n',\n",
|
||
" '@ ZoomWorkplace Meeting View Edit\\n\\nx, Layout v\\nER + ‘5 Reset\\nNew\\n¥ Slide » Section ¥\\n\\nClipboard SS Slides\\n\\n10\\n\\nWo 7\\n\\nSlide 7 of 14 [4 English (India) x Accessibility: Investigate\\n\\nWindow Help\\n\\nFont Paragraph\\n\\nPatch A\\nepidermis\\n\\nPatch B\\nepidermis\\n\\ndistance along PD axis (um)\\n\\nfor patch B of epidermis\\n\\nVeOrot © & BD\\n\\n. Arrange\\n\\nDrawing\\n\\n* KK\\n\\n2-lll\\n\\n2-IV 2-V\\n\\n= Notes\\n\\n[om]\\n=°\\n\\nPD Find m pa\\nab Replace v\\nCreate PDF Create PDF and\\nSelect ¥ and Share link Share via Outlook\\nEditing Adobe Acrobat\\n\\nDistance along PD axis increases\\nacross stages in patch B of\\nepidermis\\n\\nVv\\n\\nThere is some sort of growth\\nalong PD direction in the\\nepidermis\\n\\n§8 comments GW\\n\\nWed Feb 12 22:16\\n\\nAdd-ins\\n\\nAdd-ins ON\\n\\n',\n",
|
||
" 'Our Final Final\\n\\nFeature Literature Findings Findings ‘Agreement\\nmet! mutant loses CG v Full match\\nmethylation\\nmet affects CHG/CHH Full match\\nmethylation\\nmet! leads to TE v Full match\\n\\nhypomethylation\\n\\n@ (not Vv Needs\\n\\nmett retains low CG in\\n\\npericentromeric regions checked) further testing\\nsuvr5 mutant has reduced v Full match\\nH3K9me2\\nsuvr5 does not reduce CG vv Full match\\nmethylation\\nsuvr5 does not regulate CHG (CHG methylation in TEs is Full match\\nmethylation in TEs regulated by KYP, SUVH5, SUVH6\\n\\nvia CMT3)\\nsuvr5 does not strongly affect Full match\\nCHH methylation\\nSUVRS deposits H3K9me2 vv Full match\\nindependently of DNA\\nmethylation\\nSUVRS binds DNA via zinc- v Full match\\n\\nfingers, not an SRA domain\\n\\nFinal Standing\\n\\nOur findings fully align with published literature.\\n\\nmet mutant exhibits expected global CG loss and secondary CHG/CHH reductions.\\n suvr5 mutant affects H3K9me2 but does not regulate CHG methylation in TEs.\\n\\n SUVRS functions independently of DNA methylation, binding DNA via zinc-fingers instead of\\nan SRA domain.\\n\\nThis is the final validated conclusion with no prior assumptions. +7\\n',\n",
|
||
" '(aman_conda_env) aman@unicorn:/mnt/storage3/aman/wdbasejuicer/aligned$ ~/juicer_base/scripts/juicer_tools hiccups inter.hic ./hiccups_out\\n\\nError: Unable to access jarfile /home/aman/juicer_base/scripts/juicer_tools.jar\\n\\n(aman_conda_env) aman@unicorn:/mnt/storage3/aman/wdbasejuicer/aligned$ 1s ~/juicer_base/scripts/juicer_tools.jar\\n\\n1s: cannot access \\'/home/aman/juicer_base/scripts/juicer_tools.jar\\': No such file or directory\\n\\n(aman_conda_env) aman@unicorn: /mnt/storage3/aman/wdbasejuicer/aligned$ 1s ~/juicer_base\\n\\naligned fastq references restriction_sites scripts\\n\\n(aman_conda_env) aman@unicorn:/mnt/storage3/aman/wdbasejuicer/aligned$ 1s ~/juicer_base/scripts/\\n\\nREADME .md cleanup.sh diploid.p1l fragment_4dnpairs.pl juicer_hiccups.sh mega.sh relaunch_prep.sh\\nadjust_insert_size.awk common/ diploid.sh index_by_chr. awk juicer_postprocessing.sh mega_from_bams.sh sam_to_mnd.sh\\n\\ncheck.sh conversion.sh diploid_split.awk juicer.sh juicer_tools mega_from_bams_diploid.sh sam_to_pre.awk\\n\\nchimeric_sam. awk countligations.sh dups_sam. awk juicer_arrowhead.sh juicer_tools.2.20.00.jar | merge-stats.jar stats_sub.awk\\n\\n(aman_conda_env) aman@unicorn: /mnt/storage3/aman/wdbasejuicer/aligned$ 1s ~/juicer_base/scripts/c\\n\\ncheck.sh chimeric_sam.awk cleanup.sh common/ conversion.sh countligations.sh\\n\\n(aman_conda_env) aman@unicorn: /mnt/storage3/aman/wdbasejuicer/aligned$ 1s ~/juicer_base/scripts/c\\n\\ncheck.sh chimeric_sam.awk cleanup.sh common/ conversion.sh countligations.sh\\n\\n(aman_conda_env) aman@unicorn:/mnt/storage3/aman/wdbasejuicer/aligned$ 1s ~/juicer_base/scripts/common/\\n\\nREADME .md chimeric_sam.awk countligations.sh diploid_split.awk index_by_chr. awk juicer_hiccups.sh juicer_tools.2.20.0@.jar mega_from_bams.sh relaunch_prep.sh stats_sub.awk\\nadjust_insert_size.awk cleanup.sh diploid.pl dups_sam. awk juicer.sh juicer_postprocessing.sh juicer_tools.jar mega_from_bams_diploid.sh sam_to_mnd.sh\\ncheck.sh conversion.sh diploid.sh fragment_4dnpairs.pl juicer_arrowhead.sh juicer_tools mega.sh merge-stats.jar sam_to_pre.awk\\n(aman_conda_env) aman@unicorn:/mnt/storage3/aman/wdbasejuicer/aligned$ ~/juicer_base/scripts/c hiccups inter.hic ./hiccups_out\\n\\ncheck.sh chimeric_sam.awk cleanup.sh common/ conversion.sh countligations.sh\\n\\n(aman_conda_env) aman@unicorn:/mnt/storage3/aman/wdbasejuicer/aligned$ ~/juicer_base/scripts/common/juicer_ hiccups inter.hic ./hiccups_out\\n\\njuicer_arrowhead.sh juicer_hiccups.sh juicer_postprocessing.sh juicer_tools\\n\\n(aman_conda_env) aman@unicorn:/mnt/storage3/aman/wdbasejuicer/aligned$ ~/juicer_base/scripts/common/juicer_tools hiccups inter.hic ./hiccups_out\\n\\nWARNING: sun.reflect.Reflection.getCallerClass is not supported. This will impact performance.\\nReading file: inter.hic\\nNo valid configurations specified, using default settings\\nUsing 1 CPU thread(s) for primary task\\nDefault settings for 5kb, 1@kb, and 25kb being used\\nRunning HiCCUPS for resolution 5000\\njcuda.CudaException: Could not prepare PTX for source file \\'/tmp/temp_JCuda_872358973458234436.cu\\'\\nat jcuda.utils.KernelLauncher.create(KernelLauncher. java: 389)\\nat jcuda.utils.KernelLauncher.create(KernelLauncher. java:321)\\nat jcuda.utils.KernelLauncher.compile(KernelLauncher.java:270)\\nat juicebox.tools.utils.juicer.hiccups.GPUController.<init>(GPUController.java:72)\\nat juicebox.tools.clt.juicer.HiCCUPS.buildGPUController(HiCCUPS. java:559)\\nat juicebox.tools.clt.juicer.HiCCUPS.runCoreCodeForHiCCUPS(HiCCUPS. java: 486)\\nat juicebox.tools.clt.juicer.HiCCUPS.access$20@(HiCCUPS.java:158)\\nat juicebox.tools.clt.juicer.HiCCUPS$1.run(HiCCUPS. java:415)\\nat java.base/java.util.concurrent.ThreadPoolExecutor. runWorker(ThreadPoolExecutor.java:1144)\\nat java.base/java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:642)\\nat java.base/java. lang. Thread. run(Thread. java:1570)\\nCaused by: java.io.IOException: Cannot run program \"nvcc\": error=2, No such file or directory\\nat java.base/java.lang.ProcessBuilder. start (ProcessBuilder.java:1170)\\nat java.base/java.lang.ProcessBuilder.start(ProcessBuilder. java:1089)\\nat java.base/java.lang.Runtime.exec(Runtime. java:681)\\nat java.base/java.lang.Runtime.exec(Runtime. java:491)\\nat java.base/java.lang.Runtime.exec(Runtime. java:366)\\nat jcuda.utils.KernelLauncher.preparePtxFile(KernelLauncher. java:1113)\\nat jcuda.utils.KernelLauncher.create(KernelLauncher. java: 385)\\n«+. 10 more\\nCaused by: java.io.I0Exception: error=2, No such file or directory\\nat java.base/java.lang.ProcessImpl.forkAndExec(Native Method)\\nat java.base/java.lang.ProcessImpl.<init>(ProcessImpl.java:295)\\nat java.base/java.lang.ProcessImpl.start(ProcessImpl.java:225)\\nat java.base/java.lang.ProcessBuilder.start(ProcessBuilder.java:1126)\\n. 16 more\\nGPU/CUDA Installation Not Detected\\nExiting HiCCUPS\\n(aman_conda_env) aman@unicorn:/mnt/storage3/aman/wdbasejuicer/aligned$ ~/juicer_base/scripts/common/juicer_tools hiccups --cpu --threads 16 inter.hic ./hiccups_out\\nWARNING: sun.reflect.Reflection.getCallerClass is not supported. This will impact performance.\\nReading file: inter.hic\\nNo valid configurations specified, using default settings\\nWARNING - You are using the CPU version of HiCCUPS.\\nThe GPU version of HiCCUPS is the official version and has been tested extensively.\\nThe CPU version only searches for loops within 8MB (by default) of the diagonal and is still experimental.\\nUsing 16 CPU thread(s) for primary task\\nDefault settings for 5kb, 1@kb, and 25kb being used\\nRunning HiCCUPS for resolution 5000\\n',\n",
|
||
" 'Cannot Connect to R Session\\n\\nx) Could not connect to the R session on RStudio\\nServer.\\n\\nUnable to connect to service (1)\\n\\n',\n",
|
||
" 'Chair of Molecular Neurobiology\\n\\nAssistant Professorship of Agribusiness and Food Industry Economics\\nAssistant Professorship of Computational Plant Biology\\n\\nAssistant Professorship of Fluid Dynamics of complexe Biosystems\\nAssistant Professorship of Forest and Agroforest Systems\\n\\nAssistant Professorship of Green Technologies in Landscape Architecture\\nAssistant Professorship of Infection Pathogenesis\\n\\nAssistant Professorship of Land Surface - Atmosphere Interactions\\nAssistant Professorship of Neuronal Control of Metabolism\\nAssistant Professorship of Plant Genetics\\n\\nAssistant Professorship of Plant-Insect Interaction\\n\\nAssistant Professorship of Precision Agriculture\\n\\nAssistant Professorship of Protein Chemistry\\n\\nAssistant Professorship of Reproductive Biology\\n\\nAssistant Professorship of Translational Microbiome Data Integration\\nAssistant Professorship of Urban Productive Ecosystems\\n\\nAssociate Professorship for Population Genetics\\n\\nAssociate Professorship of Agrimechatronics\\n\\nAssociate Professorship of Bio-Informatics\\n\\nAssociate Professorship of Biostatistics\\n\\nAssociate Professorship of Biotechnology of Horticultural Crops\\nAssociate Professorship of Biotechnology of Natural Products\\nAssociate Professorship of Biothermodynamics\\n\\nAssociate Professorship of Chemoinformatics and Protein Modelling\\nAssociate Professorship of Circular Economy\\n\\nAssociate Professorship of Crop Physiology\\n\\nAssociate Professorship of Ecoclimatology\\n\\nAssociate Professorship of Food Process Engineering\\n\\nAssociate Professorship of Forest Management\\n\\nAssociate Professorship of Forest Management\\n\\nAssociate Professorship of Forest Nutrition and Water Resources\\nAssociate Professorship of Forest Nutrition and Water Resources\\nAssociate Professorship of Fungal Biotechnology in Wood Science\\nAssociate Professorship of Geobotany\\n\\nAssociate Professorship of Geomorphology and Soil Science\\nAssociate Professorship of Governance in International Agribusiness\\nAssociate Professorship of Land Management\\n\\nAssociate Professorship of Landscape Architecture and Regional Open Space\\n',\n",
|
||
" '(new_cooltools) [papantonis1@gwdu101 aman]$ csvtk filter -f \\'$n_valid!=\"nan\" && $count.sum!=\"nan\" && $balanced.sum!=\"nan\" && $count.avg!=\"nan\" && $balanced.avg!=\"nan\"\\' GEO2459_expected_5kb.tsv > GEO245\\nted_5kb_clean.tsv\\n\\n[ERRO] invalid filter: $n_valid!=\"nan\" && $count.sum!=\"nan\" && $balanced.sum!=\"nan\" && $count.avg!=\"nan\" && $balanced.avg!=\"nan\"\\n\\n(new_cooltools) [papantonis1@gwdu101 aman]$ csvtk filter -t -f \\'$4!=\"nan\" && $5!=\"nan\" && $6!=\"nan\" && $7!=\"nan\" && $8!=\"nan\"\\' GEO2459_expected_5kb.tsv > GE02459_expected_5kb_clean.tsv\\n\\n[ERRO] invalid filter: $4!=\"nan\" && $5!=\"nan\" && $6!=\"nan\" && $7!=\"nan\" && $8!=\"nan\"\\n\\n(new_cooltools) [papantonis1@gwdu101 aman]$ head -n1 GE02459_expected_5kb.tsv | tr \\'\\\\t\\' \\'\\\\n\\' | nl\\n\\nregion1\\n\\nregion2\\n\\ndist\\nn_valid\\n\\ncount.sum\\n\\nbalanced.sum\\n\\ncount.avg\\n\\nbalanced.avg\\n\\n(new_cooltools) [papantonis1@gwdu1@1 aman]$ csvtk filter -t -f \\'$4\\n[ERRO] invalid filter: $4!=\"nan\" && $5!=\"nan\" && $6!=\"nan\" && $7!=\\n(new_cooltools) [papantonis1@gwdu1@1 aman]$ ff\\n\\nONAORWNP\\n\\nnan\" && $5\\nan\" && $8!=\\n\\n\"nan\" && $6!=\"nan\" && $7!=\"nan\" && $8!=\"nan\"\\' GEO2459_expected_5kb.tsv > GEO2459_expected_5kb_clean.tsv\\nan\"\\n\\n',\n",
|
||
" 'In [515]: combined_TCR <— combineTCR(\\nlist(patient3 = contig_list_1, patient4 = contig_list_2),\\nsamples = c(\"patient3\", \"patient4\"),\\nremoveNA = FALSE,\\nremoveMulti = FALSE,\\nfilterMulti = FALSE\\n)\\n\\nError in (function (..., row.names = NULL, check. rows = FALSE, check.names = TRUE, : arguments imply differing numb\\ner of rows: 5504, 5304, 3005, 2467, 15003, 5011, 18513, 347\\nTraceback:\\n\\n1. .checkContigs(input.data)\\n2. lapply(seq_len(length(df)), function(x) {\\ndf[{x]] <- if (!is.data.frame(df[[x]]))\\nas.data. frame(df[[x]])\\nelse df[[x]]\\ndf[{x]] [df f[x]] == \"\"] <- NA\\ndf [[x]]\\n» +)\\nFUN(X[[il], ---)\\nas.data. frame(df[[x]])\\nas.data. frame. list (df[[x]])\\ndo.call(data. frame, c(x, alis))\\n(function (..., row.names = NULL, check. rows\\nfix.empty.names = TRUE, stringsAsFactors\\n\\nNOU DW\\n\\nFALSE, check.names = TRUE,\\nFALSE)\\n\\na ee ney See et ee See Se ee ee Yay\\n',\n",
|
||
" 'Bottleneck models\\n\\n(A) (B)\\n\\ntime\\n\\npopulation size\\n\\nFigure 5.2: Two cases in a bottleneck mode. (A) Only one ancestral line survives the\\nbottleneck. (B) Two or more lines survive which leads to different patterns in observed\\ndata.\\n\\n8, 8, < Oy 8, > Ow\\nTajima‘s D D<0 D>0\\n\\nIt is more difficult for bottleneck modell!!\\n',\n",
|
||
" 'aman@Laptop-von-Aman docker_rstud % docker exec -it b7629dc273da \\\\\\n\\njupyter notebook --ip=0.0.0.8 --port=8899 --no-browser --allow-root --NotebookApp.token=\\'\\' --NotebookApp.password=\\'\\'\\n[I @9:0@:37.733 NotebookApp] Writing notebook server cookie secret to /root/.local/share/jupyter/runtime/notebook_cookie_secret\\n[I @9:00:37.74@ NotebookApp] Authentication of /metrics is OFF, since other authentication is disabled.\\n\\nee\\nPlldt. Wo ro) ura)\\n\\\\_U_/ | «_/\\\\ LL -\\\\__ | \\\\-_-\\\\___ |\\n\\nRead the migration plan to Notebook 7 to learn about the new features and the actions to take if you are using extensions.\\nhttps://jupyter—-notebook. readthedocs.io/en/latest/migrate_to_notebook7.html\\nPlease note that updating to Notebook 7 might break some of your extensions.\\n\\n@9:00:39.987 NotebookApp] All authentication is disabled. Anyone who can connect to this server will be able to run code.\\n2025-04-09 @9:00:48.009 LabApp] \\'ip\\' has moved from NotebookApp to ServerApp. This config will be passed to ServerApp. Be sure to update your config before our next release.\\n2025-04-09 @9:00:48.01@ LabApp] \\'port\\' has moved from NotebookApp to ServerApp. This config will be passed to ServerApp. Be sure to update your config before our next release.\\n2025-04-09 @9:00:48.011 LabApp] \\'token\\' has moved from NotebookApp to ServerApp. This config will be passed to ServerApp. Be sure to update your config before our next release.\\n2025-04-09 @9:00:48.011 LabApp] \\'password\\' has moved from NotebookApp to ServerApp. This config will be passed to ServerApp. Be sure to update your config before our next release.\\n2025-04-09 @9:00:48.011 LabApp] \\'password\\' has moved from NotebookApp to ServerApp. This config will be passed to ServerApp. Be sure to update your config before our next release.\\n2025-04-09 @9:00:48.011 LabApp] \\'allow_root\\' has moved from NotebookApp to ServerApp. This config will be passed to ServerApp. Be sure to update your config before our next release.\\n2025-04-09 @9:00:48.012 LabApp] \\'allow_root\\' has moved from NotebookApp to ServerApp. This config will be passed to ServerApp. Be sure to update your config before our next release.\\n09:00:48.164 NotebookApp] Error loading server extension jupyterlab\\nTraceback (most recent call last):\\nFile \"/opt/conda/envs/scanpy_v1.10.4_r/lib/python3.10/site-packages/notebook/notebookapp.py\", line 2050, in init_server_extensions\\nfunc(self)\\nFile \"/opt/conda/envs/scanpy_v1.10.4_r/lib/python3.10/site-packages/jupyterlab/serverextension.py\", line 71, in load_jupyter_server_extension\\nextension. initialize()\\nFile \"/opt/conda/envs/scanpy_v1.10.4_r/lib/python3.10/site-packages/jupyterlab/labapp.py\", line 926, in initialize\\nsuper().initialize()\\nFile \"/opt/conda/envs/scanpy_v1.10.4_r/lib/python3.10/site-packages/jupyter_server/extension/application.py\", line 437, in initialize\\nself._prepare_handlers()\\nFile \"/opt/conda/envs/scanpy_v1.10.4_r/lib/python3.10/site—packages/jupyter_server/extension/application.py\", line 327, in _prepare_handlers\\nself.initialize_handlers()\\nFile \"/opt/conda/envs/scanpy_v1.10.4_r/lib/python3.10/site-packages/jupyterlab/labapp.py\", line 738, in initialize_handlers\\npage_config[\"token\"] = self.serverapp.identity_provider.token\\nAttributeError: \\'NotebookApp\\' object has no attribute ‘identity_provider\\'\\n[I @9:0@:48.409 NotebookApp] Serving notebooks from local directory: /home/rstudio\\n248.409 NotebookApp] Jupyter Notebook 6.5.7 is running at:\\n:48.41@ NotebookApp] http://b7629dc273da:8899/\\n:48.411 NotebookApp] Use Control-C to stop this server and shut down all kernels (twice to skip confirmation).\\n\\nSEZ ZTTTzz=z\\n\\n',\n",
|
||
" 'aman@lima-apptainer:~$ apptainer exec --fakeroot --writable-tmpfs rstudio_v@.1.sif bash -c \"chmod -R 777 /home/aman && mkdir -p /var/run && rserver --server-user=aman --www-port=8785 --auth-none\\nemonize=@ --secure-cookie-key-file=/tmp/rstudio-server—cookie-key\"\\n\\nTTY detected. Printing informational message about logging configuration. Logging configuration loaded from \\'/etc/rstudio/logging.conf\\'. Logging to \\'syslog\\'.\\n\\nTTY detected. Printing informational message about logging configuration. Logging configuration loaded from \\'/etc/rstudio/logging.conf\\'. Logging to \\'syslog\\'.\\n',\n",
|
||
" \"[7]\\n\\n# import standard python libraries\\nimport numpy as np\\n\\nimport matplotlib.pyplot as plt\\nimport seaborn as sns\\n\\nimport pandas as pd\\n\\nimport os\\n\\n® 0.0s\\n\\nModuleNotFoundError Traceback (most recent call last)\\nCell In[7], line 4\\n\\nimport numpy as np\\n\\nimport matplotlib.pyplot as plt\\n\\nimport seaborn as sns\\n\\nimport pandas as pd\\n\\nimport os\\n\\n---->\\n\\nIn In IB Iw IN\\n\\nModuleNotFoundError: No module named 'seaborn'\\n\",\n",
|
||
" 'Genome vv Tracks ¥ Sample Info v Session v Share Bookmark Save Image Circular View v Help v\\n\\nIGV oxford_e...me.fasta tig00000002:1,989,819-1,993,234 Q 3,416 bp (Select Tracks ) (Crosshairs )(_Center Line )(TrackLabels) @ +)\\n1,990 kb j 1,991 kb j 1,992 kb j 1,993 kb\\nAQ 0 EA A MY TAY A AY a\\n|= SS SS en |\\ntnaB tnaA mnmE_1\\n\\nINSTITUTE\\n\\nHeng igv.org UCSan Diego fe BROAD\\n\\n',\n",
|
||
" 'v What does data in a count matrix look like?\\n\\n# Lets examine a few genes in the first thirty cells\\npbmc.data[c(\"CD3D\", \"TCL1A\", \"MS4A1\"), 1:30]\\n',\n",
|
||
" 'Leaf Hi-C K4me3 HiChIP K27me3 HiChIP\\n\\nface mar rapa mat\\n\\neQTL-gene\\nlinks >20 kb\\n\\nshuffled pairs :\\n\\n',\n",
|
||
" 'In\\n\\n[1]:\\n\\nlibrary (Seurat)\\nlibrary (SeuratData)\\nlibrary (patchwork)\\n\\nLoading required package: SeuratObject\\n\\nLoading required package: sp\\n\\nAttaching package: ‘SeuratObject’\\n\\nThe following objects are masked from ‘package:base’:\\n\\nintersect, t\\n\\n’\\n\\nError in library(SeuratData): there is no package called ‘SeuratData\\nTraceback:\\n\\n1. stop(packageNotFoundError(package, lib.loc, sys.call()))\\n',\n",
|
||
" 'lf T’ is not significantly smaller than the fluctuation scale, the harmonic mean calculation risks\\nsmoothing out critical periods of small population size, underestimating the true effect of genetic\\ndrift on N.. For accurate modeling of genetic processes, T < min|[.N;] ensures that the\\ncalculation aligns with the biological timescales of population size changes and their genetic\\n\\nconsequences.\\n',\n",
|
||
" '‘Moan Methyaton Lave!\\n\\nMean Methylation Levols - CHH Context\\n\\nMean Methylation Level\\n\\na\\n\\n8\\n\\nMean Methylation Levels ~ CHH Context\\n\\nFe\\n\\nFile\\n\\nMean Methylation Levels ~ CHH Context\\n\\n',\n",
|
||
" 'About Blog Examples Plugins Docs ©\\n\\ni no+x\\nO +X) Ser4 ae\\n\\n2e+4\\nte+4\\n5e+3\\n\\n2e+3\\n1e+3\\n5e+2\\n\\n2e+2\\n\\nte+2\\n50\\n\\nchr1_chr1.mcool\\n[Current data resolution: 5.12M],\\n',\n",
|
||
" \"Dy\\n\\nprint(clr.bins() [:] ['weight'].values)\\n\\n[19] Y 0.0s\\n\\n[ nan nan nan ... nan 0.03399118 nan]\\n\",\n",
|
||
" 'Ura OU\\nreverse: 1\\nTotal time for backward call to driver() for mirror index: 00:01:37\\n\\nRenaming GCA_032401905\\nRenaming GCA_032401905\\nRenaming GCA_032401905\\nRenaming GCA_032401905\\nRenaming GCA_032401905\\nRenaming GCA_032401905\\n\\nreal 3m12.647s\\nuser 39m44.879s\\nsys @m14. 663s\\n\\n» 1_ASM3240190v1_genomic. fna.3.bt2.\\n» 1_ASM3240190v1_genomic. fna.4.bt2.\\n» 1_ASM3240190v1_genomic. fna.1.bt2.\\n» 1_ASM3240190v1_genomic. fna.2.bt2.\\n» 1_ASM3240190v1_genomic. fna. rev.1.\\n» 1_ASM3240190v1_genomic. fna. rev.2.\\n\\ntmp\\ntmp\\ntmp\\ntmp\\nbt2.\\nbt2.\\n\\nto GCA_032401905.1_ASM3240190v1_genomic. fna\\nto GCA_032401905.1_ASM3240190v1_genomic. fna\\n\\nto GCA_032401905.1_ASM3240190v1_genomic. fna\\n\\nls -lh /data/proj2/home/students/pst14/ref_gen/sample2/ncbi_dataset/data/GCA_032401905.1/\\n\\ntotal 693M\\n-rw-----—--\\n-rw-rw-r——\\n-rw-rw-r——\\n-rw-rw-r——\\n-rw-rw-r——\\n-rw-rw-r——\\n-rw-rw-r——\\n-rw-rw-r——\\n—rw-----—--\\n\\nPPRPPPRPRPPP\\n\\npst14\\npst14\\npst14\\npst14\\npst14\\npst14\\npst14\\npst14\\npst14\\n\\npst14\\npst14\\npst14\\npst14\\npst14\\npst14\\npst14\\npst14\\npst14\\n\\n240M\\n)\\n39M\\n47K\\n2.6K\\n237M\\n60M\\n119M\\n7.3K\\n\\nJan\\nJan\\nJan\\nJan\\nJan\\nJan\\nJan\\nJan\\nJan\\n\\nGCA_032401905\\nGCA_032401905\\nGCA_032401905\\nGCA_032401905\\nGCA_032401905\\nGCA_032401905\\nGCA_032401905\\nGCA_032401905\\n\\n» 1_ASM3240190v1_genomic. fna\\n\\n» 1_ASM3240190v1_genomic. fna.3.bt2.tmp\\n» 1_ASM3240190v1_genomic. fna.4.bt2. tmp\\n» 1_ASM3240190v1_genomic. fna. amb\\n\\n» 1_ASM3240190v1_genomic. fna. ann\\n\\n» 1_ASM3240190v1_genomic. fna. bwt\\n\\n» 1_ASM3240190v1_genomic. fna. pac\\n\\n» 1_ASM3240190v1_genomic. fna.sa\\nsequence_report.jsonl\\n\\n»3.bt2\\n+4, bt2\\nto GCA_032401905.1_ASM3240190v1_genomic. fna.\\n\\n1.bt2\\n\\n»2.bt2\\ntmp to GCA_@32401905.1_ASM3240190v1_genomic.\\ntmp to GCA_@32401905.1_ASM3240190v1_genomic.\\n\\nfna.rev.1.bt2\\nfna.rev.2.bt2\\n',\n",
|
||
" 'Keys in ‘annotation’: [\\'axisd\\', ‘axisi\\', \\'block@_items\\', ‘block@_values\\', ‘block1_items\\', \\'blocki_values\\', \\'block2_items\\', ‘block2_values\\',\\n\"block3_items\\', \"block3_values\\']\\n\\nDataset: axisd, Shape: (41,), Dtype: |S20\\n\\nDataset: axis1, Shape: (5496,), Dtype: int64\\n\\nDataset: block@_items, Shape: (12,), Dtype: |S20\\n\\nDataset: block@_values, Shape: (5496, 12), Dtype: uints\\n\\nHeatmap of blockO_values\\n\\n° 10\\n0.8\\n- 0.6\\n- 0.4\\n0.2\\n0.0\\n0123 45678 9100\\nDataset: block1_items, Shape: (1,), Dtype: |S7\\nDataset: block1_values, Shape: (5496, 1), Dtype: floate4\\nHeatmap of block1_values\\no- 0.100\\n250 -\\n500 -\\n750 - 0.075\\n1000 -\\n1300. 0.050\\n1750 -\\n2000 - L\\n3900 - 0.025\\n2500 -\\n2750 - - 0.000\\n3000 -\\n3250 -\\n3500 - - 0.025\\n3750 -\\n4000 -\\n4250 - 0.050\\n4500 -\\n4750 - -\\n2330 - 0.075\\n5250 -\\nf -0.100\\n0\\nDataset: block2_items, Shape: (13,), Dtype: |S12\\nDataset: block2_values, Shape: (5496, 13), Dtype: intea\\nHeatmap of block2_values es\\n3.0\\n25\\n-2.0\\n-15\\n-10\\n0.5\\n0.0\\n\\n345 67 8 9 Will 12\\n\\nDataset: block3_items, Shape: (15,), Dtype: |S18\\nDataset: block3_values, Shape: (1,), Dtype: object\\n\\n',\n",
|
||
" '[2]\\n\\n[3]\\n\\n[s2]\\n\\n#Commenting this out to remove the long result. Command run successfully\\n\\n#conda activate aman_prokka\\n\\n#for i in xfilt.vcf; do\\n\\n# vcftools --vcf $i --FILTER-summary --out pass_output_$i\\n#done\\n\\nbash\\n#Commenting this out to remove the long result. Command run successfully\\n#for i in xfail.vcf; do\\n# vcftools --vcf $i --FILTER-summary --out fail_output_$i\\n#done\\n#1s -ltrh\\nbash\\nDy Du Uy\\ncat pass_output*\\nbash\\n\\nFILTER N_VARIANTS N_Ts N_Tv Ts/Tv\\n2510 1207 499 2.41884\\n\\nVCFtools - 0.1.16\\n(C) Adam Auton and Anthony Marcketta 2009\\n\\n',\n",
|
||
" 'Genome vv Tracks ¥ Sample Info v Session v Share Bookmark Save Image Circular View v Help v\\n\\nGV oxford_e...me.fasta _ tig00000002:2,754-6,178 Q 3,425 bp (Select Tracks )( Crosshairs )( Center Line ){ Track Labels ) (—) auu==® +)\\n3 kb j 4 kb j 5 kb j 6 kb\\nLSA A A 8 a\\npo ee ss sss | %\\ndadA_1 IKAOHOFJ_00007 fadR_1\\npac Pi\\ndadA_2 fadR_2\\n\\nigv.org UCSanDiego EEBROAD\\n\\nINSTITUTE\\n',\n",
|
||
" '[19]\\n\\ncd ~\\n\\ncat <<EOF > run_spades.sh\\n\\n#!/bin/bash\\n\\nBASE_DIR=\"\"illegal_logging_trees/fastqc_raw/trimmomatic\"\\n\\nSAMPLES=(\"wood_sample_1\" “wood_sample_2\" \"“wood_sample_3\" “wood_sample_4\" \"wood_sample_5\")\\nTHREADS=8\\n\\nKMERS=\"21, 33,55,77,99\"\\n\\nfor SAMPLE in \"${SAMPLES[@]}\"; do\\necho \"Processing $SAMPLE...\"\\n\\nFORWARD_PAIRED=\"${BASE_DIR}/${SAMPLE}/${SAMPLE}_forward_paired. fq.gz\"\\nREVERSE_PAIRED=\"${BASE_DIR}/${SAMPLE}/${SAMPLE}_reverse_paired. fq.gz\"\\n\\nOUTPUT_DIR=\"$HOME/illegal_logging_trees/fastqc_raw/${SAMPLE}_spades_out\"\\n\\nspades.py -o \"$OUTPUT_DIR\" \\\\\\n-1 \"$FORWARD_PAIRED\" \\\\\\n-2 \"$REVERSE_PAIRED\" \\\\\\n--only-assembler \\\\\\n--careful \\\\\\n-t \"$THREADS\" \\\\\\n-k \"$KMERS\"\\n\\necho \"$SAMPLE done.\"\\ndone\\n\\nEOF\\n\\nbash\\n\\n',\n",
|
||
" \"In [190]: unique(sapply(combined_TCR, function(df) unique(df$sample) ))\\n\\n‘patient3'- ‘patient4'\\n\",\n",
|
||
" \"See below\\n\\nOn Wed, 12 Mar 2025 at 14:58, Aman Shamil Nalakath <aman.nalakath@tum.de> wrote:\\n\\nHi Prof.,\\n\\nSo here are the questions -\\n\\n1. Do | have to do the thesis registration from my end? | am guessing it's from my end and not like the internship one where it was done for me\\n\\nYou will have to register it. But since you are doing an external thesis, you should check with the study coordinator of the program how this is handled. | don't know.\\n\\n2. Is this the chair | should be selecting? This is taken from the thesis portal.\\n\\n| couldn't find the plant epigenomics one in the drop-down menu\\nYou are doing an external thesis, why do you have to select me from the pulldown menu?\\n\\n3. For the preliminary title, can | put the title given in the confirmation letter verbatim i.e ' bioinformatics analysis of 3D genomics and epigenetic data’? Or should | wait til | get a better understanding of the project, and then do accordingly?\\nAs your external thesis advisor what title you should put. | don't know the topic.\\n\\n4. Can | put the issue date to mid/end of April here?\\nAsk the study coordinator.\\n\\n5. Will it be graded only internally or will Géttingen’s supervisor also contribute to grading?\\nAsk the study coordinator.\\n\\n6. While doing the thesis, should | update you on my progress? If yes, how often?\\nIf you do an external thesis, your thesis advisor is external. | don't know.\\n\\n7. Will there be a thesis defense/presentation at the end? Will it be before/after the submission?\\nIn the case of an external thesis, ask the study coordinator.\\n\\nBest,\\n\\nF\\n\\nAll of this is new to me, which is why I'm asking.\\n\\nThank you so much for your time\\n\\nRegards,\\nAman\\n\",\n",
|
||
" 'How so? Answer very briefly\\n\\nSUVR5 deposits H3K9mez2, which recruits CMT3, an enzyme responsible for CHG methylation. This\\nindirect regulation explains why suvr5 mutants show reduced CHG methylation. Since CHH\\nmethylation is partially influenced by H3K9me2, SUVR5 loss also lowers CHH methylation,\\n\\nthough to a lesser extent than met1.\\n',\n",
|
||
" '@ | verify_packages.R @ | seurat_vig1.R @ | scRep.R*\\n\\nWOONDAUBPWNPR\\n\\nYPWWWWWNNNNNNNNNDNPRPRPRPRPRP RRR RB\\nABRBWNPSUVUMANDUBWNESOOANDUAWNESO\\n\\n7:21\\n\\na YA\\nFE EE EAE EAE A\\n\\n__} run071_Sample_Tag_Calls.csv __} run071_VDJ_perCell.csv =\\n\\nShow whitespace\\n\\n## BD Rhapsody Sequence Analysis Pipeline Version 2.2.1\\n\\n## Analysis Date - Fri Mar 07 2025 12:47:22\\n\\n## Libraries - Bioproduct Libraries: Pr416_Pat4TCR_MKDL250001813-1A_22YJ2MLT3_L2_1; Pr416_Pat4WTA_MKDL250001812-1A_22LMGCLT4_L2_1 | ATAC Lil\\n## References - Reference Archive: RhapRef_Human_WTA_2023-@2.tar.gz | AbSeq Reference: None | Supplemental Reference: None | ATAC Predefine\\n## Parameters - Sample Tag Version: flex | Sample Tag Names: | VDJ Version: humanTCR | Putative Cell Calling Data: mRNA | Bioproduct Cell\\n\\nHHHHHHHHHHHHHHHH HEH\\n\\nCell_Index, Sample_Tag , Sample_Name\\n11184, Undetermined , Undetermined\\n146164, Undetermined , Undetermined\\n166368 , Undetermined , Undetermined\\n205244 , Undetermined , Undetermined\\n289640 , Undetermined , Undetermined\\n393118 , Undetermined , Undetermined\\n480269 , Undetermined, Undetermined\\n544629 , Undetermined , Undetermined\\n568064 , Undetermined , Undetermined\\n679531, Undetermined , Undetermined\\n731374 , Undetermined , Undetermined\\n749304 , Undetermined , Undetermined\\n755182 , Undetermined , Undetermined\\n766428 , Undetermined , Undetermined\\n768329 , Undetermined , Undetermined\\n807115 , Undetermined, Undetermined\\n985049 , Undetermined , Undetermined\\n1002101, Undetermined, Undetermined\\n1136612 , Undetermined , Undetermined\\n1185364 , Undetermined , Undetermined\\n1328663 , Undetermined , Undetermined\\n1359810, Undetermined , Undetermined\\n1379377 , Undetermined , Undetermined\\n1487305 , Undetermined , Undetermined\\n1552703 , Undetermined , Undetermined\\n\\n1709118 , Undetermined, Undetermined\\n175537 IIndetermined IIndetermi ned\\n\\nText file\\n',\n",
|
||
" 'v Please select\\nInstituto Universitario de Lisboa (ISCTE IUL)\\nUNIVERSIDADE CATOLICA PORTUGUESA\\nUniversidade de Coimbra\\nUniversidade de Evora\\nUniversidade de Lisboa\\nUniversidade do Porto\\nUniversidade Nova de Lisboa\\n',\n",
|
||
" '5RR21866470_2\\n5RR21866471_2\\nSRR21866472_2\\n5RR21866473_2\\nSRR21866474_2\\nSRR21866475_2\\nSRR21866476_2\\nSRR21866477_2\\nSRR21866478._2\\nSRR21866479_2\\nSRR21866480_2\\nsRR2i8664ei_2\\nSRR21866482_2\\nSRR21866483_2\\nSRR21866484_2\\nsRR21866485,2\\nsRR21866486,2\\n\\nSRR21866487_2\\n\\nFastQC: Sequence Counts\\n\\n36 samples\\n\\nSoM\\n\\n10M\\n\\n15M\\n\\n25M\\n\\n30M\\n\\n35M\\n\\nUnique Reads\\nHM Duplicate Reads\\n',\n",
|
||
" 'Bedpe-like Files\\n\\nBEDPE-like files contain two sets of chromosomal coordinates:\\n\\nchr1@ 74160000 74720000 chr1@ 74165000 74725000\\nchr12 120920000 121640000 chri2 120925000 121645000\\nchr15 86360000 88840000 chr15 86365000 88845000\\n\\nTo view such files in HiGlass, they have to be aggregated so that tiles “tcontain too many\\n\\nvalues and slow down the renderer:\\neee\\n\\nclodius aggregate bedpe \\\\\\n--assembly hg19 \\\\\\n--chri-col 1 --from1-col 2 --tol-col 3 \\\\\\n--chr2-col 4 --from2-col 5 --to2-col 6 \\\\\\n--output-file domains.txt.multires \\\\\\ndomains.txt\\n\\nThis requires the --chr1-col, --from1-col, --to1-col, --chr2-col, --from2-col, --to2-\\ncol parameters to specify which columns in the datafile describe the x-extent and y-extent of\\nthe region.\\n',\n",
|
||
" '@ Mainwindow Omwertoexzt@eoee =) FSF Q S MonNov4 20:49\\n\\neee [Juicebox 2.17.00] Hi-C Map <9>: inter.hic\\nFile View Bookmarks Assembly Dev\\n\\nChromosomes Show Normalization (Obs | Ctrl) Resolution (BP) Color Range\\nA ?\\n1 @ 2 Ge Observed None @ None & rove\\n2.5MB 500KB 100KB 25KB 5KB 1KB 200BP ® a 2\\n138 MB 139 MB 140 MB 141 MB 142 MB 143 MB 144 MB 145 MB 146 MB 147 MB 148 MB\\nSt | | | | | | | | | | | | | | | | | | | | | |\\n= 6\\ng\\n3\\n38-\\n8\\ng\\n8 _ 1:145,670,001-145,680,000\\n3 :107,120,001-107,130,000\\niS} observed value (O) = 0.0\\n—— ° .. . : . . : . : 7 . . : 0 : lexpected value (E) = 0.001\\n_ . so Tae . a a 7 oo So, OVE = 0\\nao ; ;\\nx\\ns_\\nS\\n° |\\nN\\nao\\nx\\ns_\\nS\\n8\\ng\\n3\\n387\\ns\\ng\\n3\\n38-\\ns\\ng\\ns_\\n8\\n~ = 1 Layer 0 @ &- |\\n- .\\ne ) Show Annotation Panel\\n\\n',\n",
|
||
" 'First 5 rows and columns of raw genotype data:\\n\\nCl\\n\\ndddde|\\ntrop\\n\\ndddae\\n\\ndddd|\\nrrr\\n\\nddd\\n\\ndddd|\\nrrr\\n\\na)\\n\\ndadded\\nrrr\\n\\neSeeooe\\n\\ndade|\\n\\neeoo\\n\\n® -1]]]\\n\\n',\n",
|
||
" 'Palmitic acid isotopologs\\n\\nAWWW,\\n\\nKeymer and Gutjahr 2018, COPB *8c\\n\\nThe 16:0 FA isotopologue pattern in the fungus is determined by\\nthe plant\\n\\nCollaboration with\\nWolfgang Eisenreich Lab Lotus WT\\nTU Munich 100\\n\\n80\\n60\\n40 F = fungus\\n20\\n\\nR = colonized root\\n\\nplant and fungus\\n\\n16:0 FA\\n\\nAP HF phe\\n\\nfungus-specific\\n\\nz\\n§\\n$\\ng\\n5\\n5\\ng\\nF]\\n\\n16:1wS FA\\n\\nKeymer, Pimprikar et al (2017), eLife\\n',\n",
|
||
" 'aman@Laptop-von-Aman juicer_hpro % docker build -t juicer_hicpro .\\n\\n[+] Building 2.3s (16/18)\\n\\n=> [internal] load build definition from Dockerfile\\n\\n=> transferring dockerfile: 2.07kB\\n\\n[internal] load metadata for docker.io/nvidia/cuda:11.7.1-devel-ubuntu22.04\\n\\n[auth] nvidia/cuda:pull token for registry-1.docker.io\\n\\n[internal] load .dockerignore\\n\\n=> transferring context: 2B\\n\\nCANCELED [ 1/13] FROM docker.io/nvidia/cuda:11.7.1-devel—ubuntu22.04@sha256 : 18aade8cf@2eede9d4db5d8a8a73d4505bb2322e91cd54e4c601e5ae100ed691\\n=> resolve docker.io/nvidia/cuda:11.7.1-devel-ubuntu22.04@sha256: 18aade8c f02eede9d4db5d8a8a73d4505bb2322e91cd54e4c601e5ae100ed691\\n[internal] load build context\\n\\n=> transferring context: 2B\\n\\nCACHED [ 3/13] RUN locale-gen en_US.UTF-8\\n\\nCACHED [ 4/13] RUN wget https://repo.continuum.io/miniconda/Miniconda3-py37_4.8.2-Linux-x86_64.sh -O /tmp/miniconda.sh && bash /tmp/miniconda.sh -b -p /usr/local/anaconda &&\\nERROR [ 5/13] COPY environment.yml /\\n\\nCACHED [ 6/13] RUN conda env create -f /environment.yml && conda clean -a\\n\\nCACHED [ 7/13] RUN cd /opt && wget https://github.com/nservant/HiC-Pro/archive/master.zip -O hicpro_latest.zip && unzip hicpro_latest.zip && cd HiC-Pro-master &&\\n\\n> CACHED [ 8/13] WORKDIR /opt\\n\\nERROR [ 9/13] COPY install-dependencies.sh /opt/install-dependencies.sh\\n\\n> ERROR [10/13] COPY download-and-run-demo.sh /aidenlab/\\n\\n> ERROR [11/13] COPY download-demo.txt /aidenlab/\\n\\nv\\n\\nCOPY install-dependencies.sh /opt/install-dependencies.sh:\\n\\nCOPY download-and-run-demo.sh /aidenlab/:\\n\\nCOPY download-demo.txt /aidenlab/:\\n\\nCOPY install-dependencies.sh /opt/install-dependencies.sh\\n\\nCOPY download-and-run-demo.sh /aidenlab/\\n\\nCOPY download-demo.txt /aidenlab/\\n\\nRUN chmod +x /opt/install-dependencies.sh && /opt/install-dependencies.sh && \\\\\\nchmod +x /aidenlab/download-and-run-demo.sh && \\\\\\n\\nERROR: failed to solve: failed to compute cache key: failed to calculate checksum of ref mh9tt@9a7urz4xt51386tebzw: :xdsz6f9f1g9z1t18j4ipud5@bf: \"/download-demo.txt\": not found\\n\\nView build details: docker-desktop://dashboard/build/desktop-—linux/desktop—linux/4aiwsé6vrixqnjrre@férxiuzt4\\naman@Laptop-von-Aman juicer_hpro % I\\n\\ndocker:desktop-—linux\\n\\nCACHED [ 2/13] RUN apt-get update && apt-get install -y build-essential wget unzip bzip2 gcc gt+ openjdk-11-jdk git curl make ca-certificates vim\\n\\nrm /tmp/minicon\\n\\nmake configure pref\\n\\nQ.\\n-@s\\n-1s\\n-@s\\n-@s\\n-@s\\n«1s\\n-@s\\n-1s\\n-@s\\n-@s\\n-@s\\n-@s\\n-Os\\n-@s\\n-@s\\n-@s\\n-Os\\n-Os\\n-Os\\n\\nPBVVVWWWVVVVVVVGTGVONO\\n\\nQs\\n',\n",
|
||
" 'Heatmap of Block0O Values\\n0- - 1.0\\n\\ny y y y y y y y y y T T - 0.0\\n',\n",
|
||
" 'In [119]: combined_TCR <— c(combined.TCR_p3, combined. TCR_p4)\\n\\nIn [127]: head(combined. TCR_p3@meta. data)\\nhead (combined. TCR_p4@meta. data)|\\n\\nError in combined.TCR_p3@meta.data: no applicable method for “@° applied to an object of class \"list\"\\nTraceback:\\n\\n1. .handleSimpleError(function (cnd)\\n-f\\n. watcher$capture_plot_and_output()\\n\\ncnd <- sanitize_call(cnd)\\n\\nwatcher$push(cnd)\\n\\nswitch(on_error, continue = invokeRestart(\"eval_continue\"),\\n\\n. stop = invokeRestart(\"eval_stop\"), error = NULL)\\n\\n- }, \"no applicable method for *@ applied to an object of class \\\\\"list\\\\\"\",\\n\\nbase: : quote(combined. TCR_p3@meta. data) )\\n\\nIn [124]: combined_seurat$orig.ident <- ifelse(\\ngrepl(\"-1$\", colnames(combined_seurat)),\\n“patient3\",\\n“patient4\"\\n)\\n\\nIn [125]: head(combined_seurat@meta. data)\\n\\nA data.frame: 6 x 8\\n\\norig.ident nCount_RNA nFeature_RNA percent.mt nCount_SCT nFeature SCT integrated_snn_res.0.5 seurat_clusters\\n\\n<chr> <dbi> <int> <dbi> <dbi> <int> <fct> <fct>\\n\\n4100 patient4 23935, 5941 4.904951 1729 1148 4 4\\n11356 patient4 1299 836 4.080062 1489 835 1 1\\n21277 patient4 2243 1335 6.776638 2090 1332 2 2\\n30102 patient4 2860 1681 5.524476 2205 1654 4 4\\n41586 patient4 1933, 1123 7.604759 1908 1121 1 1\\n41975 patient4 925 607 9.081081 1474 619 3 3\\n\\nIn [126]: table(combined_seurat$orig. ident)\\n\\npatient4\\n3483\\n',\n",
|
||
" '> ValidPairs file from HiC-Pro used\\nas pre-input. 78M entries. Format:\\n\\nchri start1l endl chr2 start2 end2 readID strand1 strand2\\n\\n> .bedpe format (input):\\n\\nchri start1l endl chr2 start2 end2\\n\\n> Output csv\\nformat:\\n\\nchr sl el chr s2 e2 prob interacted\\n\\n> 50000 entry bedpe file - 11249\\nwith interacted score 1\\n\\n',\n",
|
||
" 'Intro\\n\\n¢ Plant Morphogenesis\\n¢ Arabidopsis\\n\\n* Ovule development\\n* Kink & Bend\\n\\nFigure 1: Kink and Bend in Arabidopsis Thaliana\\n',\n",
|
||
" 'e.@e IGV\\n\\nGCA_011696235.1_ASM...\\n\\nCM022235.1 CM022235.1:473,064-473,180 Go ff <« >» @ Fl x fa |\\n\\nSJUEEEEEEEEEEEEE EEE\\n\\n118 bp\\n\\n473,080 bp 473,100 bp 473,120 bp 473,140 bp 473,160 bp 473,18\\n| | | | | | |\\n\\nsample1_bowtie_local_sorted.bar\\n\\nsample1_sorted.bam\\n\\nSequence ™ TTGAAGATgctgaagaaagagaaagatggAGATTTTCTCTGCTTGTTGTAGGCCATGACTCTAGCTAGCTTTCTAGTGAAGAGTGCCTTAAGTATGAAGAATGTTTTGAATGTATGT1\\n',\n",
|
||
" 'Y cut_n_tag/nadine_cut_tag /nadine_cut_tag\\n> Aux_CPI_H3K27me3_results\\n> C_H3K27me3_results\\n> C_H3K27me3_Spi_results\\n> CPILH3K27me3_results\\n\\n',\n",
|
||
" '30 plot1 + plot2\\n31\\n\\n32 zehn_s <- subset(zehn, subset = nFeature_RNA > 200 & nFeature_RNA < 2500 & percent.mt < 5)\\n\\n33 VLinPlot(zehn_s, features = c(\"nFeature_RNA\", \"nCount_RNA\", \"percent.mt\"), ncol = 3)\\n34\\n\\n35 zehn_s <- NormalizeData(zehn_s, normalization.method = \"LogNormalize\", scale. factor\\n36 zehn_s <- NormalizeData(zehn_s)\\n37\\n\\n38 zehn_s <- FindVariableFeatures(zehn_s, selection.method = \"vst\", nfeatures = 1000)\\n39\\n\\n40 # Identify the 10 most highly variable genes\\n\\n41 top1@ <- head(VariableFeatures(zehn_s), 10)\\n\\n42\\n\\n43 # plot variable features with and without Labels\\n\\n44 ploti <- VariableFeaturePlot(zehn_s)\\n\\n45 plot2 <- LabelPoints(plot = plot1, points = top1@, repel = TRUE)\\n46 ploti + plot2\\n\\n47\\n\\n47:1 (Top Level) +\\n\\nConsole’ Terminal Background Jobs\\n\\nR~R4.4.2 - ~/\\n\\nroses cstesiestestinhiietiatehiadesth stated rors pr el ated tie aiid vows\\n\\nWhen using repel, set xnudge and ynudge to @ for optimal results\\n> ploti + plot2\\nError in ‘value[[3L]]QO~:\\n! The RStudio Plots window may be too small to show this patchwork.\\ni Please make the window larger.\\nRun ~ e()*> to see where the error occurred.\\nWarning messages:\\n1: In scale_x_log10Q :\\nlog-10 transformation introduced infinite values.\\n\\n= 10000)\\n\\nR Script >\\n\\na\\n\\n2: Removed 5163 rows containing missing values or values outside the scale range (*geom_point()*).\\n\\n3: In scale_x_log10Q :\\nlog-10 transformation introduced infinite values.\\n\\n4: Removed 5163 rows containing missing values or values outside the scale range (* geom_point()~).\\n\\n> \"nFeature_RNA\", \"nCount_RNA\", \"percent.mt\"\\n',\n",
|
||
" 'ploteca(vsd, intgroup-c(“condition”))\\n\\nres <- results(ddsHTSeq, name-\"condition_0G_vs_ic”)\\nres <- results(ddsHTseq, Samm aie=eNT Soap ees ese ater D2\\n\\nresultsNames (ddsHTSeq)\\nBiocManager : : install (“apegIm\")\\n\\nreslFC <- 1fcshrink(ddsHTSeq, coef=\"condition_0G_vs_16\", type=\"apegim™)\\nrestec\\n',\n",
|
||
" '300 MB\\n\\n200 MB\\n\\n100 MB\\n\\nIN OOL\\n\\naN 007\\n\\nIN 00€\\n',\n",
|
||
" '[28]:\\n\\n# Step 1: Check the type and attributes of ‘gt* OB tvaPFPea\\nprint(\"Type of gt type(gt))\\nprint(\"Attributes of gt:\", dir(gt))\\n\\n# Step 2: Inspect the shape of ‘gt*\\nprint(\"Shape of gt (variants, samples, ploidy):\", gt.shape)\\n\\n# Step 3: View a subset of data\\n# If ‘gt is an allel.GenotypeArray, use ‘.values* to access raw data\\nprint(\"First 5 rows and columns of raw genotype data:\")\\nprint(gt.values[:5, :5, :]) # Adjust slicing as needed\\n\\n# Step 4: Confirm ‘gt’ is biallelic (at most 2 alleles per variant)\\nallele_counts = gt.to_allele_counts()\\nprint(\"Shape of allele counts (variants, alleles):\", allele_counts.shape)\\n\\nType of gt: <class ‘allel.model.ndarray.GenotypeArray\\'>\\n\\nAttributes of gt: abs_\\', \\'_add_\\', \\'_and_\\', \\'_array_\\', \\'_class_\\', \\'_delattr_\\', \\'_dict_\\', \\'_dir_\\', \\'_div_\\', \\'_doc_\\', \\'\\neq__\\', \\'_floordiv_\\', \\'_format_\\', \\'_ge_\\', \\'_getattr__\\', \\'_getattribute_\\', \\'_getitem_\\', \\'_gt__\\', \\'_hash_\\', \\'_init_\\', \\'__init_su\\nbelass_\\', \\'_inv_\\', \\'__invert_\\', \\'_iter_\\', \"_le_\\', \\'_len_\\', \\'_lshift_\\', \\'_lt_\\', \\'_mod_\\', \\'_module_\\', \\'_mut_\\', \\'_ne_\\', \\'\\n_neg_\\', \\'_new_\\', \\'_or_\\', \\'_pos__\\', \\'_pow_\", \\'_reduce_\\', \\'_reduce_ex_\\', \\'_repr_\\', \\'_rshift_\\', \\'_setattr_\\', ‘_setitem_\\', \\'_\\n\\n_sizeof_\\', \\'_str_\\', \\'_sub_\\', \\'__subclasshook \"_truediv_\\', \\'_weakref_\\', \\'_xor_\\', \\'_is_phased\\', \\'_mask\\', \\'_repr_html_\\', \\'_value\\ns\\', ‘caption’, ‘compress’, ‘concatenate’, ‘copy’, ‘count_alleles\\', ‘count_alleles_subpops\\', ‘count_call\\', ‘count_called\\', ‘count_het\\', ‘count\\n_hom\\', ‘count_hom_alt\\', ‘count_hom_ref\\', ‘count_missing\\', ‘display’, ‘displayall\\', \\'fill_masked\\', ‘from_packed\\', \\'from_sparse\\', ‘get_display_\\nitems\\', ‘haploidify_samples\\', ‘is_call\\', \\'is_called\\', ‘is_het\\', \\'is_hom\\', \\'is_hom_alt\\', ‘is_hom_ref\\', ‘is_missing\\', \\'is_phased\\', \\'map_allele\\ns\\', \\'mask\\', \\'n_allele_calls\\', \\'n_calls\\', \\'n_samples\\', \\'n_variants\\', ‘ploidy’, \\'str_items\\', ‘subset’, ‘take’, \\'to_allele_counts\\', \\'to_gt\\', ‘to\\n_haplotypes\\', \\'to_html\\', \\'to_n_alt\\', \\'to_n_ref\\', \\'to_packed\\', \\'to_sparse\\', \\'to_str\\', ‘values\\']\\n\\nShape of gt (variants, samples, ploidy): (477227, 60, 2)\\n\\nFirst 5 rows and columns of raw genotype data:\\n\\n({f 1-1]\\n\\n-1\\n\\n-1\\n\\n-1\\n\\n-1))\\n\\nis\\nis\\nis\\nC1\\n\\n1)\\n-1)\\n1)\\n-1)\\n-1)]\\n\\nRPeReEPB\\n\\n-1)\\n1)\\n-1)\\n1)\\n-1)]\\n\\neorer\\n\\n1)\\n-1)\\n1)\\n-1)\\n-1)]\\n\\nesses\\n\\n-1]\\n\\n-1]\\n\\n-1]\\n\\n-1]\\n\\n-1)))\\n\\nShape of allele counts (variants, alleles): (477227, 60, 3)\\n\\neoeoce\\n\\n',\n",
|
||
" 'workflow_aman\\n\\na i\\na\\nToDo\\n\\nhic hic2cool cool\\n\\nplot\\n\\nmatrix (exported from\\n\\njuicerbon) Python script Plot\\n\\n',\n",
|
||
" '4]\\n\\n# Separate upregulated and downregulated genes\\nupregulated_genes <- deg_df %%\\nfilter(log2FoldChange > 1)\\ndownregulated_genes <- deg_df %>%\\nfilter(log2FoldChange < -1)\\n\\nenrich_up <- enricher(\\ngene = upregulated_genes,\\npvalueCutoff = 0.1,\\npAdjustMethod = \"BH\",\\nminGSSize = 10,\\nmaxGSSize = 500,\\nqvalueCutoff = 0.2,\\nTERM2GENE = tempset)\\n\\nenrich_down <~ enricher{(\\ngene = downregulated_genes,\\npvalueCutoff = 0.1,\\npAdjustMethod = \"BH\",\\nminGSSize = 10,\\nmaxGSSize = 500,\\nqvalueCutoff = 0.2,\\nTERM2GENE = tempset))\\n\\n® 00s\\n\\nError in *filter()*:\\n\\ni In argument: log2FoldChange > 1°.\\nCaused by error:\\n\\n! object \\'log2FoldChange\\' not found\\n\\nTraceback:\\n1. filter(., log2FoldChange > 1)\\n\\n2. filter.data.frame(., log2FoldChange > 1)\\n\\n3. filter_rows(.data, dots, by)\\n\\n4, filter_eval(dots, mask = mask, error_call = error_call, user_env = user_env)\\n\\n5. withCallingHandlers (mask$eval_all_filter(dots, env_filter), error = dplyr_error_handler(dots = dots,\\n\\nmask = mask, bullets = filter_bullets, error_call = error_call),\\n\\nwarning = function(cnd) {\\nlocal_error_context(dots,\\nwarning_handler(cnd)\\n\\n}, ‘dplyr:::signal_filter_one_column_matrix’ = function(e) {\\nwarn_filter_one_column_matrix(env = error_call, user_env = user_env)\\n\\n}, ‘dplyr:::signal_filter_across* = function(e) {\\nwarn_filter_across(env = error_call, user_env = user_env)\\n\\n» mask)\\n\\n}, ‘dplyr:::signal_filter_data_frame’ = function(e) {\\nwarn_filter_data_frame(env = error_call, user_env = user_env)\\n»)\\n6. mask$eval_all_filter(dots, env_filter)\\n7. eval()\\n\\n8. -handleSimpleError(function (cnd)\\n\\n+ }, “object \\'log2FoldChange\\' not found\", base: :quote(NULL))\\n\\n9. h(simpleError(msg, call)\\n\\n10. abort (message, class = error_class, parent = parent, call = error_call)\\n11. signal_abort(cnd, .file)\\n\\n12. signalCondition(cnd)\\n\\nOutput is truncated. View as a scrollable element or open in a text editor. Adjust cell output settings.\\n',\n",
|
||
" 'Thu 18. Sep 11:26\\n\\nObsidian File Edit Insert Format View Window Help 6@ +¥ ¥ © 8 © C]\\n\\neee@efea nan Oo B pnas.202201883 x + », Oo\\nEE ZG te OG xX € papers / pnas.202201883\\n5 codebrain B= Vv Q Qyv 8 of 10\\nV9 RNA: a\\n\\na (ns | ae 0 TT | | ln ila tte\\nee master_thesis Cc\\n\\nt ) H3K27mes Genomic deletion WT: EZH2 — Ezh2\"™*: RNA\\ngs papers RNA binds RNA binding defective\\noe bbad072 PDF PRC2 complex $e e i)\\n\\nbtx802 PDF =\\n\\nte) cancers-15-00466 PDF\\noa Comparison of Normalization... PDF\\n\\needinhibitor\\nemss-67387\\nemss-75815\\n\\nGenome Res.-2021-Wike-981....\\n\\nloopextrusion\\nmb_network\\nnature14222\\nnihms-491130\\nnihms-1574796\\nnihms451535\\nnrg.2016.112\\nPIlIS0092867407001882\\npnas.202201883\\nPRC1\\n$12859-022-04674-2\\n$41422-021-00606-6\\n$41467-019-13423-8\\n\\ntodos\\n\\nfoods\\n\\nHello world\\n\\niVAULT appreciation\\n\\nMetabolic Network Models\\n\\nmotivation letter\\n\\nPasted image 20250821094736\\n\\nPasted image 20250821095008\\n\\nSave up\\n\\niVault\\n\\nPDF\\n\\nPDF\\n\\nPDF\\n\\nPDF\\n\\nPDF\\n\\nPDF\\n\\nPDF\\n\\nPDF\\n\\nPDF\\n\\nPDF\\n\\nPDF\\n\\nPDF\\n\\nPDF\\n\\nPDF\\n\\nPDF\\n\\nPDF\\n\\nPDF\\n\\nPNG\\n\\nPNG\\n\\n= Hak27me3 loops Y A. H8627me9 short ana no orf EZH2 occupancy\\nlong-range cis\\n\\n~ Megabase size VA. within same compartment no orl Hak27me3\\n\\n- Developmental Y H3k27me3\\nregions without EZH2 Loop disappears\\n\\n- Enriched for EZH2\\n\\nA. genomic distance\\n\\n- Cross TADs compartments\\n\\nif no EZH2 occupancy\\n\\nFig. 5. Altered Polycomb binding due to loss of RNA binding by EZH2 alters genome architecture in human iPSCs. (A) Volcano plot of differential EZH2\\nChIP-seq peak signal in WT vs. EZH2°\"4~ iPSCs (26). Log, fold changes and P values (cutoff of absolute value log,FC > 0.5 and Benjamini-Hochberg-adjusted\\nP value < 0.05 for significance) were calculated in DESeq2. Points are labeled by the nearest gene relative to the peak. (B) WT H3K27me3 HiChIP contact\\nmatrix at the NKX2-2/PAX9/FOXA1 locus with H3K27me3 and EZH2 ChIP-seq and 4C-seq shown as depth-normalized read density at NKX2-7 and PAX9 view-\\npoints in WT and EZH2®*4~ iPSCs. Lost contact with FOXAT accompanied by loss of EZH2 binding highlighted with the Benjamini-Hochberg-adjusted P value\\nfor EZH2 ChIP-seq signal (WT/EZH2°\"4~ iPSCs) is shown. (C) Summary of the findings. In WT stem cells, Polycomb-associated H3K27me3 loops connect vast\\ngenomic distances spanning dozens of megabases, crossing TADs and A/B compartments. Deletion of anchor points leads to both local and distal changes\\nof H3K27me spreading in cis, preferentially affecting regions which lack EZH2 occupancy and are located in the same compartment as the original anchor.\\nRNA binding-deficient mutant EZH2 results in loss of looping at loci at sites with reduced EZH2 occupancy.\\n\\nmay be a conserved feature of embryonic and adult tissue stem\\ncells. While prior studies have demonstrated the involvement of\\nPRC1 in maintenance of these long-range contacts (2, 13, 39,\\n40), our results demonstrate alterations in both PRC2 occu-\\npancy and long-range interactions following disruption of\\nRNA-binding mutation by PRC2 component EZH2 in iPSCs\\nand also nominate a role for PRC2 in maintaining long-range\\ninteractions. While the relative contributions of PRC1 and\\nPRC2, as well as cross-talk between these complexes, require\\nfurther study, we acknowledge that both PRC1 and PRC2 play\\nan important role in establishment of genome organization.\\nRecent work by Ngan et al. also nominates the role of PRC2\\nin genome organization and gene silencing and demonstrates\\n\\n8 of 10 https://doi.org/10.1073/pnas.2201883119\\n\\ntelomeres that mediate position effect variegation through the\\nspread of H3K9me3 (48). Because all three of the TF loci we\\ndeleted are not transcribed in ESCs, the effects of locus deletion\\non long-range H3K27me3 deposition are likely due to architec-\\ntural roles of these loci as noncoding regulatory DNA elements.\\n\\nthat homozygous deletion of PRC2-bound silencers can lead to\\ngene expression changes of interacting genes in cis (18). While\\nNgan et al. described local effects within 500 kb, our in vivo\\nresults demonstrate ectopic gain in gene expression in the devel-\\noping limb bud at an interacting gene located 3.8 Mb away fol-\\nlowing heterozygous deletion of a PRC2-bound loop anchor.\\nAdditionally, by leaving one allele intact, we avoid potential\\nloss-of-function effects that could occur in trans. Together,\\nthese results support an important role for PRC2 in long-range\\nchromatin interaction and gene silencing in vivo?\\n\\nOur results also suggest a view of developmental gene loci\\nas architectural elements of the epigenome, nucleating and\\nspreading H3K27me3—a role analogous to centromeres and\\n\\npnas.org\\n\\nHichP. Cells (5 x 10°) were fixed in 2% formaldehyde for 10 min at room\\ntemperature (RT). HiChIP was performed as previously described (27) using antibod-\\nies against H3K27me3 (Millipore Sigma, 07-449) and H3K27ac (Active Motif,\\n39133) with the following optimizations (29): sodium dodecyl sulfate treatment at\\n62°C for 5 min; restriction digest for 15 min; no heat inactivation of restriction\\n\\nOQ backlinks Failed to sync 2 minutes ago |\\n',\n",
|
||
" 'nature protocols\\n\\nExplore content vy Aboutthejournal vy Publish withus v Subscribe\\n\\nnature > nature protocols > protocols > article\\n\\nProtocol | Published: 24 January 2020\\n\\nIdentifying statistically significant chromatin contacts\\nfrom Hi-C data with FitHiC2\\n\\nArya Kaul, Sourya Bhattacharyya & Ferhat Ay 1X)\\n\\nNature Protocols 15, 991-1012 (2020) | Cite this article\\n\\n7039 Accesses | 25 Altmetric | Metrics\\n\\nAbstract\\n\\nFit-Hi-C is a programming application to compute statistical confidence estimates for Hi-C\\ncontact maps to identify significant chromatin contacts. By fitting a monotonically non-\\nincreasing spline, Fit-Hi-C captures the relationship between genomic distance and contact\\nprobability without any parametric assumption. The spline fit together with the correction of\\ncontact probabilities with respect to bin- or locus-specific biases accounts for previously\\ncharacterized covariates impacting Hi-C contact counts. Fit-Hi-C is best applied for the study\\nof mid-range (e.g., 20 kb-2 Mb for human genome) intra-chromosomal contacts; however,\\nwith the latest reimplementation, named FitHiC2, it is possible to perform genome-wide\\nanalysis for high-resolution Hi-C data, including all intra-chromosomal distances and inter-\\nchromosomal contacts. FitHiC2 also offers a merging filter module, which eliminates\\nindirect/bystander interactions, leading to significant reduction in the number of reported\\ncontacts without sacrificing recovery of key loops such as those between convergent CTCF\\nbinding sites. Here, we describe how to apply the FitHiC2 protocol to three use cases: (i) 5-kb\\nresolution Hi-C data of chromosome 5 from GM12878 (a human lymphoblastoid cell line), (ii)\\n40-kb resolution whole-genome Hi-C data from IMR90 (human lung fibroblast), and (iii)\\nbudding yeast whole-genome Hi-C data at a single restriction cut site (EcoRI) resolution. The\\nprocedure takes -12 h with preprocessing when all use cases are run sequentially (~4 h when\\nrun parallel). With the recent improvements in its implementation, FitHiC2 (8 processors and\\n16 GB memory) is also scalable to genome-wide analysis of the highest resolution (1 kb) Hi-C\\ndata available to date (-48 h with 32 GB peak memory). FitHiC2 is available through Bioconda,\\nGitHub and the Python Package Index.\\n',\n",
|
||
" '<\\n2\\n2\\no\\nfom\\n2\\ns\\n\\n® gene-est\\n® fitted\\n® final\\n\\nT T T T T\\nde+01 le+02 1e+03 le+04 le+05\\n\\nmean of normalized counts\\n\\n',\n",
|
||
" 'In [1002]:\\n\\nmerged_TCR <- combineTCR(\\nlist(patient3 = flattened_p3, patient4 = flattened_p4),\\nsamples = c(\"patient3\", \"patient4\"),\\nremoveNA = FALSE,\\nremoveMulti FALSE,\\nfilterMulti = FALSE\\n)\\n\\nError in ‘mutate()~\\n\\ni In argument: *TCR1 = ifelse(...)*~\\nCaused by error:\\n\\n! object \\'chain\\' not found\\nTraceback:\\n\\n1. .makeGenes(cellType = \"T\", out[[i]])\\n\\n2. data2 %>% mutate(TCR1 = ifelse(chain %in% c(\"TRA\", \"TRG\"), str_c(str_replace_na(v_gene),\\nstr_replace_na(j_gene), str_replace_na(c_gene), sep = \".\"),\\nNA)) %>% mutate(TCR2 = ifelse(chain %in% c(\"TRB\", \"TRD\"),\\nstr_c(str_replace_na(v_gene), str_replace_na(d_gene), str_replace_na(j_gene),\\n\\n. str_replace_na(c_gene), sep = \".\"), NA\\n\\n3. mutate(., TCR2 = ifelse(chain %in% c(\"TRB\", \"TRD\"), str_c(str_replace_na(v_gene),\\n\\nstr_replace_na(d_gene), str_replace_na(j_gene), str_replace_na(c_gene),\\n\\n. sep = \".\"), NA\\n4. mutate(., TCR1 = ifelse(chain %in% c(\"TRA\", \"TRG\"), str_c(str_replace_na(v_gene),\\nstr_replace_na(j_gene), str_replace_na(c_gene), sep = \".\"),\\nNA) )\\n\\n5. mutate.data.frame(., TCR1 = ifelse(chain %in% c(\"TRA\", \"TRG\"),\\nstr_c(str_replace_na(v_gene), str_replace_na(j_gene), str_replace_na(c_gene),\\nsep = \".\"), NA\\n» Mutate_cols(.data, dplyr_quosures(...), by)\\n. withCallingHandlers(for (i in seq_along(dots)) {\\npoke_| error_context(dots, i, mask = mask)\\n\\nSe Pe peer 2) ee, eee TT ee eT\\n\\nNO\\n',\n",
|
||
" '* De novo fatty acid biosynthesis by the fungus occurs only\\ninside the root. (Pfeffer et al. 1999; Trepanier et al. 2005)\\n\\n* Fungal genomes lack genes encoding cytosolic fatty acid\\n\\nsynthase subunits!! (Wewer et al. 2014, Tang et al. 2016, Chen et al\\n2018)\\n\\nFungal lipid storage:\\n16:0 TAG\\n16:15 TAG (fungus-specific)\\n',\n",
|
||
" 'AN Tene enginevelry, 5 Py weeds\\n- eZ Anal biota Beat dp\\nate - Tyce Gear bei Oo\\n46, Trtwesip * Feashig UP ES\\n\\nyor\\n\\nSONGS [ab 2 S welts wort\\n\\nCoup peggy\\n\\nRepars — PDO)\\nBrcacvnud,\\nSummer school\\n\\nReding Dalukinnoyy gprs\\nL¥ Pap & Stiles\\nChater — Pronses\\nSaf & Stee Duatle fo Ui?\\nAnping fe Hos. Haig HG\\n',\n",
|
||
" 'GLBE_CHITH 48 TIQAAFPQFVGKDLDAIKGGAEFSTHAGRIVGFLGGVI-- - -- - DDLPNI 91\\nOe eee eee ees etedet\\nGLB7A_CHITH 51 DIQARFPQFAGKDVASIKDTGAFATHAGRIVGFVSELIALIGNESNAPAV 100\\n\\n———>——\\n',\n",
|
||
" \"5.2 3D Coordinate Generation\\n\\nThe spatial coordinates for the ovules were generated using 3D Coordx software, which\\nefficiently mapped the ovule's three-dimensional structure. The software processed the\\nraw image stack and produced a comprehensive set of coordinates representing the\\novule's geometry. This step was essential for downstream analyses such as spatial\\nmodeling and quantitative measurements.\\n\\n@\\n\\n\",\n",
|
||
" '@ Vivaldi File Edit View Bookmarks Mail Tools Window Help BSBeerrzt@ee @) Ff Q &S SunJan5 12:45\\n\\nS Workspaces v < © Display mcool Side by Sic (G) Discarding invalid configu [ff Pastebin.com - #1 paste i: i Tutorial—HiGlassv1.0do i Data Preparation — HiGla HiGlass + ry Ww\\n\\na) — > CD OF @NotSecure 10.162.143.69:8989/app QW ov @ Search Google ’ LL ¢ fo ee *# G C @\\n\\nY Speed Dial yY Imported From... Y Imported From... Online Bewerbung QGIS API Docume... qgis - Trying to pe... New Script- Earth... Pastebin.com-#1.. TargetP 2.0- DTU... https://www.mood... OnePlus 12R revie... Whois “Indian”in.. v A »\\n\\n@ HiGlass About Blog Examples Plugins Docs ©\\n\\nIl oo+ x\\n5e+4 =\\nBackground Color Im Configure Series > a chri_chri.mcool » +X 3er4\\nj >\\nLabel Position b |Track Type ma chr5_chr5.mcool > 4e+4\\nLabel Color > Export Data 6e+3 ©\\n=O\\nLabel Text Opacity P Divide by Lock Value Scale With 3er8\\nLabel Background Opacity P Close Series Unlock Value Scale oe\\nColor map a Replace Series Add Series 3e+2 6\\nZoom limit > Close Track te+2 =\\n\\\\ ransforms > Replace Track SOB U, 1\\nColorbar Position >\\nrack Border Width > as)\\nrack Border Color >\\na)\\nValue Scaling >\\nShow Mouse Position > ea\\n>\\n\\nShow Tooltip\\n\\n€ © ®\\n\\n@\\n\\nchr5_chr5.mcool\\n[Current data resolution: 5.12M],\\n\\n&\\n0 @ 6 O QC) 0 Reset Qu 100% == 12:45\\na .\\n\\na\\n',\n",
|
||
" 'In [425]:\\n\\nIn [426]:\\n\\nfor (i in seq_along(combined.TCR_p3)) {\\nif (!\"chain\" %in% colnames(combined.TCR_p3[[i]])) {\\ncombined. TCR_p3[[il]$chain <- \"TRB\"\\n}\\n}\\n\\nfor (i in seq_along(combined.TCR_p4)) {\\nif (!\"chain\" %in% colnames(combined.TCR_p4[[il])) {\\ncombined. TCR_p4[[i]]$chain <- \"TRB\"\\n}\\n}\\n\\ncombined_TCR <- combineTCR(\\nlist(patient3 = combined.TCR_p3, patient4 = combined.TCR_p4),\\nsamples = c(\"patient3\", \"patient4\"),\\nremoveNA = FALSE,\\nremoveMulti FALSE,\\nfilterMulti = FALSE\\n)\\n\\nError in ‘mutate()*:\\n\\ni In argument: ‘TCR1 = ifelse(...)>.\\nCaused by error:\\n\\n! object \\'chain\\' not found\\nTraceback:\\n',\n",
|
||
" '© crop_simu — aman@unicorn: /mnt/storage3/aman/wdbasejuicer — ssh aman@10.162.143.69 — 181x63\\n\\nWriting footer\\nnBytesV5: 23344806\\nmasterIndexPosition: 1890732904\\n\\n» Finished preprocess\\nDone creating .hic file. Normalization not calculated due to -n flag.\\nTo run normalization, run: java -jar juicer_tools.jar addNorm <hicfile>\\n\\nreal 16m49.35@s\\n\\nuser 48m32.701s\\n\\nsys 1m12.349s\\n\\nPicked up _JAVA_OPTIONS: -Xmx32g -Xms32g\\n\\nWARNING: sun.reflect.Reflection.getCallerClass is not supported. This will impact performance.\\nUsing 1@ CPU thread(s) for primary task\\n\\nCalculating norms for zoom BP_2500000\\nCalculating norms for zoom BP_1000000\\nCalculating norms for zoom BP_500000\\nCalculating norms for zoom BP_250000\\nCalculating norms for zoom BP_100000\\nCalculating norms for zoom BP_50000\\nCalculating norms for zoom BP_25000\\nCalculating norms for zoom BP_10000\\nCalculating norms for zoom BP_5000\\nCalculating norms for zoom BP_2000\\n“Calculating norms for zoom BP_1000f]\\n\\n',\n",
|
||
" 'Superoxide-Dismutase\\n\\nPing-pong mechanism:\\n\\nSOD (Cu) + O.7 —— sov@ + 0,\\n\\nSOD (Cu) + H,O, @\\n\\nCu,Zn SOD\\n\\n——_\\n© Mn SOD :\\n\\nSuperoxid Dismutase (SOD)\\n\\n- 2 Ht\\nsop @™ + 0,°\\n\\n* CuZn-SOD (Cu2+/+) bluegreen, MG 32 kdal (2 subunits with CuZn each), cytosolic\\n* Mn-SOD (Mn3+/2+) pink, MG 40 kdal — is not inhibited by CN-, Diethyldithiocarbamate (Cu2+-chelator) inhibit function in mitochondria\\n* Fe-SOD (Fe3+/2+) in Bacteria or in chlorplasts\\n\\n¢ EC-SOD extracellular, MG 135 kdal (Glycoprotein with 4 subunits, each with CuZn)\\n',\n",
|
||
" 'ls -ltrh /mnt/storage3/aman/jdump\\n\\npwd\\n[18]\\nZhome/aman\\ntotal 553M\\n=rw-rw= aman\\n=rw-rw= aman\\n=rw-rw= aman\\n=rw-rw= aman\\n=rw-rw= aman\\n=rw-rw= aman\\n=rw-rw= aman\\n=rw-rw= aman\\n-rw-rw-r-— 1 aman\\n\\n-rw-rw-r-- 1 aman\\n\\naman\\naman\\naman\\naman\\naman\\naman\\naman\\naman\\naman\\naman\\n\\n90M\\n68M\\n66M\\n71M\\n59M\\n41M\\n46M\\n43M\\n38M\\n37M\\n\\nJan\\nJan\\nJan\\nJan\\nJan\\nJan\\nJan\\nJan\\nJan\\nJan\\n\\neoovovveovvvove\\n\\n19:\\n19:\\n19:\\n19:\\n19:\\n19:\\n19:\\n19:\\n19:\\n19:\\n\\n29\\n29\\n29\\n29\\n29\\n29\\n29\\n29\\n29\\n29\\n\\n1_1_matrix. txt\\n2_2_mat rix.txt\\n3_3_matrix.txt\\n4_4 matrix.txt\\n5_5_matrix.txt\\n6_6_matrix.txt\\n7_T_matrix.txt\\n8_8 _matrix.txt\\n\\n9_9_matrix.txt\\n\\n10_10_matrix.txt\\n\\nbash\\n',\n",
|
||
" '@ Vivaldi File Edit View Bookmarks Mail Tools Window Help 1) ma Beertrrkr@oew® s&s Q ®§S Wed Jan 22 14:28\\n\\nS Workspaces v < Untitled Jupyter Notebook - Term Download File - Vertopal Variant calling - Wiki: pop © ChatGPT GIé Appendices | CITES + ry UW\\n\\na) — > QQ YW 6 cites.org = fl +» & Search Google ’ | Restart Required [) ® Co ee *# G C &\\n\\nY Speed Dial yY Imported From... Y Imported From... Online Bewerbung QGIS API Docume... qgis - Trying to pe... New Script- Earth... Pastebin.com-#1.. TargetP 2.0- DTU... https://www.mood... OnePlus 12R revie... Whois “Indian”in.. v A »\\n\\nFind in Page: ( Diospl 10f 1 4 > xX Match Case\\n\\nDicksoniaspp. ** (Only the\\npopulations of the Americas; no\\nother population is included in the\\nAppendices)\\n\\nDIDIEREACEAE\\nAlluaudias, didiereas\\n\\nDIDIEREACEAE spp. **\\n\\n@OuQeRBOe BD\\n\\nDIOSCOREACEAE\\n\\nElephant’s foot, kniss\\nDioscorea deltoidea**\\n\\nDROSERACEAE ee)\\n\\nVenus’ flytrap\\nDionaea muscipula**\\n\\nEBENACEAE\\n\\nEbonies e\\nDidSpyros spp. *° (Only the\\npopulations of Madagascar; no Ww\\nother population is included in the\\nAppendices)\\n\\nEUPHORBIACEAE\\n\\nSpurges 7\\n\\nEuphorbia spp. °? #* (Succulent\\nspecies only, except the species\\nincluded in Appendix &\\n\\n',\n",
|
||
" 'Higher-order Primary-order\\ndata analysis data analysis\\n\\n4 Mnase- DNase-\\n5C, hi-C raw reads FAIRE- ATAC-seq\\n\\nchimeric\\nalignment\\n\\nsize-selection\\n\\nintra-/inter-\\nchromosome detection accessibility\\ninteractions\\n\\nvisualization\\n',\n",
|
||
" '',\n",
|
||
" 'multige\\n\\nGeneral Statistics\\n\\nFastQC wea 0\\n\\n‘Sequence Counts on] fj\\n\\nFastQC: Sequence Counts\\n‘Sequence Quality Histograms [mm one\\n\\nFastQC: Mean Quality Scores\\nPer Sequence Quality Scores CIN one\\nFastQC: Per Sequence Quality Scores\\nPer Base Sequence Content NEI oa\\n\\ncrassa ruta snes np rt ant\\n\\nPer Sequence GC Content ERIE one\\n\\n',\n",
|
||
" \"Forbidden\\n\\nYou don't have permission to access this resource.\\n\",\n",
|
||
" 'In [62]:\\n\\nsce <— combineExpression(\\n\\ncombined. TCR_p3,\\n\\npatient3_transform,\\n\\ncloneCall = \"CTgene\",\\n\\ngroup.by = \"sample\",\\n\\nproportion = TRUE,\\n\\ncloneSize = c(Single = 1, Small = 5, Medium = 20, Large = 100, Hyperexpanded = 500)\\n)\\n\\nWarning message in combineExpression(combined.TCR_p3, patient3_transform, cloneCall = \"CTgene\", :\\n\\n“< 1% of barcodes match: Ensure the barcodes in the single-cell object match the barcodes in the combined immune re\\nceptor output from scRepertoire. If getting this error, please check https://www.borch.dev/uploads/screpertoire/art\\nicles/faq.”\\n\\nError in “[[<-:\\n! Cannot add more or less meta data without cell names\\nTraceback:\\n\\n1. \\\\[[<-*(**tmp*x*, col.name, value = structure(list(CTgene = c(\"TRAV1-1*01.TRAJ10*01. TRAC_TRBV15*02.TRBD1*01.TRBIJ2—\\n7*01.TRBC2\",\\n\\n» \"TRAV1-1+*01.TRAJ11+*01. TRAC_TRBV12-3*01. TRBD1*01. TRBJ2—7*01.TRBC2\",\\n\\n» \"TRAV1-1+*01.TRAJ11+*01. TRAC_TRBV12-3*01. TRBD1*01. TRBJ2—7*01.TRBC2\",\\n\\n» \"TRAV1-1+01.TRAJ11+*01. TRAC_TRBV14+*02.NA.TRBJ2-1*01.TRBC2\", \"TRAV1-1*01.TRAJ11*01. TRAC_TRBV14*02.NA. TRBJ2—1*01.TR\\nBC2\",\\n\\n» \"TRAV1-1+01.TRAJ11+*01. TRAC_TRBV14+*02.NA.TRBJ2-1*01.TRBC2\", \"TRAV1-1*01.TRAJ11*01. TRAC_TRBV14*02.NA. TRBJ2—1*01.TR\\nBC2\",\\n\\n» \"TRAV1-1+*01.TRAJ11+*01. TRAC_TRBV29-1+01.NA. TRBJ1-1*01.TRBC1\",\\n\\n» \"TRAV1-1+01.TRAJ13*01.NA_TRBV20—-1+05. TRBD1*01.TRBJ1—4+*01.TRBC1\",\\n\\n_ \"TRAV1=1*01_TRA112£01_ TRAC TRRV2A-1405 TRANI*A?- TRR12—2xA1_TRACI\"\\n',\n",
|
||
" 'be import matplotlib\\nprint(matplotlib.__version__)\\n\\nfz] Vv 0.0s\\n\\n3.10.1\\n',\n",
|
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" '@FastQC Report\\n\\nSummary\\n\\nQeasic Statistics\\nOre base sequence quality\\n\\nOber sequence quality scores\\n\\nOber base sequence content\\nQeer sequence GC content\\nOeer base N content\\n\\nQ sequence Length Distribution\\nQseauence Duplication Levels\\nQoverrepresented sequences\\nQadapter Content\\n\\nQrxmmer Content\\n\\nQbasic Statistics\\n\\na\\n\\nFilename\\n\\nFile type\\n\\nEncoding\\n\\nTotal Sequences\\n\\nSequences flagged as poor quality\\nSequence length\\n\\n%GC\\n\\nwood_sample_1_forward_paired. fq.gz\\nConventional base calls\\n\\nSanger / Illumina 1.9\\n\\n180416\\n\\n)\\n\\n30-150\\n\\n36\\n\\n@per base sequence quality\\n\\nQuality scores across all bases (Sanger / Illumina 1.9 encoding)\\n\\n40\\n\\n16\\n\\n14\\n12\\n10\\n\\noN B&O\\n\\n12345 67 8 9 1519\\n\\n30-34 45-49 60-64 75-79 90-94 105-109 120-124 135-139 150\\n',\n",
|
||
" 'Parameter\\n\\nmem\\n\\n* fasta or *.fa\\n\\n* fastq or *.fastq.gz\\n\\nAlignment function\\n\\nset the bwa to use the BWA-MEM algorithm, a fast and accurate alignment\\nalgorithm optimized for sequences in the range of 7Obp to 1Mbp\\n\\nfor split alignment, take the alignment with the smallest coordinate (5’ end) as\\nprimary, the mapq assignment of the primary alignment is calculated\\nindependent of the 3’ alignment\\n\\nskip mate rescue\\nskip pairing; mate rescue performed unless -S also in use\\n\\nThe T flag set the minimum mapping quality of alignments to output, at this\\nstage we want all the alignments to be recorded and thus T is set up to 0, (this\\nwill allow us to gather full stats of the library, at later stage we will filter the\\nalignments by mapping quality\\n\\nnumber of threads, default is 1. Set the numbers of threads to not more than\\nthe number of cores that you have on your machine (If you don’d know the\\nnumber of cores, used the command Iscpu and multiply Thread(s) per core x\\nCore(s) per socket x Socket(s))\\n\\nPath to a reference file, ending with .fa or .fasta, e,g, hg38.fasta\\n\\nPath to two fastq files; path to read 1 fastq file, followed by fastq file of read\\n2 (usually labeled as R1 and R2, respectively). Files can be in their\\ncompressed format (.fastq.gz) or uncompressed (.fastq). In case your library\\nsequence is divided to multiple fastq files, you can use a process substitution\\n< with the cat command (see example below)\\n\\nsam file name to use for output results [stdout]. You can choose to skip the -o\\nflag if you are piping the output to the next command using ‘|’\\n',\n",
|
||
" 'fs]\\n\\ncS\\n\\n# Define a function to compute the signal for each loop (separately for control and EED)\\ndef get_loop_signal(row, clr_i, clr_2):\\n# Fetch signal for anchor 1 in control\\nsignali_ctrl = clr_1.matrix(balance=True) . fetch(f\"{rowl\\'chr1\\']}:{row{\\'start1\\']}—{row{\\'end1\\']}\")\\n# Fetch signal for anchor 2 in control\\nsignal2_ctrl = clr_1.matrix(balance=True) . fetch(f\"{rowl\\'chr2\\']}:{row{\\'start2\\']}—{row{\\'end2\"]}\")\\n\\n# Fetch signal for anchor 1 in EED\\nsignall_eed = clr_2.matrix(balance=True). fetch(f\"{row[\\'chr1\\']}:{row[\\'start1\\']}-{row[\\'end1\\']}\")\\n# Fetch signal for anchor 2 in EED\\nsignal2_eed = clr_2.matrix(balance=True). fetch(f\"{row[\\'chr2\\']}:{row[\\'start2\\']}-{row[\\'end2\\']}\")\\n\\n# Sum signals for control and EED (you can also use mean or another metric)\\nsignal_ctrl = signali_ctrl.sum() + signal2_ctrl.sum()\\nsignal_eed = signali_eed.sum() + signal2_eed.sum()\\n\\nreturn signal_ctrl, signal_eed\\n\\n# Apply the function to both control and EED loop data\\nmega_loop_ctri[{\"ctrl_signal\", \"eed_signal\"]] = mega_loop_ctrl.apply( lambda row: get_loop_signal(row, clr_1, clr_2), axis=1)\\n\\n# If needed, apply for EED data as well\\nmega_loop_rbpi[{\"ctrl_signal\", “eed_signal\"]] = mega_loop_rbp1.apply(lambda row: get_loop_signal(row, clr_1, clr_2), axis=1)\\n\\n# Combine the two DataFrames or choose to analyze them separately\\n# If combining, ensure matching loop identifiers (loop IDs) between control and EED\\nmerged_df = pd.merge(mega_loop_ctrl, mega_loop_rbpi, on=(\"chri\", “start1\", \"endi\", \"chr2\", “start2\", “end2\"], suffixes=(\"_ctrl\",\\n\\need\") )\\n\\n# Calculate log2 fold change\\nmerged_df [\"log2FC\"] = np. log2(merged_df {\"eed_signal\"] / merged_df{\"ctrl_signal\"])\\n\\n# Classify loops based on log2 fold change\\nmerged_df [\"classification\"] = np.where(merged_df[\"log2FC\"] > 0, \"gain\",\\n\\nnp.where(merged_df[\"log2FC\"] < 0, \"loss\", \"stable\"))\\n\\n# Check the result\\nprint(merged_df.head())\\n\\n7m 50.18\\n',\n",
|
||
" 'In\\n\\n[1]:\\n\\nlibrary (Seurat)\\n\\nError in library(Seurat): there is no package called ‘Seurat’\\nTraceback:\\n\\n1. stop(packageNotFoundError(package, lib.loc, sys.call()))\\n',\n",
|
||
" '280\\n281 # Check the first 10 variable features before removal\\n282 VariableFeatures(scRep_example) [1:10]\\n283 Library(Seurat)\\n284 library(scRepertoire)\\n285 # Remove TCR VDJ genes\\n286 scRep_example <- quietTCRgenes(scRep_example)\\n287\\n288 # Check the first 10 variable features after removal\\n289 VariableFeatures(scRep_example) [1:10]\\n290\\n288:1 (Top Level) =\\n\\nConsole’ Terminal Background Jobs\\n\\nR~R4.4.2 - ~/\\n> scRep_example <- quietTCRgenes(scRep_example)\\nError in quietTCRgenes(scRep_example) :\\ncould not find function \"quietTCRgenes\"\\n> library(scRepertoire)\\n> # Remove TCR VDJ genes\\n> scRep_example <- quietTCRgenes(scRep_example)\\nError in quietTCRgenes(scRep_example) :\\ncould not find function \"quietTCRgenes\"\\n> libraryCSeurat)\\n> # Remove TCR VDJ genes\\n> scRep_example <- quietTCRgenes(scRep_example)\\nError in quietTCRgenes(scRep_example) :\\ncould not find function \"quietTCRgenes\"\\n\\nR Script >\\n\\n=f\\n',\n",
|
||
" 'Other papers on Maize leaf HiC\\n\\nArticle | Open access | Published: 14 June 2019\\n\\nChromatin interaction maps reveal genetic regulation\\nfor quantitative traits in maize\\n\\nYong Peng, Dan Xiong, Lun Zhao, Weizhi Ouyang, Shuangqi Wang, Jun Sun, Qing Zhang, Pengpeng\\n\\nGuan, Liang Xie, Wenqiang Li, Guoliang Li@, Jianbing Yan ™ & Xingwang Li4\\n\\nNature Communications 10, Article number: 2632 (2019) | Cite this article\\n\\n> BMC Genomics. 2021 Jan 6;22:23. doi: 10.1186/s12864-020-07324-0 4\\n\\nChromatin loop anchors contain core structural components of the gene\\nexpression machinery in maize\\n\\nStéphane Deschamps »*, John A Crow 4, Nadia Chaidir +, Brooke Peterson-Burch +, Sunil Kumar ?, Haining Lin +,\\n\\nGina Zastrow-Hayes 1, Gregory D May?\\n\\nH3K4me3-mediated loops: 49,766\\nRNAPII-mediated loops: 25,002\\nPromoter-proximal interaction (PPI) loops: 28,875\\nPromoter-distal interaction (PDI) loops: 9,152\\n\\nHiC -\\n\\n17,176 loops detected in replicate 1\\n\\n25,917 loops detected in replicate 2\\n\\n7,917 loops were present in both replicates.\\n\\n67,012 loops detected in HiChIP (H3K4me3) dataset.\\n39,818 loops detected in HiChIP (H3K27me3)\\ndataset.\\n\\n24,218 loops detected in ChIA-PET dataset.\\n\\n',\n",
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" '#Update on the script\\n\\n3. Plot\\nDraw the Chalazal base and the line\\nspecifying the neck of the Nucellus\\n\\n1. Import\\nDrag and drop\\n\\n2. Filter\\n\\nBased on the image\\n\\n\\\\\\n\\nDraws two perpendiculars to each °\\n\\nof these 2 reference lines\\n\\ne\\n\\nResults\\n\\nFie de Fort\\nfares Mean [vir Mex angie\\n7 0 » Oo $593\\n\\nRasut\\n\\n4. Measure\\n\\nSolves for two intersecting perpendiculars.\\n\\nObtains the intersection coordinates.\\n\\nAutomatically measures the kink angle at the intersection.\\n\\natternpt_32\\ntimestamp_15\\n\\n5. Save\\n\\nAutomates saving of ROls for lines, points, and\\nangles in the ROI manager.\\n\\nSaves a screenshot of the final image with the\\nattempt number and timestamp.\\n',\n",
|
||
" '@ Safari File Edit\\n\\nM- <\\n\\nParaphraser\\n\\nx\\n\\nGrammar\\nChecker\\n\\nws\\nAl Detector\\nQ@\\n\\nPlagiarism\\nChecker\\n\\n@\\nAl\\nHumanizer\\n\\nie)\\nAl Chat\\n\\ncs\\n\\nAl lmage\\nGenerator\\n\\nSummarizer\\n\\nMA\\n\\nTranslate\\n\\n99\\n\\nCitation\\nGenerator\\n\\neG\\n\\nQuillBot\\nFlow\\n\\na\\na\\nQuillBot for\\nmacOS\\n\\nView History Bookmarks Window’ Help\\n\\n118\\n\\n© ® ec\\n\\nnH Al Detector - QuillBot Al oe G\\n\\nquillbot.com\\n\\n@&@ GO BO ¥ 3 ©\\n\\nGea ©\\n\\n(4) Perfect your writing in all your favorite apps with QuillBot for macOS\\n\\nAl Detector\\n\\nEnglish French Spanish German Ally\\n\\nfactors. Also some of the differential genes were associated with compartment switches too, W\\nespecially upregulated ones, but these were not statistically significant. It was seen that\\nupregulated genes had more significant structural links as compared to the downregulated\\ngenes. Although the smaller number of downregulated genes may reduce statistical power,\\n\\nthe consistent lack of enrichment across architectural levels suggests that their regulation is\\n\\nless connected to architecture reorganization.\\n\\nTaken together, the transcriptional changes in the PRC2 mutant are linked to regions\\nundergoing architectural reorganisation in the form of loops, weak insulation, and\\ncompartment switches. It was also noted that not all architectural changes connected to\\ntranscriptional changes, and not all DEGs aligned with structural reorganization, implying\\npresence of additional regulatory layers. Chromatin architecture provides a necessary\\nframework for gene regulation, but it may not be sufficient on its own.\\n\\nwith many being linked to upregulated genes. These results indicate that the effect of PRC2\\n\\nloss on transcription is not restricted to newly formed contacts but extends across different\\n\\ncategories of loop stability. Moreover, genes were often contacted by multiple loops, in some\\ncases over ten, pointing to a high degree of regulatory connectivity. The reason for this\\nmultiplicity or redundancy was not explored in terms of log fold change. Some genes had\\n\\noD\\n\\n2,909 Words @ Analysis complete\\n\\nWant your text to sound more authentic?\\n\\nModel Version: v5.7.1\\n\\n2%\\n\\nof text is likely Al ©\\n© QuillBot\\n\\nAl\\n\\nAl-generated @\\nAl-generated & Al-refined @\\nHuman-written & Al-refined @\\n\\nHuman-written @\\n\\n¥Y Understanding your results\\n\\nHuman\\n\\n< Share\\n\\n@ Tue 14. Oct 22:33\\n\\na\\n\\n&} Apps and Extensio...\\n\\n& Download =\\n\\nFeedback\\n\\nD\\n\\nHistory\\n\\noO 22%\\n0%\\n0%\\n\\n98%\\n\\nRefine with Paraphra\\n\\nv\\n\\nae (eG\\n\\n',\n",
|
||
" 'Sequencing technologies have been a driving force in genomics science\\nsince the 70\\'s.\\n\\nAfter reading the article De novo genome assembly: what every biologist\\nshould know (Published: March 2012)\\n\\nLink: https://www.nature.com/articles/nmeth.1935,\\n\\n1. Pick one issue or problem that is mentioned in it. Describe it shortly with\\nyour own words and try to produce a possible solution for it based on what\\nyou have learnt in this course so far (it doesn\\'t matter if your solution is\\n\\nreally doable)\\n\\n2. Share your problem description and solution in TWO places:\\nIn the discussion forum “Impact of sequencing technology\" and submit the\\nsame text also in the task “Impact of sequencing technology”. Please, read in\\n\\nthe discussion forum your peers’ answers,\\n\\nTAL\\nTECH\\n\\nNEXT STEPS |\\n\\nCHECK THE DEADLINES IN MOODLE\\n\\n* Read \"De novo genome assembly: what every biologist should know\"\\n\\n* Do and submit Coursework 3 (based on the lectures so far + reading).\\n\\nOnce you have done all these, you may move on to the \"Week 4, Session 1\"\\n',\n",
|
||
" ') 5,000,000 10,000,000 15,000,000 20,000,000 25,000,000\\n\\nBigWig methylome met1 CG\\n\\nBigWig methylome merged WT All CG\\n\\n',\n",
|
||
" \"(mustache_aman) [papantonis1@gwdu1@1 aman]$ awk '$1 == $9 {print $1}' GE02457_dots_5kb.bedpe | sort | unig -c && wc -1 GE02457_dots_5kb.bedpe\\n842 chri\\n413 chr1e\\n465 chri1\\n442 chr12\\n244 chri3\\n254 chri4\\n234 chris\\n174 chri16\\n248 chr17\\n196 chri8\\n122 chri9\\n817 chr2\\n196 chr2e\\n\\n81 chr21\\n78 chr22\\n731 chr3\\n\\n594 chr4\\n609 chr5\\n631 chr6é\\n478 chr7\\n\\n505 chr8&\\n349 chr9\\n\\n184 chrx\\n\\n8888 GE02457_dots_5kb.bedpe\\n\",\n",
|
||
" 'Table Summary for each 5 samples\\n\\nTotal Mapped Singlet Mate Mapped Mate Mapped to\\nRead Reads Properly — ons to Different Different Chr (mapQ\\nSample s (%) Paired (%) (%) Chr >=5)\\nflagstat_r 377, 371,463 256,930 5,173 84,916 75,474\\neport.txt 652 (98.36% (71.20%) (1.43%\\n) )\\nflagstat_r 386,1 322,660 183,304 34,082 79,438 73,416\\neport_2tx 06 (83.57% (50.41%) (9.37\\nt ) %)\\nflagstat_.r 371,3 371,373 = 371,228 {0} 36 6\\neport_3.tx 73 (100.00 (99.98%) (0.00\\nt %) %)\\nflagstat_r 362, 362,461 358,896 24 1,838 1,282\\neport_4.tx 487 (99.99% (99.36%) (0.01%\\nt ) )\\nflagstat_r 366, 364,120 278,242 1,931 76,370 71,283\\neport_5.tx 247 (99.42% (77.50%) (0.54\\n\\nt\\n\\n)\\n\\n%)\\n',\n",
|
||
" \"In [31]:\\n\\nfrom allel.stats.decomposition import GenotypePCA\\nimport matplotlib.pyplot as plt\\n\\n# Initialize PCA model (no scaler for haploid data)\\nmodel = GenotypePCA(n_components=10, scaler=None)\\n\\n# Fit and transform haplotype data\\nmodel. fit (X)\\ncoords = model.transform(X)\\n\\n# &@ Compute variance explained (correct attribute)\\nexplained_variance = model._variance_explained\\n\\nBS\\n\\n@ Scatter plot of first two PCs with variance explained\\nt.figure(figsize=(7, 5))\\nt.scatter(coords[:, @], coords[:, 1], alpha=0.7)\\n\\np\\np\\n# @ Add variance explained to axis labels\\nplt.xlabel(f'PC1 ({explained_variance[0]*100:.2f}%) ')\\nplt.ylabel(f'PC2 ({explained_variance[1]*100:.2f}%) ')\\n#\\np\\n#\\np\\n\\nAdd title\\nt.title('PCA of Haploid Data')\\n\\nShow plot\\nt.show()\\n\\nAttributeError Traceback (most recent call last)\\nInput In [31], in <cell line: 12>()\\n\\n9 coords = model.transform(X)\\n\\n11 # Compute variance explained (correct attribute)\\n---> 12 explained_variance =\\n\\n14 # Scatter plot of first two PCs with variance explained\\n\\n15 plt.figure(figsize=(7, 5))\\n\\nAttributeError: 'GenotypePCA' object has no attribute '_variance_explained'\\n\",\n",
|
||
" 'This slide explains how rapid fluctuations in population size influence the effective population size\\n(N-), a key parameter in population genetics. Unlike the arithmetic mean, N; reflects the\\nharmonic mean of population sizes over time, which is disproportionately affected by periods of\\nsmall population size. For example, if the population fluctuates between N and N/4, the effective\\npopulation size becomes NV, = 2N, significantly smaller than the actual average population size.\\nThis occurs because smaller populations have higher probabilities of coalescence, reducing\\ngenetic diversity. For general periods (T’), Nz is calculated as the harmonic mean of population\\nsizes over T’ generations, emphasizing that even brief reductions in population size can greatly\\nlower N,. The slide highlights that these fluctuations shape genetic variation by reducing Ne,\\n\\naffecting coalescence rates and increasing the impact of genetic drift.\\n',\n",
|
||
" 'ege20000000T0R\\n\\nuild_matrices.log\\n\\nhr10_chr1@.matrix\\nhr1@_chr1@_abs.bed\\nhr1@_chri@_ord.bed\\nhri_chri.matrix\\nhri_chr1@.matrix\\nhri_chri@_abs.bed\\nhri_chri@_ord.bed\\nhri_chri_abs.bed\\nhri_chri_ord.bed\\n\\nuild_matrix_automating.sh\\n\\nchr2_chr8_abs.bed\\nchr2_chr8_ord.bed\\nchr2_chr9.matrix\\nchr2_chr9_abs.bed\\nchr2_chr9_ord.bed\\nchr3_chri@.matrix\\nchr3_chr1@_abs.bed\\nchr3_chr1@_ord.bed\\nchr3_chr3.matrix\\nchr3_chr3_abs.bed\\nchr3_chr3_ord.bed\\n\\nbase) aman@unicorn:~$ ls /mnt/storage3/aman/20000/interchr_matrix\\n\\nchr5_chr6_abs.bed\\nchr5_chr6_ord.bed\\nchr5_chr7.matrix\\n\\nchr5_chr7_abs.bed\\nchr5_chr7_ord.bed\\nchr5_chr8.matrix\\n\\nchr5_chr8_abs.bed\\nchr5_chr8_ord.bed\\nchr5_chr9.matrix\\n\\nchr5_chr9_abs.bed\\nchr5_chr9_ord.bed\\n',\n",
|
||
" 'Work ’ Proton Mail: Sign-in la Launch Meeting - Zoom li app.diffgram.com 0% Mail - aman.nalakath@tum Bg SoleMOVE / My data =a Incoming EuroTeQ students +\\n\\nOo < > CG VW & saas.solenovo.fi/solemove/disp/_/en/mydata/edi/edi/clr Wy | Q& Search Google ’ ‘| ¢ & > OW e QO F ry\\n\\n| ’ Speed Dial ~ Imported From...» Imported From... Online Bewerbung QGIS API Docume... qgis - Trying to pe... New Script - Earth... Pastebin.com - #1... TargetP 2.0 - DTU...\\n\\n» |\\n\\ni\\n\\na Tallinn University of Technology My data\\nApplication form for incoming\\nstudent mobility (Save —_ f} Change password\\n@\\nApplication form for short\\nxz student mobility\\nClient TalTech\\nNalakath Aman Shamil Username* dmuncnalak7030\\nName* .\\n() Nalakath Aman Shamil\\nm sical aman.nalakath@tum.de\\n© Last login 15.08.2024 22:45:44\\nLast edited by 15.08.2024 22:45:50 / Nalakath Aman Shamil\\n(Save —_ f} Change password\\n\\n© 2010-2024 Solenovo Oy\\n\\n= Oo——_—_—_—_— 100% 01:20\\n',\n",
|
||
" 'Mean Matton Lat\\n\\nMoan Methylation Levels - CG Contaxt\\n\\nMoan Metryaton vet\\n\\n‘Mean Methyation Lo\\n\\n= 0G Context\\n\\na a Hl\\n\\né -\\n\\nFe\\n\\n',\n",
|
||
" '@ Juicebox @ (®o gw@obor¥rz@ee Tue Dec 3 12:58\\n\\nee@ [Juicebox 2.17.00] Hi-C Map <9>: inter.hic\\n\\nFile View Bookmarks Assembly Dev\\nChromosomes Show Normalization (Obs | Ctrl) Resolution (BP) Color Range\\n\\nObserved 5 ¢ reyrt rt rrr tnt\\n2.5MB 500KB 100KB 25KB 5KB 1KB 200BP\\n\\nInter Balanced++\\nGenome-wide Balanced++\\nCoverage\\n\\n~- ¥ Coverage (Sqrt)\\n\\né Balanced++\\n\\n1:216,500,001-217,000,000\\n3:500,001-1,000,000\\n\\na observed value (O) = 3.875\\nS ae : expected value (E) = 2.663\\n\\n2 : : : : Mi O/E = 1.455\\n\\nLayerO <> | & |\\n\\nShow Annotation Panel\\n\\n',\n",
|
||
" \"In [50]:\\n\\nimport matplotlib.pyplot as plt\\n\\n# Compute variance explained\\nexplained_variance = model.values_ / model. values_.sum()\\n\\n# Scatter plot of first two PCs\\n\\nplt.scatter(coords[:, @], coords[:, 1], alpha=0.7)\\nplt.xlabel(f'PC1 ({explained_variance[0]*100:.2f}%)')\\nplt.ylabel(f'PC2 ({explained_variance[1]*100:.2f}%)')\\nplt.title('PCA of Haploid Data')\\n\\nplt.show()\\n\\nAttributeError Traceback (most recent call last)\\nInput In [5@], in <cell line: 4>()\\n\\n1 import matplotlib.pyplot as plt\\n\\n3 # Compute variance explained\\n----> 4 explained_variance = fiodelsValUesl) / model.values_.sum()\\n\\n6 # Scatter plot of first two PCs\\n\\n7 plt.scatter(coords[:, @], coords[:, 1], alpha=0.7)\\n\\nAttributeError: 'GenotypePCA' object has no attribute 'values_'\\n\",\n",
|
||
" '‘oUala Cel (urgor IS Freguiatea DY a\\n\\nABA complex network of interacting second\\nSe messengers, pH, membrane potential,\\nprotein phosphorylation, ion channel\\nNOE 10 activity — and more!!\\n\\n',\n",
|
||
" '@ © @ ~~ aman—amnala@base:~/bioinfo24/RNASEQ/alignment — ssh amnala@base.hpc.taltech.ee —...\\n\\nstar_out_728910.txt\\nstar_out_728911.txt\\n[amnala@base alignment]$ ls sorted.GLDS*\\n\\nsorted\\nam\\n\\nsorted\\n«bai\\nsorted\\n\\nsorted\\n«bai\\nsorted\\n\\nsorted\\n«bai\\nsorted\\n\\nsorted\\n«bai\\nsorted\\n\\nsorted\\n«bai\\nsorted\\n\\nsorted\\n«bai\\nsorted\\n«bam\\nsorted\\n\\n-Dam.\\n\\n.GLDS-251_rna—seq_13JUN2017HiSeq_Run_Samp\\n.GLDS-251_rna—seq_13JUN2017HiSeq_Run_Samp\\n.GLDS-251_rna—seq_13JUN2017HiSeq_Run_Samp\\n.GLDS-251_rna—seq_13JUN2017HiSeq_Run_Samp\\n.GLDS-251_rna—seq_13JUN2017HiSeq_Run_Samp\\n.GLDS-251_rna—seq_13JUN2017HiSeq_Run_Samp\\n.GLDS-251_rna—seq_13JUN2017HiSeq_Run_Samp\\n.GLDS-251_rna—seq_13JUN2017HiSeq_Run_Samp\\n.GLDS-251_rna—seq_13JUN2017HiSeq_Run_Samp\\n.GLDS-251_rna—seq_13JUN2017HiSeq_Run_Samp\\n.GLDS-251_rna—seq_13JUN2017HiSeq_Run_Samp\\n.GLDS-251_rna—seq_13JUN2017HiSeq_Run_Samp\\n.GLDS-251_rna—seq_13JUN2017HiSeq_Run_Samp\\n\\n.GLDS-251_rna—seq_13JUN2017HiSeq_Run_Samp\\nbai\\n\\n[amnala@base alignment]$\\n\\ne_111_UMISS_Hoeksema_ATCACG_L@@1_R1_001_1M.\\ne_111_UMISS_Hoeksema_ATCACG_L@@1_R1_001_1M.\\ne_114_UMISS_Hoeksema_CGATGT_L@@1_R1_001_1M.\\ne_114_UMISS_Hoeksema_CGATGT_L@@1_R1_001_1M.\\ne_116_UMISS_Hoeksema_TTAGGC_L@@1_R1_001_1M.\\ne_116_UMISS_Hoeksema_TTAGGC_L@@1_R1_001_1M.\\ne_12@_UMISS_Hoeksema_TGACCA_L@0@1_R1_001_1M.\\ne_120_UMISS_Hoeksema_TGACCA_L@@1_R1_001_1M.\\ne_175_UMISS_Hoeksema_AGTTCC_L@@2_R1_001_1M.\\ne_175_UMISS_Hoeksema_AGTTCC_L@@2_R1_001_1M.\\ne_179_UMISS_Hoeksema_CCGTCC_L@@3_R1_001_1M.\\n\\ne_179_UMISS_Hoeksema_CCGTCC_L@@3_R1_001_1M.\\n\\nfastq.\\nfastq.\\nfastq.\\nfastq.\\nfastq.\\nfastq.\\nfastq.\\nfastq.\\nfastq.\\nfastq.\\nfastq.\\n\\nfastq.\\n\\nDd\\n\\ne_235_239_UMISS_Hoeksema_GTTTCG_L@03_R1_001_1M. fas\\n\\ne_235_239_UMISS_Hoeksema_GTTTCG_L@@3_R1_001_1M. fas\\n',\n",
|
||
" 'Sample, Cluster\\nFG_001,1\\nFG_002,1\\nFG_003,1\\nFG_004,2\\nFG_005,0\\nFG_006,2\\nFG_007,2\\nFG_008,2\\nFG_009,2\\n',\n",
|
||
" 'for i in samplex/ncbi_dataset/data/GCx/x1_genomic.fna; do\\nbowtie2-build \"$i\" \"$i\"\\ndone 9 ©\\n\\nSettings:\\nOutput files: \"sample*/ncbi_dataset/data/GC*/*1_genomic. fna.*.bt2\"\\nLine rate: 6 (line is 64 bytes)\\n\\nLines per side: 1 (side is 64 bytes)\\nOffset rate: 4 (one in 16)\\n\\nFTable chars: 10\\n\\nStrings: unpacked\\n\\nMax bucket size: default\\n\\nMax bucket size, sqrt multiplier: default\\nMax bucket size, len divisor: 4\\nDifference-cover sample period: 1024\\nEndianness: little\\n\\nActual local endianness: little\\n\\nSanity checking: disabled\\n\\nAssertions: disabled\\n\\nRandom seed: 0\\n\\nSizeofs: void*:8, int:4, long:8, size_t:8\\n\\nInput files DNA, FASTA:\\nsamp le*x/ncbi_dataset/data/GC*/*1_genomic. fna\\n\\nError: could not open samplex/ncbi_dataset/data/GC*/*1_genomic. fna\\n\\nTotal time for call to driver() for forward index: 00:00:00\\n\\nError: Encountered internal Bowtie 2 exception (#1)\\n\\nCommand: /data/proj2/teaching/NGS_course/Softwares/bowtie2-2.4.5-lLinux-x86_64/bowtie2-build-s -—-wrapper basic-@ sample*/ncbi_dataset/data/GC\\n\\n*/*1_genomic.fna samplex/ncbi_dataset/data/GC*/*1_genomic. fna\\n\\nHal\\n',\n",
|
||
" '',\n",
|
||
" 'Hierarchical Clustering - Heatmap\\n\\n‘Sample Distance Matrix\\n\\n2\\n\\nune waeoggn sess f=\\n°\\n\\n‘under wateriogging stress\\n\\n‘under control\\n\\nunder control\\n\\nunder contro\\n\\n‘under control\\n\\n‘under control\\n\\nunder wateriogging stress\\n\\nLunder waterlogging stress\\n\\nunder waterogging stress\\n\\nunder waterogging stress\\n\\nss9ns Bus650ua1en pun\\n\\nss9ns Bus6Soyaien pun\\n\\nsans Gus6Goyaien spun\\n\\n_.\\n\\nton\\n\\ne208 6u6SousieM pun\\n\\nonus sapun\\n\\nssons Bu6Souo1em pun\\n\\nssons 6u6Sous1en pun\\n\\nsean 6us6Soueien pun\\n\\nsons BubSouaien spun\\n\\nss2n Bus6Soysien spun\\n\\nsean Sis6Souswm pun\\n\\ne208 Si6Sousim pun\\n\\ne208 6us65ouawn pun\\n\\nunder waterogging stress\\n\\nunder wateiogging stress\\n\\nunder watelogging stress\\n\\nunder waterogging stress\\n',\n",
|
||
" 'In [313]:\\n\\nIn [314]:\\n\\ncombined. TCR_p3 <- combineTCR(\\n\\ncontig_list\\nsamples = c\\n\\n(\"P3_S1\"),\\n\\nremoveNA = FALSE,\\n\\nremoveMulti\\nfilterMulti\\n\\n)\\n\\n# output = a list of contig data frames that will be reduced to the reads associated with a\\n\\nhead (combined\\n\\nA data.frame: 6 x 11\\n\\nbarcode\\n\\n<chr>\\n\\n4 1002101\\n\\n3 10279593\\n\\n5 10300542\\n\\n6 10311423\\n\\n8 10627379\\n\\n10 10742731\\n\\nVisualization\\n\\n= FALSE,\\n= FALSE\\n\\n-TCRE[1]])\\n\\nTCR1\\n\\n<chr>\\n\\nTRAV19°01.TRAJ27*01.TRAC\\n\\nTRAV13-2*01.TRAJ26*01.TRAC\\n\\nTRAV21*02.TRAJ34*01.TRAC\\n\\nTRAV21*01.TRAJ15*01.TRAC\\n\\nTRAV29/DV5*01.TRAJ47*01.TRAC\\n\\nTRAV9-2°01.TRAJ43*01.TRAC\\n\\ncdr3_aat\\n\\n<chr>\\n\\nCAPTPMQANQP\\n\\nedr3_nt1\\n\\n<chr>\\n\\nTGTGCCCCAACACCAATGCAGGCAAATCAACCTTT\\n\\nCAENTRGRRSEFCL TGTGCAGAGAATACGAGGGGTAGGAGGTCAGAAT ‘GTCTTT.\\n\\nCAAYNTDKLIF\\n\\nCAVVNQAGTALIF\\n\\nCAASRYGNKLVF\\n\\nCALGEGDMRF\\n\\n# total or relative numbers of unique clones.\\nclonalQuant (combined. TCR_p3,\\nstrict\",\\nboth\",\\nTRUE)\\n\\nscale =\\n\\nPercent of Unique Clones\\n\\n25:\\n\\n‘Samples\\n\\nPast\\nNA\\n\\nTGTGCTGCTTATAACACCGACAAGCTCATCTTT\\n\\nTGTGCTGTAGTTAACCAGGCAGGAACTGCTCTGATCTTT\\n\\nTGTGCAGCAAGCAGATATGGAAACAAACTGGTCTTT\\n\\nTGTGCTCTGGGGGAGGGTGACATGCGCTIT\\n\\nsingle cell barcode. It\\n\\nTCR2\\n\\n<chr>\\n\\nTRBV11-2*01.NA.TRBJ1-\\n1°01.TRBC1\\n\\nTRBV6-\\n2*01.TRBD1*01.TRBJ1-\\n1*01.TRBC1\\n\\nNA\\n\\nTRBV11-\\n2*01.TRBD2*02.TRBJ1-\\n2°01.TRBC1\\n\\nTRBV27*01.TRBD2*02.TRBJ2-\\n2°01.TRBC2\\n\\nTRBV11-\\n2*01.TRBD1*01.TRBJ2-\\n7*01.TRBC2\\n\\nCAS\\n\\nCc\\n\\nCASI\\n',\n",
|
||
" '(62):\\n\\nimport pandas as pd H@tvAiaFPea\\ngenotypes = allel. GenotypeArray(callset [\\'calldata/GT\"])\\nprint (genotypes.shape) # Should be (n_variants, n_samples, ploidy)\\n\\n= pd.DataFrame(genotypes)\\nprint(\"\\\\nFirst few rows of gn as a DataFrame:\\nprint (df.head()) C73)\\n\\n(477227, 60, 2)\\n\\nCaucEraor Traceback (cas recent call last)\\nInput . [62], in <cell line: 4>()\\n2 genotypes = allel.GenotypeArray(callset [\\'calldata/GT\\'])\\n\\n3 print(genotypes.shape) # Should be (n_variants, n_samples, ploidy)\\n—---> 4 df paltstserawe (GenoEypes]\\n\\n5 print(\"\\\\nFirst few rows of gn as a DataFrame\\n6 print(df.head())\\n\\nFile ~/.conda/envs/test_allel_env/1ib/python3.9/site-packages/pandas/core/frame.py:867, in DataFrame.__init_(self, data, index, columns, dty\\npe, copy)\\n859 mgr = arrays_to_mgr(\\n860 arrays,\\n861 columns,\\n(aes)\\n864 typ=manager,\\n865 )\\n866 else:\\n--> 867 mgr =\\n868\\n869\\n870\\n871\\n872\\n873\\n874\\n875 else:\\n876 mgr = dict_to_mgr(\\n877 0,\\n878 index,\\n(aa0)\\n881 typ=manager,\\n882 )\\n\\nFile ~/.conda/envs/test_allel_env/Lib/python3.9/site-packages/pandas/core/internals/construction.py:314, in ndarray_to_mgr(values, index, col\\numns, dtype, copy, typ)\\n\\n308 = _copy = (\\n309 copy_on_sanitize\\n310 if (dtype is None or astype_is_view(values.dtype, dtype))\\n311 else False\\n312 )\\n313. values = np.array(values, copy=_copy)\\n-> 314 values\\n316 else:\\n317s # by definition an array here\\n318 # the dtypes will be coerced to a single dtype\\n319 values = _prep_ndarraylike(values, copy=copy_on_sanitize)\\n\\nFile ~/.conda/envs/test_allel_env/Lib/python3.9/site-packages/pandas/core/internals/construction.py:592, in _ensure_2d(values)\\n\\n590 values = values. reshape((values.shape[0], 1))\\n591 elif values.ndim != 2:\\n--> 592 raise ValueError(f\"Must pass 2-d input. shape={values. shape}\")\\n\\n593 return values\\n\\nValueError: Must pass 2-d input. shape=(477227, 60, 2)\\n\\n',\n",
|
||
" 'ne...@broadinstitute.org Jan 18, 2019, 8:16:32PM y+ roN\\nto AS, 3D Genomics\\n\\nHello,\\nYou can just run HiCCUPS or Arrowhead on the hic file using the latest jar: https://github.com/aidenlab/juicer/wiki/Download\\nThere is extensive documentation here: https://github.com/aidenlab/juicer/wiki/CPU-HiCCUPS\\n\\nPlease note: 300 million reads is not enough to reliably call loops. We also do not recommend domain calling at this depth. The ENCODE standard for loop\\ncalling is 2 billion reads.\\n\\nBest\\nNeva\\n\\nYou received this message because you are subscribed to the Google Groups \"3D Genomics\" group.\\n\\nTo unsubscribe from this group and stop receiving emails from it, send an email to 3d-genomics...@googlegroups.com.\\nTo view this discussion on the web visit https://groups.google.com/d/msgid/3d-genomics/761 6da19-9387-4c46-99d6-\\nef852e2b0170%40googlegroups.com.\\n\\nFor more options, visit https://groups.google.com/d/optout.\\n\\nNeva Cherniavsky Durand, Ph.D.\\nStaff Scientist, Aiden Lab\\nwww.aidenlab.org\\n',\n",
|
||
" 'toluene123/rstudio:v0.1\\n\\nINDEX DIGEST sha256:006134f99654557919795a7bc53ff602364074e6d16764673ad270c742b4a9b6 oO\\n\\nOS/ARCH COMPRESSED SIZE LAST PUSHED TYPE MANIFEST DIGEST\\n\\nlinux/arm64 725.62 MB 10 minutes by toluene123 Image sha256:d4af4ff49... oO\\n',\n",
|
||
" 'In [588]: DimPlot(integrated_seurat, reduction = “umap\", group.by = “orig. ident\")\\n\\norig.ident\\n\\n© patients\\n© patient\\n\\n',\n",
|
||
" '[pst14@node25 ~]$ head inp struct.ped\\n\\nFG_001\\n\\n0\\n0\\n0\\n0\\n0\\n0\\n0\\n0\\n0\\n0\\n\\nFG_001\\nremit tI Arner .1e\\n\\nFG 002\\n\\nFG 002\\n\\nFG 003 FG 003\\n\\nFG 004\\n\\nFG 004\\n\\nFG 005 FG 005\\n\\nFG_006\\n\\nFG_006\\n\\nFG_007\\n\\nFG_007\\n\\nFG_008\\n\\nFG_008\\n\\nFG_009\\n\\nFG_009\\n\\nFG_010\\n\\nFG_010\\n',\n",
|
||
" 'Mean Methylation Level\\n\\noo:\\n\\nMean Methylation Levels - CG Context\\n\\nFile\\n\\n0.100\\n\\nMean Methylation Level\\n2 g\\n\\ni\\n\\n0.000.\\n\\nMean Methylation Levels - CHG Context\\n\\nFile\\n\\n008\\n\\n0.04\\n\\n8\\n\\ng\\n\\nMean Methylation Level\\n\\noot\\n\\n0.00.\\n\\nMean Methylation Levels - CHH Context\\n\\nFile\\n\\n',\n",
|
||
" 'In [16]: print(hic.getGenomeID())\\nprint (hic.getResolutions())\\n\\n/home/aman/basejuicer/references/chrom.sizes\\n[2500000, 1000000, 500000, 250000, 100000, 50000, 25000, 10000, 5000, 2000, 1000, 500, 200, 100]\\n\\nIn [17]: for chrom in hic.getChromosomes():\\nprint(chrom.name, chrom. length)\\n\\nALL 2135083\\nNC_@24459.2 307041717\\nNC_@24460.2 244442276\\nNC_@24461.2 235667834\\nNC_@24462.2 246994605\\nNC_@24463.2 223902240\\nNC_@24464.2 174033170\\nNC_@24465.2 182381542\\nNC_@24466.2 181122637\\nNC_@24467.2 159769782\\nNC_@24468.2 150982314\\nNW_@17972002.1 50531\\nNW_@17972003.1 60109\\nNW_@17972004.1 59657\\nNW_@17972005.1 66261\\nNW_@17972006.1 83265\\nNW_@17972007.1 255484\\nNW_@17972008.1 71433\\nNW_@17972009.1 64470\\n\\nIn [18]: chromosomes = hic.getChromosomes()\\nprint(Len(chromosomes) )\\n\\n268\\n\\nIn [20]: matrix_object_chr4 = hic.getMatrixZoomData( \\'NW_@17972091.1\\', \\'NW_017972091.1\\', \"observed\", \"KR\", \"BP\", 20000)\\nFile did not contain KR normalization vectors for one or both chromosomes at 20000 BP\\n\\nMemoryError Traceback (most recent call last)\\nCell In[20], line 1\\n\\n> 1 matrix_object_chr4 = hic.getMatrixZoomData( \\'Nbl_017972091.1\\', ‘NW_017972091.1\", “observed”, \"KR\", \"BP\", 2000\\n\\nMemoryError: std::bad_alloc\\n',\n",
|
||
" \"nature PROTOCOL\\n\\nprotocols\\n\\nhttps://doi.org/10.1038/s41596-019-0273-0\\n\\nIdentifying statistically significant chromatin\\ncontacts from Hi-C data with FitHiC2\\n\\nArya Kaul'*°, Sourya Bhattacharyya®° and Ferhat Ay©?>*\\n\\nFit-Hi-C is a programming application to compute statistical confidence estimates for Hi-C contact maps to identify\\nsignificant chromatin contacts. By fitting a monotonically non-increasing spline, Fit-Hi-C captures the relationship\\nbetween genomic distance and contact probability without any parametric assumption. The spline fit together with the\\ncorrection of contact probabilities with respect to bin- or locus-specific biases accounts for previously characterized\\ncovariates impacting Hi-C contact counts. Fit-Hi-C is best applied for the study of mid-range (e.g., 20 kb-2 Mb for human\\ngenome) intra-chromosomal contacts; however, with the latest reimplementation, named FitHiC2, it is possible to perform\\ngenome-wide analysis for high-resolution Hi-C data, including all intra-chromosomal distances and inter-chromosomal\\ncontacts. FitHiC2 also offers a merging filter module, which eliminates indirect/bystander interactions, leading to\\nsignificant reduction in the number of reported contacts without sacrificing recovery of key loops such as those between\\nconvergent CTCF binding sites. Here, we describe how to apply the FitHiC2 protocol to three use cases: (i) 5-kb resolution\\nHi-C data of chromosome 5 from GM12878 (a human lymphoblastoid cell line), (ii) 40-kb resolution whole-genome Hi-C\\ndata from IMR90 (human lung fibroblast), and (iii) budding yeast whole-genome Hi-C data at a single restriction cut site\\n(EcoRI) resolution. The procedure takes ~12 h with preprocessing when all use cases are run sequentially (~4 h when run\\nparallel). With the recent improvements in its implementation, FitHiC2 (8 processors and 16 GB memory) is also scalable\\nto genome-wide analysis of the highest resolution (1 kb) Hi-C data available to date (~48 h with 32 GB peak memory).\\nFitHiC2 is available through Bioconda, GitHub and the Python Package Index.\\n\",\n",
|
||
" 'chromosome1 x1 x2 chromosome2 yl y2 color observed\\nexpected_bottom_left expected_donut expected_horizontal expected_vertical\\nfdr_bottom_left fdr_donut fdr_horizontal fdr_vertical\\nnumber_collapsed centroid1 centroid2 radius\\n',\n",
|
||
" '',\n",
|
||
" '@FastQC Report\\n\\nSummary\\n\\nQeasic Statistics\\nOre base sequence quality\\n\\nOber sequence quality scores\\n\\nOber base sequence content\\nQeer sequence GC content\\nOeer base N content\\n\\nQ sequence Length Distribution\\nQseauence Duplication Levels\\nQoverrepresented sequences\\nQadapter Content\\n\\nQrxmmer Content\\n\\nQbasic Statistics\\n\\na\\n\\nFilename\\n\\nFile type\\n\\nEncoding\\n\\nTotal Sequences\\n\\nSequences flagged as poor quality\\nSequence length\\n\\n%GC\\n\\nwood_sample_2_forward_paired. fq.gz\\nConventional base calls\\n\\nSanger / Illumina 1.9\\n\\n181818\\n\\n)\\n\\n30-150\\n\\n38\\n\\n@per base sequence quality\\n\\nQuality scores across all bases (Sanger / Illumina 1.9 encoding)\\n\\n40\\n\\n16\\n\\n14\\n12\\n10\\n\\noN B&O\\n\\n12345 67 8 9 1519\\n\\n30-34 45-49 60-64 75-79 90-94 105-109 120-124 135-139 150\\n',\n",
|
||
" '2 (01\\n\\n2 to\\n\\nIntegratedatad\\n\\n5 ntt_ransorn)\\n\\nseurat Lust < Uapply(eearat_ict, function) {\\n2 fart izaseab\\nsr Mingarisioreaaress, slectun.aethed « *vt\", natures = 2t00)\\n\\n»\\n\\n1 (o\\n\\netares<- Selecinteprationentures(bjec List = seat st)\\n\\n1 (ol\\n\\n1 oo\\n\\n1 toa\\n\\n2 (oa)\\n\\n1 tr\\n\\n1 0\\n\\n25 01\\n\\n1 0)\\n\\nSojece tases seutoe ast\\n\\n“Seaing fetures for provided abjecte\\n\\neee\\ntering antars\\n\\n‘egratd surat < Taepratecatl\\n\\nEROS\\nnaing sategrationvectar weights\\n\\nhoneys eee\\n\\nin ajact of clase eure\\nBoy abe tentures)\\n\\nintegrated surat = Fingeighoors(inepretesseuret, ang = 2:30)\\n\\n(Seco\\n\\ndn eject of clase Seurat\\n{itive sosnye aneegrated aon feature, 2aub varsable features)\\n\\nneat ntegratedseuraty rection = \"unas groby = \"arigc ld)\\n\\nost\\n\\n',\n",
|
||
" '‘|\\n\\nf+\\n\\nfor i in samplex/ncbi_dataset/data/GC*/*1_genomic.fna; do re]\\nbowtie2-build \"$i\" \"$i\"\\ndone\\n\\n© 4\\n© a\\n\\nSettings:\\nOutput files: \"sample*/ncbi_dataset/data/GC*/*1_genomic. fna.*.bt2\"\\nLine rate: 6 (line is 64 bytes)\\n\\nLines per side: 1 (side is 64 bytes)\\nOffset rate: 4 (one in 16)\\n\\nFTable chars: 10\\n\\nStrings: unpacked\\n\\nMax bucket size: default\\n\\nMax bucket size, sqrt multiplier: default\\nMax bucket size, len divisor: 4\\nDifference-cover sample period: 1024\\nEndianness: little\\n\\nActual local endianness: little\\n\\nSanity checking: disabled\\n\\nAssertions: disabled\\n\\nRandom seed: 0\\n\\nSizeofs: void*:8, int:4, long:8, size_t:8\\n\\nInput files DNA, FASTA:\\nsamp le*x/ncbi_dataset/data/GC*/*1_genomic. fna\\n\\nError: could not open samplex/ncbi_dataset/data/GC*/*1_genomic. fna\\n\\nTotal time for call to driver() for forward index: 00:00:00\\n\\nError: Encountered internal Bowtie 2 exception (#1)\\n\\nCommand: /data/proj2/teaching/NGS_course/Softwares/bowtie2-2.4.5-lLinux-x86_64/bowtie2-build-s -—-wrapper basic-@ sample*/ncbi_dataset/data/GC\\n\\n*/*1_genomic.fna samplex/ncbi_dataset/data/GC*/*1_genomic. fna\\n\\nHal\\n',\n",
|
||
" '| f -\\nLara-Maike L... R\\n\\nEvaluation\\n\\nIndividual evaluation on a project work, according to:\\ne During the project work\\n\\nMotivation\\n\\nProblem solving capacity\\n\\nData analysis skills\\n\\nProgramming capabilities\\n\\ne Final presentation (10 min)\\nClearness of presentation\\nUsed methods\\nAchieved results\\n\\ne Written report (20 pages maximum)\\nConciseness and precision\\nDelineation of question / problem and solution\\nAccurate method description\\nAccurate usage of the taught biological concepts\\n\\nPresentation is on\\nThursday, 24.7.2025!\\n\\nRegister for the “exam”\\n(=project) on TUM online\\nto be graded\\n(Registration opens at\\nMay 26th)!\\n\\nCluster access:\\n\\n- Gagneur lab, 2FA.\\nLogin admin support\\nonly until 4.6.2025\\n\\n',\n",
|
||
" '@ | verify_packages.R @ | seurat_vig1.R @ | scRep.R* __j run071_Sample_Tag_Calls.csv __j run071_VDJ_perCell.csv =f\\n\\na YQ Show whitespace\\n\\nHHHHHHHHHHHHHHHH HEH\\n\\n## BD Rhapsody Sequence Analysis Pipeline Version 2.2.1\\n\\n## Analysis Date - Fri Mar 07 2025 12:47:22\\n\\n## Libraries - Bioproduct Libraries: Pr416_Pat4TCR_MKDL250001813-1A_22YJ2MLT3_L2_1; Pr416_Pat4WTA_MKDL250001812-1A_22LMGCLT4_L2_1 | ATAC Libr\\n## References - Reference Archive: RhapRef_Human_WTA_2023-@2.tar.gz | AbSeq Reference: None | Supplemental Reference: None | ATAC Predefined\\n## Parameters - Sample Tag Version: flex | Sample Tag Names: | VDJ Version: humanTCR | Putative Cell Calling Data: mRNA | Bioproduct Cell Cc\\nHHHHHHHHHHHHHHHH HEH\\n\\nCell_Index, Total_VDJ_Read_Count , Total_VDJ_Molecule_Count , TCR_ALpha_Gamma_V_gene_Dominant , TCR_Alpha_Gamma_J_gene_Dominant , TCR_ALpha_Gamma_C_ge\\n111840, 7659 9, TRAV1-1*01, TRAJ31*@1, TRAC , GCTGTGAACAACAATGCCAGACTCATG, AVNNNARLM, 6529, 5, TRBV7-9*@1, TRBD2*Q1, TRBJ2-1*@1, TRBCZ , GCCAGCAGCTTAGTGGGGC\\n146164, 13961,6,,,,,,@,0, TRBV27*01, , TRBJ2-3*01, TRBC2 , GCCAGCAGGCCCCCTAGCACAGATACGCAGTAT , ASRPPSTDTOY , 13961, 6, False, T_CD8_memory, True\\n\\n166368 , 2682 , 8, TRAV12-2*01, TRAJ41*O1, TRAC, GCCGTGACCCCCAATTCCGGGTATGCACTCAAC , AVTPNSGYALN, 2187 ,4, TRBV11-2*01 , TRBD1*01, TRBJ1-6*01, TRBC1, GCCAGCAGC\\n205244 ,4509,3,,,,,,0,, TRBV3-1*01, TRBD2*O2 , TRBJZ2-1*@1, TRBC2 , GCCAGCACCCTGGGACTAGCGGGATTCAATGAGCAGTTC,, ASTLGLAGFNEQF , 4509, 3, False, T_CD8_memory,, F\\n289640 , 11702, 11, TRAV38-1*01, TRAJ43*01 , TRAC, GCTTTCATCCCTTATAACAATGACATGCGC, AFIPYNNDMR , 2062, 2, TRBV7-8*01, TRBD1*01, TRBJ2-7*01, TRBC2 , GCCAGCAGCTTC\\n393118 ,0,0,,,,,,0,0,,,,,,,0,0,False, T_CD8_memory, False\\n\\n480269 , 14051, 15, TRAV20*01, TRAJ49*01, TRAC, GCTGTGCAGGCGCGTAGAACCGGTAACCAGTTCTAT , AVQARRTGNOFY , 3370, 6, TRBV9*01, TRBD2*01, TRBJ2-2*01, TRBC2, GCCAGCAC\\n544629 ,0,0,,,,,,0,0,,,,,,,0,0,False, T_CD8_memory, False\\n\\n568064,0,0,,,,,,0,0,,,,,,,0,0,False, T_CD8_memory, False\\n\\n679531, 21831, 19, TRAV12-1*01, TRAJ49*01, TRAC, GTGGAGGAGTTCTAT, VEEFY , 1485, 2, TRBV20-1*05 , TRBD1*01, TRBJ2-1*01, TRBC2 , AGTGCTAGGGCAAGACAGGGGGCGCACAATC\\n731374 ,4770,2, TRAV8-2*01, TRAJZ0*01, TRAC , GTTGTGAGTGGTAACGACTACAAGCTCAGC , VVSGNDYKLS ,4770,2,,,,,,,0,0,False, T_CD8_memory, False\\n\\n749304 ,0,0,,,,,,0,0,,,,,,,0,0,False, T_CD4_memory, False\\n\\n755182 ,6937,6,,,,,,0,0, TRBV4-3*01, TRBD2*@1, TRBJ2-3*@1, TRBC2 , GCCAGCAGCCCTGAGACCGGGACTAGCGGGTTAGGAGCGCAGTAT , ASSPETGTSGLGAQY , 6937 ,6, False, T_CD8_\\n766428 ,0,0,,,,,,0,0,,,,,,,0,0,False, T_CD8_memory, False\\n\\n768329 , 5083, 7, TRAV30*Q1, TRAJ8*Q1, TRAC , GGCACATGGGTGGACACAGGCTTTCAGAAACTTGTA, GTWVDTGFQKLV, 5083,7,,,,,,,0,0, False, T_CD8_memory, True\\n807115,0,0,,,,,,0,0,,,,,,,0,0,False,Natural_killer, False\\n\\n985049,0,0,,,,,,0,0,,,,,,,0,0, False, T_CD8_memory, False\\n\\n1002101 , 10208 ,6, TRAV19*01, TRAJZ7*O1, TRAC , GCCCCAACACCAAT GCAGGCAAATCAACC , APTPMQANOP , 1434, 1, TRBV11-2*@1, , TRBJ1-1*01, TRBC1, GCCAGCAGCTTACATCCGGTTA\\n1136612 5491, 3, TRAV1-1*@1, TRAJ31*O1, TRAC , GCTGTGAACAACAATGCCAGACTCATG , AVNNNARLM, 1464, 2, TRBV7-9*1, TRBD2*01, TRBJ2-1*O1, TRBC2, GCCAGCAGCTTAGTGGGC\\n1185364, 66055 ,51, TRAV21*01, TRAJ9*O1, TRAC , GCTGTGAGGCCAAGGGATGGAGGCTTCAAAACTATC , AVRPRDGGFKTI , 28627 , 27 , TRBV9*@1, TRBD1*@1, TRBJ1-2*01, TRBC1, GCCAGC\\n1328663 , 33604 , 26, TRAV13-1*@1, TRAJ24*O2, TRAC , GCAGCCCAGGGGGGAACTGACAGCT GGGGGAAATTGCAG , AAQGGTDSWGKLQ, 3647 , 8, TRBV28*@1, TRBD1*01, TRBJ2-7*01, TRBC2,\\n1359810,0,0,,,,,,0,0,,,,,,,0,0,False, TCD8_memory, False\\n\\n1379377 ,6610,11, TRAV2*01, TRAJ27*01, TRAC , AGACTGTGTAACACCAATGCAGGCAAATCAACC, RLCNTNAGKST , 3611, 3, TRBV4-1*01, TRBD1*01, TRBJ1-4*01, TRBC1, GCCAGCAGCCC\\n1487305 , 36873, 19, TRAV1-1*01, TRAJ31*O1, TRAC , GCTGTGAACAACAATGCCAGACTCATG , AVNNNARLM, 20250, 10, TRBV7-9*01 , TRBD2*@1, TRBJ2-1*@1, TRBC2, GCCAGCAGCTTAGT\\n1552703 , 3993 ,9 , TRAV38-1*01, TRAJ53*@1, TRAC , GCCTCTCTGAATAGTGGAGGTAGCAACTATAAACTGACA , ASLNSGGSNYKLT , 33,2, TRBV7-9*01, , TRBJ1-2*01, TRBC1, GCCAGCAGCTT\\n1709118 , 20901 , 43 , TRAV38-1*01, TRAJ37*02, TRAC, GCTTTCATGAAGTTAACCGTCACTCCTATCGTCTCTAGCAACACAGGCAAACTAATC , AFMKLTVTPIVSSNTGKLI , 11055 , 20, TRBV4-1*01\\n1755027 AA aa A A False T CNR memoarw False\\n\\nWOONDAUBPWNPR\\n\\nNNNNNRPPRRPRPR PEPPER\\nBRWNPSOMNDAURWNES\\n\\nYPWWWWWNNNND\\nABWNPOBOUOON DU\\n\\n1\\n>\\n',\n",
|
||
" \"Recording valid ligation events\\n\\nWe use the parse module of the pairtools pipeline to find ligation junctions in Micro-C (and\\nother proximity ligation) libraries. When a ligation event is identified in the alignment file the\\npairtools pipeline will record the outer-most (5’) aligned base pair and the strand of each one of the\\npaired reads into .pairsam file (pairsam format captures SAM entries together with the Hi-C pair\\ninformation). In addition, it will also asign a pair type for each event. e.g. if both reads aligned\\nuniquely to only one region in the genome, the type UU (Unique-Unique) will be assigned to the\\npair. The following steps are necessary to identify the high quality valid pairs over low quality\\nevents (e.g. due to low mapping quality):\\n\\npairtools parse options:\\n\\nParameter Value Function\\nMapg threshold for defining an alignment as a multi-mapping\\n\\nmin-mapq 40 alignment. Alignment with mapq <40 will be marked as type\\nM (multi)\\n\\nWalks is the term used to describe multiple ligations events,\\nresulting three alignments (instead of two) for a read pair.\\nHowever, there are cases in which three alignment in read\\npairs are the result of one ligation event, pairtool parse can\\n\\nwalks-policy Sunique rescue this event. walks-policy is the policy for reporting un-\\nrescuable walk. Sunique is used to report the 5'-most unique\\nalignment on each side, if present (one or both sides may\\nmap to different locations on the genome, producing more\\nthan two alignments per DNA molecule)\\n\\nIn cases where there is a gap between alignments, if the gap\\nis 30 or smaller, ignore the gap, if the gap is >30bp, mark as\\n“null” alignment\\n\\nmax-inter-align- 4)\\ngap\\n\\npairtools has an automatic-guess function to identify the\\nformat of the input file, whether it is compressed or not.\\nWhen needed, the input is decompressed by bgzip/Iz4c. The\\noption nproc-in set the number of processes used by the\\nauto-guessed input decompressing command, if not\\nspecified, default is 3\\n\\nnproc-in integer, e.g. 16\\n\\npairtools automatic-guess the desired format of the output\\nfile (compressed or not compressed, based on file name\\nextension). When needed, the output is compressed by\\nbezip/Iz4c. The option nproc-out set the number of\\nprocesses used by the auto-guessed output compressing\\ncommand, if not specified, default is 8\\n\\nnproc-out integer, e.g. 16\\n\\nchroms-path path to .genome file, e.g. hg38.genome\\n\\npath to sam file used as an input. If you are piping the input\\n(stdin) skip this option\\n\\nname of pairsam file for writing output results. You can\\n\\n*pairsam choose to skip and pipe the output directly to the next\\ncommad (pairtools sort)\\n\\npairtools parse command example for finding ligation events:\\n\",\n",
|
||
" 'Percentage (26)\\n\\nCG DMR Annotation Enrichment Plot CHG DMR Annotation Enrichment Plot CHH DMR Annotation Enrichment Plot\\n\\n‘Annotation Type ‘Annotation Type ‘Annotation Type\\n\\nPercentage (2)\\n\\nPercentage (26)\\n\\n= 57.8 60.5 © 637\\n\\n445\\n\\n24.4\\n\\n165 20.9 20.9 20.9\\n\\n',\n",
|
||
" 'In [259]: merged_seurat <- FindVariableFeatures(merged_seurat)\\n\\nError: SCT assay is comprised of multiple SCT models. To change the variable features, please set manually with Var\\niableFeatures<-—\\nTraceback:\\n',\n",
|
||
" '',\n",
|
||
" 'In [191]: # Get barcodes from Seurat\\nseurat_barcodes <- colnames(combined_seurat)\\n\\n# Get barcodes from TCR (combine all into one vector)\\ntcr_barcodes <- unlist(lapply(combined_TCR, function(df) df$barcode) )\\n\\n# Check intersection\\nlength(intersect(seurat_barcodes, tcr_barcodes) )\\n\\n274\\n',\n",
|
||
" 'Genome editing\\n(to insert mutations or correct them)\\n\\nTUM\\n\\nThere are several genome editing tools\\nthat are commonly used for plants,\\nincluding:\\n\\n1. CRISPR/Cas9\\n\\n2. CRISPR/Cas12a: CRISPR/Cas12a\\nis another CRISPR system that has\\nbeen used for genome editing in plants,\\noffering some advantages over the\\nCas9 system.\\n\\n3. TALENs (Transcription Activator-\\nLike Effector Nucleases)\\n\\n4. ZFNs (Zinc Finger Nucleases)\\n',\n",
|
||
" '### plot the corrected data in fall heatmap and compare to the white-red colormap ###\\n### thanks for the alternative collormap naming to https: //twitter.com/HiC_memes/status/1286326919122825221/photo/###\\nimport cooltools. Lib. plotting\\n\\nvmax = 5000\\nnorm = LogNorm(vmin=1, vmax=100_000)\\nfruitpunch = sns.blend_palette([\\'white\\', ‘red\\'], as_cmap=True)\\n\\nf, axs = plt.subplots(\\nfigsize=(13, 10),\\n\\nncols=2,\\nsharex=True, share\\n\\nax = axs(0, 0]\\n\\nax.set_title( ‘Pumpkin Spice\")\\n\\nim = ax.matshow(clr.matrix(balance=False) [:\\nplt.colorbar(im, axsax , fraction=0.046, pad=0.04, label\\nplt.xticks(chromstarts, cLr.chromnames) ;\\n\\ncounts (Linear)\");\\n\\nax = axs[0, 1]\\nax.set_title(\\'Fruit Punch\")\\n\\nin3 = ax.matshow(clr.matrix(balance-False) [:], vmax=vmax, cnap=fruitpunch)\\n204, label\\n\\nplt.colorbar(im3, axsax, fraction=0.046, pac\\nplt.xticks (chromstarts, clr.chromnanes)\\n\\ncounts (Linear)\");\\n\\nax = axs(1, 0]\\nim = ax.matshow(clr.matrix(balance=False)\\nplt.colorbar(im, axsax , fraction=0.046, pad=0.04, label\\nplt.xticks(chromstarts, cLr.chromnanes)\\n\\ncounts (1og)\");\\n\\nax = axs(1, 1]\\n\\nim3 = ax.matshow(clr.matrix(balance=False) [:], norm=norm, cmap=fruitpunch) ;\\n-04, label\\n\\nplt.colorbar(im3, axsax, fraction=0.046, pac\\nplt.xticks (chromstarts, clr.chromnanes)\\n\\ncounts (109) *\\n\\nplt.tight_layout()\\n4] @ 08s\\n\\nModuleNotFoundError Traceback (most recent call last)\\nFile ~/anaconda3/envs/cool_notebook/1ib/python3. 10/site-packages/cooltools/Lib/plotting.py:6\\ntry:\\nfrom matplotlib.colormaps import register\\nexcept ImportError:\\n\\n6\\nz\\n\\nModuleNotFoundError: No module named ‘matplotlib.colormaps’\\n\\nDuring handling of the above exception, another exception occurre\\n\\nInportError Traceback (most recent call last)\\n\\nCell Inl4], Line 3\\n\\ndt plot the corrected data in fall heatmap and compare to the white-red colormap ###\\n\\n‘### thanks for the alternative collormap naming to https://twitter.com/HiC memes /status/1286326919122825221/photo/1###\\nimport cooltools. Lib. plotting\\n\\nvmax = 5000\\n\\nnorm = LogNorm(vmin=1, vmax:\\n\\n\\\\\\nIe ben ke\\n\\nFile ~/anaconda3/envs/cool_notebook/Lib/python3. 10/site-packages/cooltools/Lib/plotting.py:8\\n\\n& from matplotlib.colormaps import register\\n7 except InportError:\\n---> 8 from matplotlib.cm import register_cmap as register\\n\\nimport matplotlib as mpl\\n11 import matplotlib.pyplot as plt\\n\\nImportError: cannot import name \\'register_cmap\\' from \\'matplotlib.cm\\' (/usr/users/papantonis1/anaconda3/envs/cool_notebook/ Lib/python3. 10/site-packages/matplotlib/cm.py)\\n\\nimport matplotlib\\nprint(matplotlib.__version.\\n\\n2] ¥ 00s\\n\\n3.10.1\\n\\nPROBLEMS OUTPUT DEBUG CONSOLE TERMINAL PORTS JUPYTER\\n\\n‘AU nano) /ust/users/paparitonis1/anacondas/envs/cool_notebook/ UiD/py\\'\\n\\nMigrated from :mod:*mirnylib.plotting” .\\n\\nfron natplotlib.colormaps import register\\nexcept InportError:\\nfrom natplotlib.on inport register_orap as register\\n\\ninport matplotlib as mpl\\nimport matplotlib.pyplot as plt\\nimport nunpy as np\\n',\n",
|
||
" '| herewith apply for enrolment as a guest student at the Faculty\\n\\nand for permission to attend the courses specified overleaf.\\nAMAN SHAMIL NALAKATH\\nName, first name(s):\\n\\nPrevious guest\\nmatriculation no. (if\\n\\nknown):\\n02.09.2000 Kerala, India\\nDate of birth: Place of birth:\\nIndian\\nNationality:\\nMaster Student\\nOccupation:\\n\\nLovely Professional University, Punjab, India\\nSecondary education\\n(graduation):\\n\\nAddress and phone No. C/o Rishab Suresh, AngerstraRe 5, 37073, Gottingen\\n\\nduring the semester:\\n\\nE-Mail Address: Aman.nalakath@tum.de\\n\\nType and duration of previous studies (specifying the universities and semesters attended)\\n\\nfrom ...... to... Subject(s) University\\n\\n01.10.2023 — 30.09.2025 | MSc. Agricultural Biosciences Technical University of Munich\\n\\n',\n",
|
||
" '[30]:\\n\\n#Summarizing\\nls -ltrh | grep .txt\\n\\n—rw-rw-r——\\n—rw-rw-r——\\n—rw-rw-r——\\n—rw-rw-r——\\n—rw-rw-r——\\n\\n1 pstl4 pst14\\n1 pstl4 pst14\\n1 pstl4 pst14\\n1 pstl4 pst14\\n1 pstl4 pst14\\n\\n429 Jan\\n430 Jan\\n417 Jan\\n424 Jan\\n428 Jan\\n\\n20 21:35 flagstat_report. txt\\n\\n20 21:44 flagstat_report_2.txt\\n20 21:51 flagstat_report_3. txt\\n20 21:57 flagstat_report_4. txt\\n20 22:01 flagstat_report_5. txt\\n',\n",
|
||
" 'ge Plant Epigenome\\nBrowser\\n\\nsect\\n\\nanc seg 3\\n\\nmeg :\\nBea meri\\n\\nrae ‘\\n\\nirsenabnitninpmeyiityrr afl mnie ahi\\n\\n',\n",
|
||
" 'Other alignment workflows\\n\\nBeyond the default 10x Genomic Cell Ranger pipeline outputs, scRepertoire supports the following single-cell formats:\\n\\ne AIRR\\n\\nBD Rhapsody Multiomic Immune Profiling\\nImmcantation\\n\\nJSON-formatted contig data\\n\\nMiXCR\\n\\nOmniscope OS-T/OS-B\\n\\nParse Evercode TCR/BCR\\n\\nTRUST4\\n\\n¢ WAT3R\\n\\nloadContigs() can be given a directory where the sequencing experiments are located and it will recursively load and process the\\ncontig data based on the file names. Alternatively, loadContigs() can be given a list of data frames and process the contig data\\n\\n#Directory example\\n\\ncontig.output <— c(\"~/Documents/MyExperiment\" )\\n\\ncontig. list <- loadContigs(input = contig.output,\\nformat = \"TRUST4\")\\n\\n#List of data frames example\\n\\nS1 <- read.csv(\"~/Documents/MyExperiment/Sample1/outs/barcode_results.csv\")\\nS2 <- read.csv(\"~/Documents/MyExperiment/Sample2/outs/barcode_results.csv\")\\n$3 <- read.csv(\"~/Documents/MyExperiment/Sample3/outs/barcode_results.csv\")\\nS4 <- read.csv(\"~/Documents/MyExperiment/Sample4/outs/barcode_results.csv\")\\n\\ncontig_list <- list(S1, $2, $3, S4)\\ncontig. list <- loadContigs(input = contig.output,\\nformat = \"WAT3R\")\\n',\n",
|
||
" '2-IV\\n\\nFigure 1: Kink and Bend in Arabidopsis Thaliana\\n',\n",
|
||
" 'a Genome assembly\\nSe\\n\\nCollection of high-quality DNA\\nfrom reference individual\\n\\nWhole genome sequencing\\n\\nb Identification of informative genetic markers\\n\\ney ACCGATT\\n—_— ATCGGTC\\nACCGATC\\nATCGGTC\\nATCGATC\\n—\\nRange-wide sampling of Whole genome sequencing Read mapping and\\nbaseline populations {individual or pooled) variant calling\\n€ Design of SNP array and testing\\ny < | >eim rceccatrc\\nv— & | pele Talsiccalrirc\\noo © | elm tToleccalrrc\\nx— © | >—Om roalccalclrc\\nS| whe rcalccalcirc\\n2 | Sem roalccalcrc\\nMarker ranking Testing of candidate markers\\nand selection in baseline samples\\n\\nd Application to fisheries management\\n\\nFine-tuning of existing stocks\\nand management areas\\n\\nReads -— ale\\na —= =\\n= _—=\\nContigs ’ hs 4\\nGenome |\\n\\noe\\n\\nAssembly of the reference\\n\\ngenome sequence\\n\\nGenetic difference\\n\\nDetection of differentiated\\n\\ngenetic markers\\nPopA PopB\\n\\n1.0\\n0.5\\n‘ |\\n\\nDevelopment of assignment\\nmodel with baseline samples\\n\\nProbability\\n\\nMixed sample\\n1.0\\n2\\n3 osb —\\n-\\na ™) PopA\\n0 1 i PopB\\nAssignment of mixed Long-term\\nstock samples monitoring\\n',\n",
|
||
" 'corrected Hi-C counts\\n\\n10!\\n\\n10°\\n\\n107}\\n\\n104\\n\\n10°\\ngenomic distance\\n\\n10®\\n\\n—— data_mcool.h5\\n\\n',\n",
|
||
" '(snpeff_env)\\n(snpeff_env)\\n(snpeff_env)\\n(snpeff_env)\\n(snpeff_env)\\n(snpeff_env)\\n\\n[pst14@frontend\\n[pst14@frontend\\n[pst14@frontend\\n[pst14@frontend\\n[pst14@frontend\\n[pst14@frontend\\n\\nref_gen]$\\nref_gen]$\\nref_gen]$\\nref_gen]$\\nref_gen]$\\nref_gen]$\\n\\nsnpEff\\nsnpEff\\nsnpEff\\nsnpEff\\nsnpEff\\n\\ndatabases\\ndatabases\\ndatabases\\ndatabases\\ndatabases\\n\\ngrep\\ngrep\\ngrep\\ngrep\\ngrep\\n\\n--color\\n--color\\n--color\\n--color\\n--color\\n\\nCedrela_odorata\\nDiospyros_rhombifolia\\nQuercus_robur\\nSwietenia_mahagoni\\nQuercus_mongolica\\n',\n",
|
||
" 'Visualization: HiGlass,\\nJuicaBox\\n\\nHICCUPS juicer_tools:\\n\\n-bedpe file\\n\\nEnrichmnet Juicer\\n\\nAPA,\\nTADS: Arrowhead\\n\\nJuicer\\n\\n‘Timmomatic, FastQC\\n\\nHic-Pro,\\n\\ntbedpe ~—————>_GenomicLinks\\n\\nVisualization: JuiceBox\\nAnolysis: Hic Straw\\n\\nJuicer\\ndump\\n\\nHic-Pro -\\nbuild_matrix\\n\\nIndividual Matrices <——\\n\\nAnalysis: Cooler\\nliorary python,\\n\\n> FitHiC2 loop caller\\n\\nEnrichment:\\n‘coolpup.ey\\n\\nVisualization: HiGloss\\n',\n",
|
||
" 'Binned Normalized Y Coordinate\\n\\n[0.75, 1.00]\\n\\n[0.50, 0.75]\\n\\n[0.25, 0.50]\\n\\n[0.00, 0.25]\\n\\nVolume Histogram\\n\\n|\\n\\nmmm Stage 2-III\\njm Stage 21V\\nmm Stage 2-V\\n\\nC) 5 10 15 20\\nSum of pool rescaled volume\\n\\n25\\n\\n',\n",
|
||
" '@ Zed File Edit Selection View Go Window Help\\n\\nma BmeOorrtktoewwwn F-<ase\\n\\nTue Dec 17 16:52\\n\\n@ © @ = multiqc_datajson\\n\\nreport_data_sources <>\\nFastQc\\nall_sections multiqc_data.json\\nGLDS-251_rna-seq_13JUN2017H\\nGLDS-251_rna-seq_13JUN2017H\\nGLDS-251_rna-seq_13JUN2017H\\nGLDS-251_rna-seq_13JUN2017H\\nGLDS-251_rna-seq_13JUN2017H\\nGLDS-251_rna-seq_13JUN2017H\\nGLDS-251_rna-seq_13JUN2017H\\nreport_general_stats_data\\nGLDS-251_rna-seq_13JUN2017HiSeq_|\\npercent_gc\\navg_sequence_length\\nmedian_sequence_length\\ntotal_sequences\\npercent_duplicates\\npercent_fails\\nGLDS-251_rna-seq_13JUN2017HiSeq_|\\npercent_gc\\navg_sequence_length\\nmedian_sequence_length\\ntotal_sequences\\npercent_duplicates\\npercent_fails\\nGLDS-251_rna-seq_13JUN2017HiSeq_|\\npercent_gc\\navg_sequence_length\\nmedian_sequence_length\\ntotal_sequences\\n\\npercent_duplicates\\nFilter... z\\n\\nst & v\\n\\nClick to restart and update Zed\\n\\nmultiqc_data.json\\n\\nPELCSNL_LaLLsS 2 7.UFUFTUTUIVUIVIVIZ\\n\\nBo\\n\\nSign in\\n\\n+\\n\\noO\\n\\nQ*® I\\n\\n\"GLDS-251_rna-seq_13JUN2017HiSeq_Run_Sample_235_239_UMISS_Hoeksema_GTTTCG_L0@3_R1_001_1M\": {\\n\\n\"percent_gc\": 46.0,\\n\"avg_sequence_length\": 125.0,\\n\"median_sequence_length\": 125,\\n\"total_sequences\": 1000000.0,\\n\"percent_duplicates\": 23.347216247708587,\\n\"percent_fails\": 9.090909090909092\\n\\nBo\\n\\n\"GLDS-251_rna-seq_13JUN2017HiSeq_Run_Sample_120_UMISS_Hoeksema_TGACCA_L001_R1_001_1M\":\\n\\n\"percent_gc\": 49.0,\\n\"avg_sequence_length\": 125.0,\\n\"median_sequence_length\": 125,\\n\"total_sequences\": 1000000.0,\\n\"percent_duplicates\": 52.07411329479328,\\n\"percent_fails\": 18.181818181818183\\n\\nBo\\n\\n\"GLDS-251_rna-seq_13JUN2017HiSeq_Run_Sample_175_UMISS_Hoeksema_AGTTCC_L00Q2_R1_001_1M\":\\n\\n\"percent_gc\": 47.0,\\n\"avg_sequence_length\": 125.0,\\n\"median_sequence_length\": 125,\\n\"total_sequences\": 1000000.0,\\n\"percent_duplicates\": 30.77778969527732,\\n\"percent_fails\": 9.090909090909092\\n\\nBo\\n\\n\"GLDS-251_rna-seq_13JUN2017HiSeq_Run_Sample_179_UMISS_Hoeksema_CCGTCC_LO0Q3_R1_001_1M\":\\n\\n\"percent_gc\": 45.0,\\n\"avg_sequence_length\": 125.0,\\n\"median_sequence_length\": 125,\\n\\nNt ata enniianene \". ANNNANAAN A\\n\\nUpdated to Zed 0.163.2\\nView the release notes\\n\\nalgal JSON\\n\\nv\\n',\n",
|
||
" 'c\\n\\nPDF pe Ss 0) p (DE)\\n\\nBookma a 00 do elp\\nourse: Bioinforma feld g:Profiler — a web serve P A: age ete pantherdb.org/gene A agriGO - re\\nee tN Q _\\ned Fro Online Bewerbung Q API Do e qg g to pe e p a Pastebin.co argetP 2.0 - D p ood\\nXPOFT tO jow query Bs; OW short lin Bs;\\n© For print @\\nCapped @\\n©) Make unselected terms transparent\\n@ @. €0e 3\\n2165 30 values above this rresnoye capped\\n144\\ne\\n2\\n“ ° £ °\\n~ 104 ®\\n3 @\\na\\ng 7 e 3 ee e\\n3 ee? e @ e\\n64 4 e@\\ne e ® . e ° ° © °\\n44 6\\noe 7 © Se, gS\" ee\\n3 oe ABH OP DB & e °° wm °\\noJ\\nSo, So, So, ‘edie\\n> “Cig 7\\n7) Z 7\\nID Source Term ID | Term Name Paaj (query_1)\\n1 GO:MF GO:0009055 electron transfer activity 1.555x10-23\\n2 GO:MF G0:0016168 chlorophyll binding 9.742x107\\'3\\n3 GO:MF GO:0048038 quinone binding 6.992x10°8\\n4 GO:MF GO:0046872 metal ion binding 9.366x10°6\\n5 GO:MF GO:0043565 sequence-specific DNA binding 5.917x10°5\\n6 GO:MF G0:0046933 proton-transporting ATP synthase activity, rota... 4.390x10-4\\n7 GO:MF GO:0003700 DNA-binding transcription factor activity 3.258x10°3\\n8 GO:BP GO:0015979 photosynthesis 5.036x10-83\\n9 GO:BP GO:0042221 response to chemical 2.332x10°13\\n10 GO:BP. GO:0019438 aromatic compound biosynthetic process 2.015x10-3\\n11 GO:BP. GO:0022904 respiratory electron transport chain 2.569x10°3\\n12 GO:BP GO:1902600 proton transmembrane transport 4.867x10-3\\n13 GO:BP. GO:0015986 proton motive force-driven ATP synthesis 1.042x10°2\\n14 GO:BP. GO:0009644 response to high light intensity 1.238x1072\\n15 GO:BP. GO:0042430 indole-containing compound metabolic process 4.127x10-2\\n16 GO:BP. GO:0009873 ethylene-activated signaling pathway 4.228x10°2\\n17 GO:BP. GO:0009889 regulation of biosynthetic process 4.775x102\\n18 GO:CC GO:0009535 chloroplast thylakoid membrane 6.016x10734\\n19 GO:CC GO:0010287 plastoglobule 4.281102\\nversion e111_eg58_p18_f463989d A Deng\\n3 &\\n\\n>\\na\\n\\nY\\n',\n",
|
||
" 'Mean Methyation Level\\n\\nMean Methylation Levels ~ CG Context\\n\\nMean Methylation Levels ~ CG Context\\n\\nFile\\n\\nFile\\n\\nMean Methylation Levels ~ CG Context\\n\\nMean Methylation Level\\n\\n',\n",
|
||
" \"HiProt.,\\n\\n| think there needs to be an internal examiner as well, which | was hoping you'd be\\n\\nThesis register (external)\\n\\n‘YOUR NAME (FIRST NAME, LAST NAME)\\n\\n‘YOUR MATRICULATION NUMBER\\n\\nDEGREE PROGRAM\\n\\nMaser Agree Borcerces Vv\\n\\nPRELIMINARY TITLE IN GERMAN (EXCEPT FOR ENGLISCH DEGREE PROGRAMS A GERMAN TITLES\\nMANDATORY FOR THESIS REGISTRATION)\\n‘ett at mah pn oho ed Ba Ps py tno ptr ak A pn PLEASE GEOR\\n\\nBUS % x Bsone aaa Q\\n\\nPRELIMINARY TITLE IN ENGLISH (MANDATORY FOR THESIS REGISTRATION)\\n\\nBUS % x Deore eee a\\n\\nORGANIZATION / CHAIR INTERNAL\\n\\n‘asitant Protease of Poputon Epona ana Epigerorce Vv\\n\\nORGANIZATION / CHAIR INTERNAL (IF NOT AVAILABLE ABOVE.)\\n\\n{1LEXAMINER INTERNAL\\n\\neames Fra] Po De Vv\\n\\nyour exami nti into at leae coi! the Canpus Oflesexannaton eam\\n\\n2. EXAMINER INTERNAL\\n\\nPhase soe Vv\\n\\n|Asacond examiner cy eared be wing epee popes:\\n\\n1+ Bachar Frety Sconce and Resare Management you stad your degre ber 0110202: yu tata your\\n\\nsean th wr somes 202 orate ery oer rage\\n1+ Master Foret ar Wood Scance (yu tried your degre bee 0.10.2022: you sare your dare ne wer\\nsemest 2022 rt nly oe examiner's raed)\\n\\n[Atetor:Intbe Mase alo an Mater Maca techy epee prorat, th second examina a the WPP 2 NOT\\narate th sacend examine Beard Perfor doesnt have be entre ee!\\n\\nORGANIZATION / CHAIR EXTERNAL\\n\\nSUPERVISOR EXTERNAL\\n\\nIl also ask our study coordinator about this\\n‘Thank you\\nRegards,\\n\\n‘Aman\\n\",\n",
|
||
" '### plot the corrected data in fall heatmap and compare to the white-red colormap ###\\n### thanks for the alternative collormap naming to https: //twitter.com/HiC_memes/status/1286326919122825221/photo/###\\nimport cooltools. Lib. plotting\\n\\nvmax = 5000\\nnorm = LogNorm(vmin=1, vmax=100_000)\\nfruitpunch = sns.blend_palette([\\'white\\', ‘red\\'], as_cmap=True)\\n\\nf, axs = plt.subplots(\\n13, 10),\\n\\nncols=2,\\nsharex=True, sharey=True)\\n\\nax = axs[0, 0]\\n\\nax.set_title( ‘Pumpkin Spice\")\\n\\nim = ax.matshow(clr.matrix(balanc\\nplt.colorbar(im, axeax ,fraction=0.046, pad=0.04, label=\"counts (Linear) \\'\\nplt.xticks (chronstarts, clr.chromnanes) ;\\n\\nax = axs[@, 1\\n\\nax.set_title( ‘Fruit Punch\")\\n\\n4im3 = ax.matshow(clr.matrix(balance=False) [:], vmax=vmax, cnap=fruitpunch:\\nplt.colorbar(im3, ax=ax, fraction=0.046, pa\\nplt.xticks(chromstarts, cLr.chromnames) ;\\n\\n+04, label=\"counts (Linear) \\');\\n\\nax = axs[1, 0]\\n\\nim = ax.matshow(clr.matrix(balance=False) [:], normsnorn, cnap=\" fall!\\nplt.colorbar(in, axeax ,fraction=0.046, pad=0.04, label=\\'counts (1og)\")\\nplt.xticks (chronstarts, clr.chromnanes) ;\\n\\nax = axs(1, 1)\\n\\nim3 = ax.matshow(clr.matrix(balance=False) [\\nplt.colorbar(im3, axsax, fraction=0.046, pad=é\\nplt.xticks(chromstarts, cLr.chromnames) ;\\n\\n+ normenorm, cnap=fruitpunch\\n.04, label=\"counts (109) \\'\\n\\nplt.tight_layout()\\n7 @ 00s\\n\\nModuleNotFoundError Traceback (most recent call last)\\nFile ~/anaconda3/envs/cool_notebook/1ib/python3. 10/site-packages/cooltools/Lib/plott ing. p\\n5 tr\\n\\n\\\\\\n\\n& from matplotlib.colormaps import register\\n7 except InportError:\\n\\nModuleNotFoundErroi\\n\\nNo module named \\'matplotlib.colormaps\\'\\nDuring handling of the above exception, another exception occurred:\\n\\nInportError Traceback (most recent call last\\n\\nCell In[17], Line 3\\n\\n#t# plot the corrected data in fall heatmap and compare to the white-red colormap ###\\n\\n‘### thanks for the alternative collormap naming to https://twitter.com/HiC memes /status/1286326919122825221/photo/1###\\nimport cooltools. Lib. plotting\\n\\nvmax = 5000\\n\\nnorm = LogNorm(vmii\\n\\n\\\\\\nIe ben ke\\n\\nFile ~/anaconda3/envs/cool_notebook/Lib/python3. 10/site-packages/cooltools/Lib/plott ing-p\\n\\n& from matplotlib.colormaps import register\\n7 except InportError:\\n---> 8 from matplotlib.cm import register_cmap as register\\n\\n10 import matplotlib as mpl\\n11 import matplotlib.pyplot as plt\\n\\nInportErro\\n\\ncannot import name \\'register_cmap\\' from \\'matplotlib.cm\\' (/usr/users/papantonis1/anaconda3/envs/cool_notebook/ Lib/python3. 10/site-packages/natplotlib/cm.py)\\n\\nimport matplotlib\\n2] ¥ 00s\\n\\nPROBLEMS OUTPUT DEBUG CONSOLE TERMINAL PORTS JUPYTER,\\n\\n‘AU nano)\\n\\n/ust/users /papantonis1/anacondas/envs/cool_notebook/ Lib/python3.10/site-packages/cooltools/ Lib/plotting.py\\n\\nMigrated from :nod:*mirnylib. plotting’.\\n\\nfron natplotlib.colormaps import register\\n‘except Inportérror:\\nfrom natplotlib.on inport register_orap as register\\n\\nimport matplotlib as mpl\\nimport natplotlib.pyplot as plt\\nimport nunpy as np\\n',\n",
|
||
" '[amnala@base prokka]$ cat Ecoli_nano/Ecoli_nano_genome.tsv | grep hypothetical\\n\\nIKAOHOFJ_@0002 CDS 158 hypothetical protein\\nIKAOHOFJ_@0@007 CDS 1425 hypothetical protein\\nIKAOHOFJ_0@017 CDS 447 hypothetical protein\\nIKAOHOFJ_@0020 CDS 483 hypothetical protein\\nIKAOHOFJ_@0021 CDS 369 hypothetical protein\\nIKAOHOFJ_@0025 CDS 174 hypothetical protein\\nIKAOHOFJ_@0@026 CDS 285 hypothetical protein\\nIKAOHOFJ_@0@029 CDS 1539 hypothetical protein\\nIKAOHOFJ_@0@042 CDS 342 hypothetical protein\\nIKAOHOFJ_00043 CDS 375 hypothetical protein\\nIKAOHOFJ_@0045 CDS 789 hypothetical protein\\n\\nIKAOHOFJ_@0046 CDS 585 hypothetical protein\\n',\n",
|
||
" 'Ice ot\\n‘earn ere ta rao pen 2 prema ne oe [eeremsne [seen]\\n\\nFastQC: Per Sequence GC Content\\nPea Samp\\n\\nPer Base N Content [aim\\n\\n‘epocenapecttancastcan poten ren an asa\\n\\nFastQC: Per Base N Content\\n\\n‘Sequence Length Distribution [a\\n\\nMimosa equa ci ng)\\n\\n‘Sequence Duplication Levels SE (ome)\\n‘eae ge yer\\n[eeewwres [cere]\\n\\nFastQC: Sequence Duplication Levels,\\n\\nOverrepresented sequences by sample SKIN\\n\\n‘Pett arr ctonnpeericsminceanh eaten.\\n\\nTop overrepresented sequences\\n\\n‘ie onmmteeseince sr ssarde The soe 2 trent ser cern aye noosa yr\\n\\n‘Adapter Content [ZI [ome]\\n\\n‘Peamusiepenep cathe sana yay te asa en aspen enon\\n[eeremsne [seen]\\n\\nFastQC: Adapter Content\\n\\n',\n",
|
||
" \"In\\n\\n[7]:\\n\\nR.version$plat form\\nR.version$version.string\\n\\n'x86_64-conda-linux-gnu'\\n\\n'R version 4.3.3 (2024-02-29)'\\n\",\n",
|
||
" 'In [192]: sapply(combined_TCR, function(df) {\\nsum(df$barcode %in% colnames(combined_seurat) )\\n\\n3)\\n$1: 0 S2: 0 S3: 0 S4: 0 S5: 0 S6: 0 S7: 0 S8: 0 S1: 274\\n',\n",
|
||
" \"callset ['calldata/GT']\\n\\nB\\n\\narray(([{{ 1, -1],\\n-11,\\n\\n-11,\\n\\n-11,\\n-11,\\n-11,\\n\\n-11,\\n-11,\\n-11,\\n\\n-11,\\n-11,\\n-11,\\n\\n-11,\\n-11.\\n\\n\",\n",
|
||
" 'Arbuscule development\\n\\na cee. ees\\nSbtM1 Gene\\nceeennnnnnennnsnnenseneesennenennsenneenenennseneenensesnenasenennsecunmeaneneanees expression\\nBCPI\\n\\nPM Cell wall\\n\\nC | stage! Stage Il Stage Ill Stage lV Stage V\\nPPA Cell entry Birdsfoot Mature arbuscule Collapsed arbuscule\\nt t t t TL\\nCYCLOPS RAM1, RAM2 OsPT13\\nDIS\\nRED\\n\\n3 VAMPs @ PT4 tT ] SbtM1 P BCPI\\n',\n",
|
||
" '[amnala@base alignment]$ ls sorted.GLDS*\\n\\nsorted\\nsorted\\nsorted\\nsorted\\nsorted\\nsorted\\nsorted\\nsorted\\nsorted\\nsorted\\nsorted\\nsorted\\nsorted\\nsorted\\n\\n.GLDS-251_rna—seq_13JUN2017HiSeq_Run_Samp\\n-GLDS-251_rna—seq_13JUN2017HiSeq_Run_Samp\\n.GLDS-251_rna—seq_13JUN2017HiSeq_Run_Samp\\n.GLDS-251_rna—seq_13JUN2017HiSeq_Run_Samp\\n.GLDS-251_rna—seq_13JUN2017HiSeq_Run_Samp\\n.GLDS-251_rna—seq_13JUN2017HiSeq_Run_Samp\\n.GLDS-251_rna—seq_13JUN2017HiSeq_Run_Samp\\n.GLDS-251_rna-—seq_13JUN2017HiSeq_Run_Samp\\n.GLDS-251_rna—seq_13JUN2017HiSeq_Run_Samp\\n-GLDS-251_rna—seq_13JUN2017HiSeq_Run_Samp\\n.GLDS-251_rna—seq_13JUN2017HiSeq_Run_Samp\\n.GLDS-251_rna—seq_13JUN2017HiSeq_Run_Samp\\n.GLDS-251_rna—seq_13JUN2017HiSeq_Run_Samp\\n.GLDS-251_rna—seq_13JUN2017HiSeq_Run_Samp\\n\\n[amnala@base alignment]$\\n\\ne_111_UMISS_Hoeksema_ATCACG_L@@1_R1_001_1M.\\ne_111_UMISS_Hoeksema_ATCACG_L@@1_R1_001_1M.\\ne_114_UMISS_Hoeksema_CGATGT_L@@1_R1_001_1M.\\ne_114_UMISS_Hoeksema_CGATGT_L@@1_R1_001_1M.\\ne_116_UMISS_Hoeksema_TTAGGC_L@@1_R1_001_1M.\\ne_116_UMISS_Hoeksema_TTAGGC_L@@1_R1_001_1M.\\ne_12@_UMISS_Hoeksema_TGACCA_L@0@1_R1_001_1M.\\ne_120@_UMISS_Hoeksema_TGACCA_L@@1_R1_001_1M.\\ne_175_UMISS_Hoeksema_AGTTCC_L@@2_R1_001_1M.\\ne_175_UMISS_Hoeksema_AGTTCC_L@@2_R1_001_1M.\\ne_179_UMISS_Hoeksema_CCGTCC_L@@3_R1_001_1M.\\ne_179_UMISS_Hoeksema_CCGTCC_L@@3_R1_001_1M.\\ne_235_239_UMISS_Hoeksema_GTTTCG_L@@3_R1_001_1M.fastq.\\ne_235_239_UMISS_Hoeksema_GTTTCG_L@@3_R1_001_1M.fastq.\\n\\nfastq.\\nfastq.\\nfastq.\\nfastq.\\nfastq.\\nfastq.\\nfastq.\\nfastq.\\nfastq.\\nfastq.\\nfastq.\\nfastq.\\n\\nbam\\n\\nDam.\\n\\ndam\\n\\nDam.\\n\\ndam\\n\\nDam.\\n\\ndam\\n\\nDam.\\n\\nbam\\n\\nDam.\\n\\ndam\\n\\nDam.\\n\\nbai\\n\\nbai\\n\\nbai\\n\\nbai\\n\\nbai\\n\\nbai\\nbam\\n\\nbam. bai\\n',\n",
|
||
" 'tion divergence as a function of divergence time\\n\\nbt (generators\\n\\nOt (gene ations\\n\\n',\n",
|
||
" 'chr sl e1 chr s2 e2 prob interacted\\n',\n",
|
||
" \"Dear Aman,\\n\\n| am sorry you had a negative experience with the course. We agree that the structure of the course can be improved to give students more time and will do so next semester. However, if you need the credits from the course, | encourage you to try\\n\\ntomorrow. At this point you have nothing to lose, only to win.\\n\\nBest regards\\n\\nNadia\\n\\nProf. Dr. Nadia Kamal\\n\\nTechnische Universitat Minchen | Technical University of Munich\\nSchool of Life Sciences\\n\\nProfessorship for Computational Plant Biology\\n\\n‘Am Staudengarten 2, 85354 Freising, Germany\\n\\nE-Mail: n.kamal@tum.de\\nTelefon | phone: +49 8161 71 5301\\nWebseite | website: www.mis.'s.tum.de/epb\\n\\nSekretariat | secretariat : sekretariat.cpb@Is.tum.de\\n\\nAman Shamil Nalakath\\n56 PM\\n\\nYesterday, 6:\\nNadia Kamal v\\n\\nDear Prof. Kamal,\\n\\n® Reply all | v\\n\\n| am writing this email to inform you that | am deregistering from the exam for this module. While | have the results, | am not sure | really understand what they mean. It would've been best if there was more time to review, but |\\n\\nunderstand the limitations with block courses. Nonetheless, it was a valuable experience for which | am grateful.\\n\\nAdditionally, | applied for a PhD application in the lab previously, but after reflecting on my experience during the course, | have realized that it may not be the best fit and would like to withdraw my application.\\n\\nThank you again for your time and guidance.\\n\\nBest Regards,\\nAman\\n\",\n",
|
||
" 'J]\\n\\na]\\n\\n#Commenting this out to remove the long\\n\\n#conda activate aman_prokka\\n\\n#for i in xfilt.vcf; do\\n\\n# vcftools --vcf $i --FILTER-summary\\n#done\\n\\nresult. Command run successfully\\n\\n--out pass_output_$i\\n\\n#Commenting this out to remove the long\\n#for i in xfail.vcf; do\\n\\n# vcftools --vcf $i --FILTER-summary\\n#done\\n\\n#1s -ltrh\\n\\nresult. Command run successfully\\n\\n--out fail_output_$i\\n\\n',\n",
|
||
" 'a]\\n\\nFor creating intra chromosomal matrices\\n\\nUsed the build_matrix command in HiCPro scripts\\n\\nls /mnt/storage3/aman/20000/matrix\\n\\ncat /mnt/storage3/aman/20000/matrix/command_used. txt\\n\\n#No.of intra. chr. matrices\\n\\nls /mnt/storage3/aman/20000/matrix/*.matrix | we -l\\n\\nchri@_chri@.matrix chr2_chr2_ord.bed\\nchri@_chr1@_abs.bed chr3_chr3.matrix\\nchri@_chr1@_ord.bed chr3_chr3_abs.bed\\nchri_chri.matrix chr3_chr3_ord.bed\\nchri_chri1_abs. bed chr4_chr4. matrix\\nchri_chri_ord. bed chr4_chr4_abs.bed\\nchr2_chr2.matrix chr4_chr4_ord.bed\\nchr2_chr2_abs.bed chr5_chr5.matrix\\n\\ncommand used for generating - /mnt/storage3/aman/HiC-Pro-master/scripts/build matrix —-binsize 15000\\n\\n10\\n\\nchr5_chr5_abs.bed\\nchr5_chr5_ord.bed\\nchr6_chr6.matrix\\n\\nchr6_chr6_abs.bed\\nchr6_chr6_ord.bed\\nchr7_chr7.matrix\\n\\nchr7_chr7_abs.bed\\nchr7_chr7_ord.bed\\n\\nchr8_chr8.matrix\\nchr8_chr8_abs.bed\\nchr8_chr8_ord.bed\\nchr9_chr9.matrix\\nchr9_chr9_abs.bed\\nchr9_chr9_ord. bed\\ncommand_used. txt\\n\\n--chrsizes /mnt/storage3/aman/lat_chrom.sizes\\n\\n--ifile /mnt/storage3/aman/hic_results/hic_results/data/data/data.allValidPairs\\n\\n--oprefix chr9_chr9\\n\\nbash\\n\\n--chrA 9 --chr\\n',\n",
|
||
" 'The intersection point of the two perpendicular lines is determined using their equations:\\n\\n1. Perpendicular line from the first line:\\n\\ny= Moperpendicular (Z _ Zmia) q\\n\\n2. Perpendicular line from the second line:\\n\\ny =m, (2 —-\\n\\n‘perpendicular\\n\\nTo find the intersection point:\\n\\nBina)\\n\\nr Ymid\\n\\nI\\nT Ymid\\n\\n_pe , !\\nMoperpendicular(® — mid) + Ymid = ™Mperpendicular (@ — Bina) + Yinia\\n\\nSolve for z:\\n\\nI ul J\\n(TMperpendicular * mid — Yinid) — (perpendicular * Dinid — Yinia)\\n\\nx\\n\\n; _ m!\\n™Mperpendicular — perpendicular\\n\\nThen substitute z into either equation to find y.\\n',\n",
|
||
" '[25]:\\n\\nSum of alternate and reference allele frequencies (first 5):\\n[Q.\\n\\nQ.5 Q. ]\\n[0. 40833333 0.09166667 Q. ]\\n\\n[0.45 0.05 0. ]\\n[0.48333333 0.01666667 Q. ]]\\n\\nHn vd\\n\\n[[e. @.5 0.\\n\\n',\n",
|
||
" 'HiGlass About Blog Examples Plugins Docs ©\\n\\n5e+4 FA 40 -\\n2e+4\\nte+4 20\\n5e+3 40\\n2e+3\\n1e+3 S\\n5e+2 4\\n2e+2 2\\nte+2 =\\n50= = qomts\\nchr5_chr5.mcool c1c2.mcool\\n[Current data resolution: 5.12M], [Current data resolution: 20k],\\n349= 5e+4\\n2e+4\\n\\\\ te+4\\n2 5e+3\\n2e+3\\n1et3\\n5e+2\\n2er2 =\\n1- = - -\\nc1c9.mcool chr9_chr9.mcool\\n\\n[Current data resolution: 20k], [Current data resolution: 5.12M],\\n\\n',\n",
|
||
" '',\n",
|
||
" 'import numpy as np\\nweights = clr.bins()[:][\\'weight\\'].values\\nprint(f\"Valid weights: {np.sum(~np.isnan(weights) )}/{len(weights) }\")\\n\\n2] Y 0.0s\\n\\nValid weights: 2769/3103\\n',\n",
|
||
" '2. scRepertoire on patient 3\\n\\nIn [367]: library(scRepertoire)\\nS1 <- read.delim(\"/home/rstudio/run070/run070-nsclc-3_VDJ_Dominant_Contigs_AIRR.tsv\", sep = \"\\\\t\", header = TRUE, str\\n\\ncontig_list <- list(S1)\\ncontig. list <- loadContigs(input = S1,\\nformat = \"AIRR\")\\n',\n",
|
||
" 'zcat ~/fihic_bias/fithic_res/FitHiC.spline_pass1.res20000.significances.txt.gz | head\\n\\nchri fragmentMid1 chr2 fragmentMid2 contactCount p-value q-value biasl1 bias2 ExpCC\\n\\n1 10000 «1 30000 1 1. 000000e+00 1.000000e+00 1. 000000e+00 1. 000000e+00 26.440884\\n1 10000 «1 2470000 1 3.096889e-01 1.000000e+00 1. 000000e+00 1. 000000e+00 @.370613\\n1 10000 «1 3070000 1 2.609742e-01 1.000000e+00 1. 000000e+00 1. 000000e+00 @.302423\\n1 10000 «1 3130000 1 2.569911e-01 1.000000e+00 1. 000000e+00 1. 000000e+00 @.297047\\n1 10000 «1 6270000 1 1.502626e-01 1.000000e+00 1. 000000e+00 1. 000000e+00 @.162828\\n1 10000 «1 7470000 1 1.350215e-01 1.000000e+00 1. 000000e+00 1. 000000e+00 @.145051\\n1 10000 «1 8790000 1 1.343277e-01 1.000000e+00 1. 000000e+00 1. 000000e+00 @.144249\\n1 10000 «61 20830000 1 8.989326e-02 1. 000000e+00 1. 000000e+00 1. 000000e+00 @.094193\\n1 10000 «61 24010000 1 6.377743e-02 1. 000000e+00 1. 000000e+00 1. 000000e+00 @.065902\\n',\n",
|
||
" 'Gibberellin biosynthesis is well understood TUT\\n\\ncore\\nSS\\n) The “green revolution”\\nsemidwarf1 rice variety is\\naf mutated in a GA20ox that is\\nexpressed in shoots but not\\n\\nGAg\\n\\na\\n\\n1\\ni 5, ¢ ot — ent-kaurenoic acid in reproductive tissues, ; Q\\ni | t Ga2q GA. leading to increased grain |\\ni GA GAs . yields.\\na; —_—_\\n5A200 .\\n\\\\ GA _ Sasaki ef al. & Matsuoka, 2002, Nature\\nA > GA, —+ GA, —+> GAy, Spielmeyer et al. & Chandler, 2002, PNAS\\n”\\n\\n4 Ce Gazal\\n\\nCA» ——> GA,\\n\\n~\\n\\nGA30x\\n\\nGA\\n\\nGAs ——® GA\\n\\nBrigitte Poppenberger (TUM) Hernandez-Garcia et al & Blazquez, 2021, Sem. Cell Dev. Biol 13\\n\\n',\n",
|
||
" '—-2025-04-16 12:39:40-- https: //wwwuser.gwdguser.de/~txie/polycomb/nadine_micro/C_ctcfbed.pdf\\n\\nResolving wwwuser.gwdguser.de (wwwuser.gwdguser.de)... 134.76.203.116\\nConnecting to wwwuser.gwdguser.de (wwwuser.gwdguser.de) |134.76.203.116|:443... connected.\\nHTTP request sent, awaiting response... 403 Forbidden\\n\\n2025-04-16 12:39:40 ERROR 403: Forbidden.\\n',\n",
|
||
" '(a)\\n\\nCryt\\n\\nCrytt\\nCry2\\n\\nCry3\\n\\nCry4\\n\\nCrys\\n\\nCryé\\n\\nCry?\\n\\nCry@\\n\\nCryo\\n\\nCry10\\nCryt1\\nCry12\\nCry13\\nCryt4\\nCryis\\nCry6\\nCry17\\nCry18\\nCry19\\nCry20\\nCry21\\nCry22\\nCry23\\nCry24\\nCry25\\nCry26\\nCry27\\nCry28\\nCry29\\nCry30\\nCry31\\n\\nBlock\\n\\nDomain |\\n\\nDomain I! Domain til\\n\\na al\\n\\n1 2\\n\\n34\\n\\n5\\n\\nCry-Sequences\\n\\n100 amino acids\\noe)\\n\\n5 conserved areas (some with long C-terminal part)\\n\\nN-/C-terminus processed (activated toxin)\\n',\n",
|
||
" 'In\\n\\n[117]:\\n\\ncombined_TCR <- combineTCR(list(combined.TCR_p3, combined. TCR_p4) )\\n\\nError in (function (...,\\n\\ner of rows: 2805, 2893, 1328, 1278, 6942, 2747, 8991, 201\\nTraceback:\\n\\n1.\\n2.\\n\\nNounw\\n\\n4\\n\\n» checkContigs(input.data)\\nlapply(seq_len(length(df)), function(x) {\\ndf[[x]] <- if (!is.data.frame(df[[x]]))\\nas. data. frame(df[[x]])\\nelse df[[x]]\\ndf {{x]] [df {[x]] == \"\"] <- NA\\ndf {[x]]\\n\\n» })\\n\\nFUN(X[[i]], .-+)\\n\\nas. data. frame(df[[x]])\\nas.data. frame. List (df [[x]])\\n\\ndo.call(data. frame, c(x, alis))\\n\\n(function (..., row.names = NULL, check. rows\\nfix.empty.names = TRUE, stringsAsFactors\\n\\nFALSE, check.names = TRUE,\\nFALSE)\\n\\nmuna eo ce te ete,\\n\\nrow.names = NULL, check.rows = FALSE, check.names = TRUE,\\n\\n: arguments imply differing numb\\n',\n",
|
||
" 'What excites you about doing science?\\n\\nWhat excites you about doing science?\\ndo you have? Please describe a past ex\\nyour drive for scientific inquiry. {max 3C\\n',\n",
|
||
" 'nucellus\\n\\nposterior\\nchalaza\\n® .\\n\\nanterior\\nchalaza\\n\\n',\n",
|
||
" '[papantonis1@gwdu101 mustache_results]$\\nBIN1_CHR BIN1_START BIN1_END\\nchr 5510000 5515000 chr1 5610000\\nchr1 5505000 5510000 chr1 5745000\\nchr1 5635000 5640000 chr1 5745000\\nchr1 7665000 7670000 chr1 7750000\\nchr1 7985000 7990000 chr1 8325000\\nchri1 7990000 7995000 chr1 8105000\\nchr1 8020000 8025000 chr1 8310000\\nchri1 8020000 8025000 chr1 8240000\\nchr 8560000 8565000 chr1 8725000\\npapantonis1@gwdu101 mustache_results]$\\npapantonis1@gwdu101 mustache_results]$\\npapantonis1@gwdu101 mustache_results]$\\npapantonis1@gwdu101 mustache_results]$\\npapantonis1@gwdu101 mustache_results]$\\npapantonis1@gwdu101 mustache_results]$\\npapantonis1@gwdu101 mustache_results]$\\n12007 anchor_bed_try2/rbp1_anchors_final\\npapantonis1@gwdu101 mustache_results]$\\n13598 anchor_bed_try2/ctrl_anchors_final\\n\\npapantonis1@gwdu101 mustache_results]$\\n\\n> -a anchor_bed_try2/rbp1_anchors_fina\\n> -b ~/aman/microc_data/nadine_macro/C!\\n> -u > anchor_bed_try2/rbp1_anchors_wi\\n\\npapantonis1@gwdu101 mustache_results]$\\n1153 anchor_bed_try2/rbp1_anchors_with_C\\n\\npapantonis1@gwdu101 mustache_results]$\\n> -a anchor_bed_try2/ctrl_anchors_fina\\n> -b ~/aman/microc_data/nadine_macro/C.\\n> -u > anchor_bed_try2/ctrl_anchors_wi\\n\\n1767 anchor_bed_try2/ctrl_anchors_with_C\\npapantonis1@gwdu101 mustache_results]$\\n> -a anchor_bed_try2/rbp1_anchors_fina\\n> -b ~/aman/microc_data/nadine_macro/Cl\\n> -w 500@ -u > anchor_bed_try2/rbp1_an\\npapantonis1@gwdu101 mustache_results]$\\n> -a anchor_bed_try2/ctrl_anchors_fina\\n> -b ~/aman/microc_data/nadine_macro/C.\\n> -w 50@@ -u > anchor_bed_try2/ctrl_an\\n\\n1833 anchor_bed_try2/rbp1_anchors_near5k\\n\\n2689 anchor_bed_try2/ctrl_anchors_near5k\\npapantonis1@gwdu101 mustache_results]$\\n> -a anchor_bed_try2/rbp1_anchors_fina\\n> -b ~/aman/microc_data/nadine_macro/Cl\\n> -w 10000 -u > anchor_bed_try2/rbp1_a\\npapantonis1@gwdu101 mustache_results]$\\n> -a anchor_bed_try2/ctrl_anchors_fina\\n> -b ~/aman/microc_data/nadine_macro/C.\\n> -w 10000 -u > anchor_bed_try2/ctrl_a\\n\\n2046 anchor_bed_try2/rbp1_anchors_near1®\\n\\n3055 anchor_bed_try2/ctrl_anchors_near1@\\n\\nhead rbp1_loops_5k.bedpe\\nBIN2_CHROMOSOME BIN2_START BIN2_END\\n5615000\\n5750000\\n5750000\\n7755000\\n8330000\\n8110000\\n8315000\\n8245000\\n8730000\\ntail -n +2 rbp1_loops_5k.bedpe | cut -f1-3 > anchor_bed_try2/rbp1_anchor1.bed\\ntail -n +2 rbp1_loops_5k.bedpe | cut -f4-6 > anchor_bed_try2/rbp1_anchor2.bed\\ncat anchor_bed_try2/rbp1_anchor1.bed anchor_bed_try2/rbp1_anchor2.bed | sort -k1,1 -k2,2n | uniq > anchor_bed_try2/rbp1_anchors_final_tab.bed\\ntail -n +2 ctrl_loops_5k.bedpe | cut -f1-3 > anchor_bed_try2/ctrl_anchor1.bed\\ntail -n +2 ctrl_loops_5k.bedpe | cut -f4-6 > anchor_bed_try2/ctrl_anchor2.bed\\ncat anchor_bed_try2/ctrl_anchor1.bed anchor_bed_try2/ctrl_anchor2.bed | sort -k1,1 -k2,2n | uniq > anchor_bed_try2/ctrl_anchors_final_tab.bed\\nwe -l anchor_bed_try2/rbp1_anchors_final_tab.bed\\n\\n_tab.bed\\n\\nwe -l anchor_bed_try2/ctrl_anchors_final_tab.bed\\n_tab.bed\\n\\nbedtools intersect \\\\\\n\\nl_tab.bed \\\\\\nPI_CTCF_seacr_top@.@1.peaks.stringent.bed \\\\\\nth_CTCF.bed\\n\\nwe -l anchor_bed_try2/rbp1_anchors_with_CTCF.bed\\nTCF. bed\\n\\nbedtools intersect \\\\\\n\\nl_tab.bed \\\\\\n_CTCF_seacr_top@.01.peaks.stringent.bed \\\\\\nth_CTCF.bed\\n\\npapantonis1@gwdu101 mustache_results]$ we -l anchor_bed_try2/ctrl_anchors_with_CTCF.bed\\n\\nTCF. bed\\n\\nbedtools window \\\\\\n\\nl_tab.bed \\\\\\nPI_CTCF_seacr_top@.@1.peaks.stringent.bed \\\\\\nchors_near5kb_CTCF.bed\\n\\nbedtools window \\\\\\n\\nl_tab.bed \\\\\\n_CTCF_seacr_top@.01.peaks.stringent.bed \\\\\\nchors_near5kb_CTCF.bed\\n\\npapantonis1@gwdu101 mustache_results]$ wc -1 anchor_bed_try2/rbp1_anchors_near5kb_CTCF.bed\\n\\nb_CTCF.bed\\n\\npapantonis1@gwdu101 mustache_results]$ wc -1 anchor_bed_try2/ctrl_anchors_near5kb_CTCF.bed\\n\\nb_CTCF.bed\\n\\nbedtools window \\\\\\n\\nl_tab.bed \\\\\\nPI_CTCF_seacr_top@.@1.peaks.stringent.bed \\\\\\nnchors_near1@kb_CTCF.bed\\n\\nbedtools window \\\\\\n\\nl_tab.bed \\\\\\n_CTCF_seacr_top@.01.peaks.stringent.bed \\\\\\nnchors_near1@kb_CTCF.bed\\n\\npapantonis1@gwdu101 mustache_results]$ wc -1 anchor_bed_try2/rbp1_anchors_near1@kb_CTCF.bed\\n\\nkb_CTCF.bed\\n\\npapantonis1@gwdu101 mustache_results]$ wc -1 anchor_bed_try2/ctrl_anchors_neari@kb_CTCF.bed\\n\\nkb_CTCF.bed\\n',\n",
|
||
" 'H9K27me9 HiChIP Hakéme HIChIP\\n\\nA\\n\\nH\\n§\\n\\n‘Chromosome position\\nHakémes HiChIP\\n9:156.6-156.9 Mb\\nHake7mes HichIP\\n4:10,8-113 ND\\n\\n8:195,9-196.1 Mo\\n\\nA f\\ng\\n\\nZl é ‘Haxdmes HChIP é Moxa HICH\\n\\nbatinarst S10 sete g * cee\\nb 3 3 10\\n* Bs\\noo ooo won | Il i iF\\n28 oo} #1 fo piaas Bo ee\\nu ai. to 538 $4 5 58\\nBE wo jl Low edges per gACA Loop eden per ston\\nuy a dd :\\nBg 20 a \\' 60\\na8 I a Dake oo =.\\ngs ole Te woe 3k ebso sae\\n\\ns 2 A\\n3 = pre eal\\n\\nzeal ia 0. 30 —_—e—eeeeoeee\\n\\nLeaf HC HoKimes —-HaK27med a sf\\nHCH HIChIP oe\\n\\n2\\n=\\n\\nExpression of genes in\\n‘dACR-gone loops (1o9,gFPKM)\\n\\nEnrichment of TF on both\\n(04906 of loop (ogy? vale))\\n\\nPercentage of aGTL-gone pairs\\n‘overlapping dACR-gene loops\\n\\nficate dACR-gene contacts. b, Percentage of intergenic-gene loop edges overlapping dACRs. The asterisks denote P-<2.2x10\"\\ntest, two-sided). Leaf Hi-C, n=1,177 total loops (within a single biological replicate); H3K4me3 HiChIP, n= 24,141; and H3K27me3 HiChIP, n:\\n¢, Representative regions containing various HiChIP loops (top panel) and called loop numbers from Hi-C and HiChIP experiments (bottom panel). Grey\\ncurves indicate intergenic-intergenic interactions, pink curves indicate intergenic-gene interactions and green curves indicate gene-gene interactions.\\n\\nde, Regions demonstrating ACR interaction hubs (dACR anchors indicated by shaded pale blue regions in the upper panel). White squares in heat\\n\\nmaps indicate loops. Yellow curves indicate dACR-gene loops and grey curves indicate intergenic-intergenic loops. f.g, Percentages of dACRs involved\\n\\nin multiple dACR-gene loops compared to a control of shuffled dACRs and loops. From a total of 6,939 dACRs (excluding the transcribed group dACRs),\\n2,809 dACRs looped with one or more genes in H3K4me3-HIChIP (f) and 2,001 dACRs looped with one or more genes in H3K27me3-HIChIP (g).h, The\\npercentages of dACR-gene loops in which the dACR resides either upstream or downstream of the target gene\\'s promoter. dACR-gene pairs that were\\nnot crossing gene(s) were used for the analysis. i, Virtual circularized chromosome conformation capture intrachromosomal interaction signals at dACR\\nsummits and flanking regions. j, Top panel: a representative eQTL-gene pair (black curve) connected to Hi-C/HIChIP loops (red curves). Bottom panel:\\nthe percentage of eQTL-gene pairs that were connected by loops (red line) compared to genomic-distance-constrained dACR-gene random permutations\\n(blue dots). P values were determined by a two-sided permutation test (n= 100). k, Enrichment of DAP-seq peaks of the same TF in both edges of the\\nsame loop (dACR-gene loops only). The red line indicates P=0.01 (Fisher\\'s exact test, two-sided). bZIP, basic leucine zipper; EREB, ethylene responsive\\nelement binding; LBD, lateral organ boundaries domain; SBP, squamosa-promoter binding protein. I, Expression of genes involved in different dACR-gene\\nloops, separated by HiChIP loop type (n=number of genes shown in the violin distribution). The box plot shows median and quartiles. For the Hi-C and\\nHIChIP experiments in this figure, biological replicates were not performed. FPKM, fragments per kb of transcript per million mapped reads.\\n\\n',\n",
|
||
" 'nFeature_RNA nCount_RNA percent.mt\\n\\n6000\\n\\n2000\\n\\n4000\\n\\n1000\\n2000\\n\\nIdentity Identity Identity\\n',\n",
|
||
" '[6]\\n\\ncount_data <- read.csv(\"/mnt/volume/data/group8/kallisto_output/gene_counts.csv\", row.names = 1)\\n# Ensure that the sample names in the metadata match the column names in the count matrix\\nrownames(col_data) <- col_data$Run\\n\\ncount_data <- count_data[, rownames(col_data), drop = FALSE]\\n\\n# Convert relevant columns to factors\\ncol_data$strain <- as. factor(col_data$strain)\\ncol_data$stress <- as.factor(col_data$stress)\\n\\n# Align ‘abundance_data\\' with \\'count_data\\' to ensure they have the same genes in the same order\\ncommon_genes <~ intersect(rownames(count_data), rownames(abundance_data) )\\n\\ncount_data <- count_data[common_genes, ]\\n\\nabundance_data <- abundance_data[common_genes, 1]\\n\\n# Identify genes that are expressed above the threshold in at least 3 samples\\nkeep <- rowSums(abundance_data > low_expression_threshold) >= sample_threshold\\n\\n# Filter \\'count_data\\' to retain only the genes that meet the criteria\\ncount_data <- count_data[keep, ]\\n\\n# Display the first few rows of the filtered count data\\nhead(count_data)\\n\\n@ 08s\\n\\nError in *[.data.frame*(count_data, , rownames(col_data), drop = FALSE): undefined columns selected\\nTraceback:\\n\\n1. *[.data.frame*(count_data, , rownames(col_data), drop = FALSE)\\n2. stop(\"undefined columns selected\")\\n\\n3. «handleSimpleError(function (cnd)\\n\\naf\\n\\n. watcher$capture_plot_and_output()\\n\\n. cnd <- sanitize_call(cnd)\\n\\n. watcher$push(cnd)\\n\\n. switch(on_error, continue = invokeRestart(\"eval_continue\"),\\n. stop = invokeRestart(\"eval_stop\"), error = NULL)\\n\\n. }, \"undefined columns selected\", base::quote(*[.data. frame (count_data,\\n. » rownames(col_data), drop = FALSE)))\\n\\n',\n",
|
||
" 'In [17]: print(np.array(gt_h[:5, :5]))\\n\\nIn [27]: allelcounts = gt_h.count_alleles()\\nalleliscounts = gt_h.count_called()\\nallelmcounts = gt_h.count_missing()\\n\\nIn [28]: print(np.array(allelcounts) )\\nprint(np.array(alleliscounts) )\\nprint(np.array(allelmcounts) )\\n\\n[[ 0 30 @]\\n[ @ 33 @]\\n[29 6 @]\\n[18 10 0]\\n[15 9 @]\\n[11 6 01]\\n13836196\\n\\n14797424\\n',\n",
|
||
" 'Merge objects (with integration)\\n\\nSee introduction to integration for more information.\\n\\nmerged_obj <- merge(x = ifnb_list$CTRL, y = ifnb_list$STIM)\\nmerged_obj <- NormalizeData(merged_obj)\\nmerged_obj <- FindVariableFeatures(merged_obj)\\nmerged_obj <- ScaleData(merged_obj)\\nmerged_obj <- RunPCA(merged_obj)\\nmerged_obj <- IntegrateLayers(object = obj, method = RPCAIntegration, orig.reduction = \"pca\", ne\\nw. reduction = \"integrated.rpca\",\\nverbose = FALSE)\\n\\n# now that integration is complete, rejoin layers\\nmerged_obj[[\"RNA\"]] <- JoinLayers(merged_obj)\\n',\n",
|
||
" \"@ Vivaldi\\n\\nat)\\n\\nv Speed Dial\\n\\nFile Edit View Bookmarks\\n\\nS\\n\\n&% Workspaces v\\n\\n—- > a U8\\n\\nv Imported From... YY Imported From...\\n\\n© sic all pats\\n\\nSources\\n\\n+ Add source\\n\\nSelect all sources\\n\\n]\\n\\nFormulae.pdf\\n\\nLecture O.pdf\\n\\nLecture 1.pdf\\n\\nLecture 1_1.pdf\\n\\nLecture 2.pdf\\n\\nLecture 3.pdf\\n\\nLecture 4.pdf\\n\\nLecture 5.pdf\\n\\nLecture 6.pdf\\n\\n0 @ 0 ®\\n\\nMail\\n\\n@ Cool Guides\\n\\ngoogle.com\\n\\nOnline Bewerbung QGIS API Docume...\\n\\nOo\\n\\nTools Window Help\\n\\nStartpage Search Results Kurs: Evolutionsgenetik de § NotebookLM\\n\\nom {fl v\\n\\nqgis - Trying to pe... New Script - Earth...\\n\\nChat @ Refresh\\n\\nexplain more about the following as per the sources\\n- 1. The change in allele frequency due to selection\\n2. The probability of fixation for an allele with\\nselection advantage and the time to fixation 3. The\\nequilibrium frequencies for both alleles in\\noverdominance\\n\\nHere's more information about allele frequency\\nchange due to selection, the probability and time to\\nfixation of an allele with a selective advantage, and\\nequilibrium frequencies in overdominance, based on\\nthe provided sources:\\n1. The change in allele frequency due to selection\\n* General principle Natural selection causes\\nchanges in allele frequencies from one generation\\n.. . Fitter individuals produce more\\nviable offspring, influencing future generations 3.\\n\\nto the next 1\\n\\n- Fisher's theorem The fitness of the population is\\n\\nmaximized 4\\n\\n14 sources\\n\\nStart typing...\\n\\n°©@\\n\\nDescribe Wright-Fisher model assumptions abou >\\n\\nPastebin.com - #1...\\n\\nSo\\nte)\\n\\n$vertr? 6 8B &@ SB FQ\\n\\n® EvolG all pdfs - Notebook BAKSMMaTi ela dale most rel is\\n\\nQ: Search Startpage v\\n\\nTargetP 2.0-DTU... https://www.mood... OnePlus 12R revie... Who is “Indian” in ...\\n\\n«~ Share 3 Settings\\n\\nStudio > Note a\\n\\nGenomic Signatures of Natural Selection\\n\\n(Saved responses are view only)\\n\\na reduction in variation [9].\\n2. Detecting Selection: Neutrality Tests\\n\\n- Neutrality tests compare observed data to theoretical\\nexpectations under a neutral model (where alleles are not under\\nselection) to detect if selection has occurred [10].\\n\\n* Comparing multiple loci across the genome: Because selection\\nacts on few genes, loci under selection should be outliers when\\ncompared to other parts of the genome [10, 11].\\n\\n* Tajima's D: This test compares two estimators of the population\\nmutation rate, 6 (theta). Tajima's D will be:\\n\\n* Negative when there is an excess of rare alleles, suggesting a\\nrecent selective sweep or population expansion [5, 12].\\n\\n+ Positive when there is an excess of intermediate-frequency\\nalleles, suggesting balancing selection, population structure,\\nor a recent bottleneck [8, 9, 12].\\n\\n* Zero when a population is in equilibrium, and evolving\\nneutrally [12].\\n\\n* McDonald-Kreitman (MK) test: Compares the ratio of non-\\nsynonymous (amino acid changing) to synonymous (silent)\\nsubstitutions within a species and between species. A higher ratio\\nbetween species compared to within species suggests positive\\nselection, and a lower ratio suggests purifying selection [13, 14].\\n\\n* Site Frequency Spectrum (SFS): The distribution of allele\\nfrequencies in a sample. Selection leaves specific sianatures in the\\n\\nG) Convert to source\\n\\nNotebookLM can be inaccurate; please double check its responses.\\n\\n+\\nQ\\n\\nQC) Co reset —O—— 100 %\\n\\nc\\n\\nvA\\n\\nMon Feb 10 16:00\\n\\naw\\n\\nv\\n\\nQD\\n\\nee\\n\\nQu qa\\n\\nOH O®e< GO 8W OW A\\n\\n&\\n\\n16:00\\n\\nO © HD\\n\\n”\\n\",\n",
|
||
" 'In\\n\\n[293]:\\n\\ncombined_TCR <- combineTCR(\\nlist(patient3 = combined.TCR_p3, patient4 = combined.TCR_p4),\\nsamples = c(\"patient3\", \"patient4\"),\\nremoveNA = FALSE,\\nremoveMulti = FALSE,\\nfilterMulti = FALSE\\n)\\n\\nError in (function (..., row.names = NULL, check.rows = FALSE, check.names = TRUE,\\n\\ner of rows: 2805, 2893, 1328, 1278, 6942, 2747, 8991, 201\\nTraceback:\\n\\n1. .checkContigs(input.data)\\n\\n2. lapply(seq_len(length(df)), function(x) {\\n\\ndf[[x]] <- if (!is.data.frame(df[[x]]))\\nas.data. frame(df[[x]])\\n\\nelse df[[x]]\\n\\ndf {{x]] [df {{[x]] == \"\"] <- NA\\n\\ndf {[x]]\\n\\n- i)\\n\\nFUN(X[[i]], .-.)\\n\\nas. data. frame(df[[x]])\\n\\nas.data. frame. List (df[[x]])\\n\\ndo.call(data. frame, c(x, alis))\\n\\n(function (..., row.names = NULL, check. rows\\n\\no fix.empty.names = TRUE, stringsAsFactors\\n\\n-{\\n\\nNounw\\n\\nFALSE)\\n\\nue\\n\\nee\\n\\nFALSE, check.names = TRUE,\\n\\n=I\\n: arguments imply differing numb\\n',\n",
|
||
" \"Thesis register (external)\\n\\nYOUR NAME (FIRST NAME, LAST NAME)\\n\\n‘Aman Shamil Nalakath\\n\\nYOUR MATRICULATION NUMBER\\n\\nDEGREE PROGRAM\\n\\nMaster - Agricultural Biosciences Vv\\n\\nPRELIMINARY TITLE IN GERMAN (EXCEPT FOR ENGLISCH DEGREE PROGRAMS A GERMAN TITLE IS.\\nMANDATORY FOR THESIS REGISTRATION)\\n\\n‘The na! te wa be requested again when uploading the esis Please pay terion to paling, las ete when uploading, PLEASE CHECK.\\n\\nBI US x x*| Bsource Q\\n\\nPa\\na\\na\\n\\n“This CKEdior 4.14.0 version snot secu\\n\\nConsider 14.25.15\\n\\nPRELIMINARY TITLE IN ENGLISH (MANDATORY FOR THESIS REGISTRATION)\\n\\n‘The na! te wa be requested again when uploading the esis Please pay lniion to paling, las ete when uploading, PLEASE CHECK.\\n\\nBI US x x*| Bsource Q\\n\\nPa\\na\\na\\n\\n“This CKEdior 4.14.0 version snot secu\\n\\nConsider 14.25.15\\n\\nORGANIZATION / CHAIR INTERNAL.\\n\\n‘Assistant Professorship of Population Epigenalics and Epigenomics Vv\\n\\nORGANIZATION / CHAIR INTERNAL (IF NOT AVAILABLE ABOVE.)\\n\\n1. EXAMINER INTERNAL,\\n\\n‘Johannes, Frank| Prof Dr Vv\\n\\nIf your 1. examiner isnot incuded inthe list, please contact the Campus Ofice examination team,\\n\\n2. EXAMINER INTERNAL\\n\\nPleace select Vv\\n\\n‘Asecond examiners only required forthe folowing degree programs:\\n\\n' Bachelor Foresty Scionce and Resource Management (f you stared your degree before 01.10.2028; i you started your\\ndegree in the winter semester 2028 or later only one examiner is required),\\n\\n‘+ Master Forestry and Wood Science (i you started your degree before 01.10.2022; if you started your degree in the winter\\n‘Semester 2022 or later only one examiner is required),\\n\\n[Altention: In the Master Biology and Master Molecular Blolachnology degree programs, the second examiner ofthe WEP is NOT\\n‘automatically the second examiner ofthe thesis and therefore does not have to be entered here!\\n\\nORGANIZATION / CHAIR EXTERNAL\\n\\nSUPERVISOR EXTERNAL\\n\\n\",\n",
|
||
" 'Solving environment: ...working... INFO conda.cc\\nINFO conda.conda_libmamba_solver.solver:_solve_é\\n{\\n\\n\"INSTALL\": [\\n\\n\"hicexplorer\"\\n\\n]\\n}\\ninfo libmamba Parsing MatchSpec hicexplorer\\ninfo libmamba Parsing MatchSpec hicexplorer\\ninfo libmamba Adding job: hicexplorer\\n',\n",
|
||
" '# SplitDotPlotGG has been replaced with the “split.by’ parameter for DotPlot\\nDotPlot(pbmc3k. final, features = features, split.by = \"groups\") + RotatedAxis()\\n\\nPlatelet_group2\\nPlatelet_group1l\\nDC_group2\\nDC_group1\\nNK_group2\\nNK_group1\\n\\nFCGR3A+ Mono_group2\\nPercent Expressed\\n\\nry FCGR3A+ Mono_group1l\\n\\n5 CD8 T_group2 . os\\n\\nv CD8 T_groupl @ 50\\n\\noe B_group2 4 0\\n\\nB_groupl\\n\\nCD14+ Mono_group2\\nCD14+ Mono_group1l\\nMemory CD4 T_group2\\nMemory CD4 T_group1\\nNaive CD4 T_group2\\nNaive CD4 T_groupl\\n\\nFeatures\\n',\n",
|
||
" 'Sample\\n\\nSample\\n\\nSample\\n\\nSample\\n\\nSample\\n\\nSample\\n\\nTotal\\n\\nReads\\n\\n180,416\\n\\n181,818\\n\\n185,642\\n\\n180,607\\n\\n179,506\\n\\nConcordantly 0\\n\\nTimes (%)\\n\\n12.66\\n\\n48.47\\n\\n0.02\\n\\n0.15\\n\\n4.64\\n\\nConcordantly 1\\n\\nTime (%)\\n\\n24.11\\n\\n30.59\\n\\n8.26\\n\\n21.58\\n\\n21.22\\n\\nConcordantly >1\\n\\nTimes (%)\\n\\n63.23\\n\\n20.94\\n\\n91.72\\n\\n78.27\\n\\n74.14\\n\\nOverall\\n\\nAlignment\\n\\nRate (%)\\n\\n97.02\\n\\n69.38\\n\\n100.00\\n\\n99.93\\n\\n98.36\\n',\n",
|
||
" '[pst14@frontend ref_gen]$ head -n 3@ alignment_stats_3.txt | grep *SN | cut -f 2-\\nraw total sequences:\\nfiltered sequences:\\nsequences: 371284\\nis sorted: 1\\n\\n1st fragments: 185642\\nlast fragments: 185642\\n\\nreads\\nreads\\nreads\\n\\nMapped: 371284\\nmapped and paired:\\nunmapped: @\\n\\n371284 # excluding supplementary and secondary reads\\n)\\n\\n371284 # paired-end technology bit set + both mates mapped\\n\\nreads properly paired: 371228 # proper-pair bit set\\n\\nreads paired: 371284 # paired-end technology bit set\\n\\nreads duplicated: (7) # PCR or optical duplicate bit set\\nreads MQ@: 166772 # mapped and MQ=0\\n\\nreads QC failed: ()\\n\\nnon-primary alignments: @\\n\\nsupplementary alignments: 89\\n\\ntotal length: 48168357 # ignores clipping\\n\\ntotal first fragment length: 24092091 # ignores clipping\\n\\ntotal\\nbases\\nbases\\nbases\\nbases\\n\\nlast fragment leng\\nMapped: 48168357\\nmapped (cigar):\\ntrimmed: 0\\nduplicated:\\n\\nth: 24076266 # ignores clipping\\n# ignores clipping\\n48164712 # more accurate\\n\\nQ\\n',\n",
|
||
" '# Read raw table\\ndf <- read. table(\"/usr/users/papantonis1/aman/rnaseq_data/irnaseq_results/all_samples_counts.txt\", header = TRUE, sep = \"\\\\t\", stringsAsFactors = FALSE)\\n\\n# Extract *only* count columns — from column 9 onwards\\ncounts <- df[, 9:ncol(df)]\\n\\n# Rename columns to match samples\\ncolnames(counts) <- c(\"S55\", \"S56\", \"S57\", \"S58\", \"S59\", \"S60\")\\n\\n# Set rownames to gene symbols (or RefSeq if preferred)\\nrownames(counts) <- df$Symbol\\n\\n(1] v 08s\\nDr Dy\\nb counts # this is the cout matrix\\n[2] v Os\\nA data.frame: 14665 x 6\\n\\ns55 S56 S57 $58 $59 s60\\n\\n<int> <int> <int> <int> <int> <int>\\n\\nsccA 88\\n\\nADRB3 0 4 0 0 te) 0\\n\\nABCD1 143 157 668 543 594 516\\n\\nALDOB 0 2 0 0 te) 0\\n\\nAPOC3 6 17 0 0 0 0\\n\\nAPOH 10 te) 0 0 2 0\\n\\nFs ns a a\\n\\nARSB 262 357. 1550 10291512 1141\\n\\nASPA 0 0 0 0 0 0\\n\\nAVPR2 0 te) 0 0 2 0\\n\\nBCHE 2 6 0 0 0 0\\n\\nSERPING1 0 4 4 0 te) 2\\n\\nos 8 COBO BB\\n\\nCD40LG 4 te) 0 0 te) 0\\n\\nCHRNA1 183 161 1 8 2 1\\n\\nCHRNE 63 76 28 9 31 18\\n\\nERCC8 699 958 5415 3907 4895 3732\\n\\nCLCNKB 20 12 0 0 te) 2\\n\\ncums 8 8g 8\\n\\ncP 39 38 2 0 2 0\\n\\nCPOX 266 306 2398 2079 2346 2315\\nCSTB 66 100 478 475 433 455\\n\\nDUOXA2 4 10 0 oO 0 0\\nERVFRD-1 O 4 0 O 2 0\\nCllorf87 2 1 0 oO 0 0\\n\\nABCA2 131 133 184 154 360 294\\nBLOC1S3 4 47 40 65 67 35\\n\\nPATE2 15 2 0 oO 0 0\\nASB18 4 2 2 2 0 0\\nAMTN 0 0 0 oO 0 0\\n\\nRAPH1 728 767 3094 2022 3150 2292\\nDIO1 4 0 0 oO 0 0\\nRFX4 109 132 10 oO 4 4\\nFOXN4 154 149 106 101 131 49\\nZNF543 172 226 276 250 268 226\\nPLA2G4F 238 284 29 37 68 52\\nADAMTSL5S, 19 13 253 212 373 251\\nSLC16A12 335, 328 12 10 2 0\\nC2orf66 49 56 2 oO 2 2\\nATP6V1H 1538 1745 7399 4432 6413 5164\\nWARS1 16 21 13 132 89 72\\nSFXN4 101 108 1187 1097 1402 931\\n\\nRPLP1 127 131 1443 1760 1819 1442\\nPRR27 0 0 0 oO 0 0\\n\\n#-Load libraries\\n\\nlibrary (DESeq2)\\n\\n# beriné sainpte*meauaca\\n\\ncoldata <- data. frame(condition = factor(c(\"AuxinCPI\", “AuxinCPI\", \"CPI\", \"CPI\", \"control\", “control\")))\\nrownames(coldata) <- colnames(counts)\\n\\n# Create DESeq2 dataset\\n\\ndds <- DESeqDataSetFromMatrix(countData = counts,\\ncolData = coldata,\\ndesign = ~ condition)\\n\\nru <> riuyyuus, vtanu = inucy\\n\\n# Transpose: samples as rows, genes as columns\\nmat <- t(assay(rld))\\n\\n# Filter out genes (columns) with zero variance\\nmat_filtered <- mat[, apply(mat, 2, var) != 0]\\n\\nPLBCO) ~ aosuatae rr ameipragas\\n\\npca_df$condition <- coldata$condition\\n\\n# Plot PCA\\n\\nggplot(pca_df, aes(x = PC1, y = PC2, color = condition)) +\\ngeom_point(size = 3) +\\ntheme_minimal()\\n\\nLoading required package: stats4\\n\\nLoading required package: BiocGenerics\\n\\nAttaching package: ‘BiocGenerics’\\n\\nIQR, mad, sd, var, xtabs\\n\\nThe following objects are masked from ‘package:base’:\\n\\nanyDuplicated, aperm, append, as.data.frame, basename, cbind,\\ncolnames, dirname. do.call., duplicated. eyal. eyala. Filter. Find.\\nPosition, rank, rbind, Reduce, rownames, sapply, saveRDS, setdiff,\\ntable, tapply, union, unique, unsplit, which.max, which.min\\n\\nAttaching package: ‘S4Vectors’\\n\\nto use factor(...,levels=...) or relevel() to set this as the reference level\\n\\nOutput is truncated. View as a scrollable element or open in a text editor. Adjust cell output settings...\\n\\n°\\n.\\ncondition\\n. © Auxince!\\ngz? ; conte\\n°\\ner\\n.\\n°\\n200 50 ° so\\nPCL\\nPAR waun voscye Yuta rune caun\\nplotPCA(rld, intgroup = \"condition\"\\n[4] Vv 04s\\nusing ntop=500 top features by variance\\n5 sroup\\n5s © Aucince!\\ngo @ s © contro!\\n\\n# Load libraries\\nlibrary (DESeq2)\\nlibrary (ggplot2)\\n# Define sample metadata\\ncoldata <- data. frame(condition = factor(c(\"AuxinCPI\", “AuxinCPI\", \"CPI\", \"CPI\", \"control\", “control\")))\\nouranee (coldatrlucascelnamac (enunte)\\ndds <- DESeqDataSetFromMatrix(countData = counts,\\ncolData = coldata,\\ndesign = ~ condition)\\n\\n# Apply VST (variance stabilizing transformation)\\nvst_mat <- assay(vst(dds, blind = TRUE))\\n\\n# Trancnnca: camnlac ac rawe manac ac calimne\\n# Remove genes with zero variance\\n\\ngene_vars <- apply(mat, 2, var)\\n\\nmat_filtered <- mat[, gene_vars != @]\\n\\n# PCA\\npca <- prcomp(mat_filtered, scale. = TRUE)\\npca_df <- as.data. frame(pca$x)\\nnea dtérandition <— coldatalmatch(rownames(nea df). rownames(caldata)). \"condition\"\\nggplot(pca_df, aes(x = PC1, y = PC2, color = condition)) +\\ngeom_point(size = 3) +\\ntheme_minimal() +\\nxlab(paste@(\"PC1\\nylab(paste@(\"PC2\\n\\n[11] v 17s\\n\\nround(100 * summary(pca)$importance[2, 1], 1), \"% variance\")) +\\nround(100 * summary(pca)$importance[2, 2], 1), \"% variance\") )\\n\\nit appears that the last variable in the desian formula, ‘condition’,\\nbefore proceeding. for more information, please see the ‘Note on factor levels\\'\\nin vignette(\\'DESeq2\\').\\n\\n°\\n°\\n\\ns condition\\n\\nan © Auxincel\\n\\n& 3 © control\\n\\nae eo\\n\\n9 e\\n\\n30\\ne\\n\\n60\\n\\n50 °\\nPCI: 74.3% variance\\n',\n",
|
||
" 'Ji\\n\\ncondition\\ncondition condition\\n1G condition\\n%6 i oc 16) 16\\n0G\\n15\\n15\\n14\\n14\\n13,\\n12 SS 8\\n\\nhe\\n\\nOz\\nbee\\nLL\\nSLL\\n6LL\\nsez\\n\\n2 3\\nz 8\\na —\\n—_____\\n= = = = = =\\n> 8s 2 2 3 @ B\\nze 8 2 8 &2 3 8B\\n\\n= = ry = =\\n2 = 8 3 3\\n2 a a a 3\\ncondition\\ncondition\\n1G\\nofl\\n15\\n14\\n13\\n12\\n',\n",
|
||
" '',\n",
|
||
" 'In [171]: table(combined_seurat$orig. ident)\\n\\npatient3 patient4\\n3160 323\\n',\n",
|
||
" '19]\\n\\ncount_data <- read.csv(\"/mnt/volume/data/group8/kallisto_output/gene_counts.csv\", row.names = 1)\\n# Ensure that the sample names in the metadata match the column names in the count matrix\\nrownames(col_data) <- col_datasRun\\n\\ncount_data <- count_data[, rownames(col_data), drop = FALSE]\\n\\n# Convert relevant columns to factors\\ncol_datasstrain <- as.factor(col_datastreatment) #\\ncol_data$stress <- as.factor(col_datastime) #\\n\\n# Align ‘abundance_data\\' with ‘count_data\\' to ensure they have the same genes in the same order\\ncommon_genes <- intersect (rownames(count_data), rownames(abundance_data) )\\n\\ncount_data <- count_data[common_genes, }\\n\\nabundance_data <- abundance_data{common_genes, |\\n\\n# Identify genes that are expressed above the threshold in at least 3 samples\\nkeep <- rowSums(abundance_data > low_expression_threshold) >= sample_threshold\\n\\n# Filter ‘count_data\\' to retain only the genes that meet the criteria\\ncount_data <- count_data[keep, ]\\n\\n# Display the first few rows of the filtered count data\\nhead (count_data)\\n\\nv0.38\\nSRR21866470 SRR21866471 SRR21866472 SRR21866473 SRR21866474\\n\\n<dbl> <dbi> <dbi> <dbl> <dbl>\\n\\nHORVU.MOREX.r3.1HG0000030 43.000 5.0000 0.0000 12.000 0.0000\\nHORVU.MOREX.r3.1HG0000040 1450.000 204.0000 559.0000 955.000 192.0000\\nHORVU.MOREX.r3.1HG0000050 1361.930 98.0000 173.0000 333.000 93.0000\\nHORVU.MOREX.r3.1HG0000060 487.233 44.1801 87.6339 174.378 33.2423\\nHORVU.MOREX.r3.1HG0000070 2475,000 250.0000 570.0000 1145.00 149.0000\\n\\nHORVU.MOREX.r3.1HG0000080 643.000 42.0000 83.0000 185.000 46.0000\\n\\nSRR21866475\\n<dbi>\\n\\n2.0000\\n190.0000\\n123.0000\\n36.5383\\n224.0000\\n65.0000\\n\\nSRR21866476\\n<dbi>\\n\\n5.000\\n\\n939.000\\n516.000\\n234.029\\n1092.000\\n385.000\\n\\nA data.frame: 6 x 18\\n\\nSRR21866477\\n<dbl>\\n\\n6.00\\n\\n918.00\\n\\n272.00\\n\\n112.79\\n1443.00\\n432.00\\n\\nSRR21866478\\n<dbl>\\n\\n3\\n\\n638\\n\\n295\\n\\n27\\n\\n301\\n\\n37\\n\\nSRR21866479\\n<dbl>\\n\\n4.000\\n\\n849,000\\n364.988\\n34.000\\n474,000\\n30.000\\n\\nSRR21866480\\n<dbl>\\n\\n0.000\\n\\n221.000\\n61.000\\n\\n36.881\\n\\n176.000\\n46.000\\n\\nSRR21866481\\n<dbl>\\n\\n2.00\\n\\n55.00\\n\\n36.00\\n\\n12.64\\n\\n102.00\\n\\n13.00\\n\\nSRR21866482\\n<dbl>\\n\\n2.0000\\n172.0000\\n104.0000\\n47.7192\\n355.0000\\n29.0000\\n\\nSRR21866483\\n<dbl>\\n\\n2.0000\\n210.0000\\n143.0000\\n44.9645\\n387.0000\\n53.0000\\n\\nSRR21866484\\n<dbi>\\n\\n0.0000\\n54.0000\\n51.0000\\n32.5228\\n106.0000\\n29.0000\\n\\nSRR21866485\\n<dbi>\\n\\n7.0\\n\\n244.0\\n\\n202.0\\n\\n13.9\\n\\n446.0\\n\\n18.0\\n\\nSRR21866486\\n<dbl>\\n\\n31.000\\n1043.000\\n775.000\\n539.985\\n2496.000\\n768.000\\n\\nSRR21866\\n<\\n\\n29\\n\\n934\\n\\n658\\n\\n506\\n\\n2515\\n\\n607\\n',\n",
|
||
" 'Instituto Universitario de Lisboa (ISCTE IUL)\\nUNIVERSIDADE CATOLICA PORTUGUESA\\nUniversidade de Coimbra\\n\\nUniversidade de Evora\\n\\nUniversidade de Lisboa\\n\\nUniversidade do Porto\\n\\nUniversidade Nova de Lisboa\\n',\n",
|
||
" 'ounts\\n\\nea\\n\\n1e+08\\n\\n5e+07\\n\\n0e+00\\n\\nStatistics of Read Pairs Alignment on Restriction Fragments\\n\\nAll Pairs\\n\\nValid 3C Pairs\\ndata\\n\\nInvalid 3C Pairs\\n\\n| Seen |\\n\\nInvalid_pairs\\n\\nValid_interaction_pairs\\n\\nValid_interaction_pairs_FF\\n\\nValid_interaction_pairs_RR\\n\\nValid_interaction_pairs_RF\\n\\nValid_interaction_pairs_FR\\n\\nFiltered_pairs\\n\\nDumped_pairs\\n\\nSelf_Cycle_pairs\\n\\nReligation_pairs\\n\\nSingle-end_pairs\\n\\nDangling_end_pairs\\n\\n',\n",
|
||
" '—_—eo—e NO _—— eS\\n\\n0\\n\\nne — Background Jobs elo B cell e 10\\nLevels: NK_cell T_cells B_cell @ 20\\n> #Idents(zehn_s) ra @ 30\\n> table(zehn_s$SingleR. Labels) =\\n9 o |cells Average Expression\\nB_cell NK_cell T_cells 2 1.0\\n1 3 38\\n> markers <- FindAllMarkers(zehn_s, only.pos = TRUE, min.pct = @.25, logfc.threshold = @.25) NK cell 0.5\\nCalculating cluster NK_cell ~ 0.0\\nCalculating cluster T_cells -0.5\\nCalculating cluster B_cell -1.0\\n\\n> head(markers)\\n\\np_val avg_log2FC pct.1 pct.2 p_val_adj cluster gene\\nHERPUD2 @.0045430282 2.434368 0.654 0.250 1 NK_cell HERPUD2\\nSIK3 @.0055943496 2.136887 0.654 0.188 1 NK_cell SIK3 Features\\nPPIA @.0002798360 1.747111 0.917 0.533 1 T_cells PPIA\\n\\n',\n",
|
||
" 'Bose Bost Boor\\n& § 5\\n3 3 3\\nrs rs 2\\n4 z g\\nEB EB E\\nFoose Boo Boos\\neH roy a ry\\n\\n(800) [stan] (Ena) (1000) (800) [stan (ena) (1000)\\n\\n‘samples — mettylome 16.suvr5_gones — methylome merged WT_All genes — methylome mett_genes ‘samples — mettylome 16.suvr5_gones — methylome merged WT_All genes — methylome mett_genes samples — mothyiome 16 suvi5_gones — methylome merged WT_All genes — methylome_mett_genes\\n',\n",
|
||
" 'Peakachu total: 11870\\n\\nControl loop overlap (+10kb Midpoint)\\n\\nPeakachu Mustache\\n\\nCooldots\\n\\nCooltools-dots total: 8552\\n\\nMustache total: 12973\\n\\nPeakachu total: 10564\\n\\nEED mutant Loop Overlap (+10kb Midpoint)\\n\\nPeakachu Mustache\\n\\nCooldots\\n\\nCooldots total: 7556\\n\\nMustache total: 11399\\n\\n',\n",
|
||
" 'Y Speed Dial v Imported From... Y Imported From... Online Bewerbung QGIS API Docume... qgis - Trying to pe... New Script - Earth... Pastebin.com - #1\\n\\nScience that inspires\\n\\n5 Cell Systems\\n\\nThis journal Journals Publish News &events About Cell Press\\n\\nTOOL - Volume 3, Issue 1, P95-98, July 27,2016 - OpenAccess [RAPP NN aNIN SU)\\n\\nJuicer Provides a One-Click System for Analyzing Loop-\\nResolution Hi-C Experiments\\n\\nAffiliations & Notes V Article Info \\\\V_—_Linked Articles (1) V\\n\\nDownload PDF 33 Cite «Share LA SetAlert © Get Rights [[) Reprints\\n\\nHighlights\\n\\n- Juicer enables users to process terabase scale Hi-C datasets with a single click\\n\\n- Juicer automatically annotates loops and contact domains\\n\\nShow Outline Y\\n\\n- Juicer is available as open source software\\n\\n- Juicer is compatible with multiple cluster operating systems and with Amazon Web Services\\n\\nSummary\\n\\nHi-C experiments explore the 3D structure of the genome, generating terabases of data to create high-resolution\\ncontact maps. Here, we introduce Juicer, an open-source tool for analyzing terabase-scale Hi-C datasets. Juicer\\n\\nhttps://www.cell.com/fulltext/$2405-4712(16)30219-8#fig ‘ound to transform raw sequence data into normalized contact maps\\n= with nna click lbiicar nrnqdiirac 4 hir tila cpantaining epAamnracecad ronntart matrirac at many racnliitinne farilitating\\n',\n",
|
||
" 'Timeline\\n\\nNow - March\\n2025\\n\\nApril - May 2025\\n\\nJanuary ~ April\\n2025\\n\\nMay ~ August\\n2025\\n\\nSeptember 2025\\n\\nOctober —\\n\\nDecember 2025\\n\\nJanuary 2026\\n\\nAction\\n\\nPrepare for PhD\\nApplications\\n\\nStart Thesis\\n\\nApply to PhD Programs\\n\\nThesis Work & Gap\\nManagement\\n\\nFinish Thesis &\\n\\nPrepare for Transition\\n\\nPrepare for PhD Start\\n\\nStart PhD Program\\n\\nDetails\\n\\n~ Research Nordic universities (Uppsala, Aarhus,\\nHelsinki, etc.) and their PhD funding mechanisms.\\n\\n- Draft CV, motivation letter, and research proposal\\ntailored to programs.\\n\\n~ Network with professors/researchers; inquire about\\nupcoming PhD opportunities.\\n\\n- Secure recommendation letters from\\nprofessors/advisors early.\\n\\n~ Begin thesis work; align research with potential PhD\\ntopics.\\n\\n- Ensure your student visa covers the thesis duration.\\n\\n- Submit applications to PhD programs starting in late\\n2025 or early 2026.\\n\\n- Provide interim transcripts and proof of enrollment.\\n\\n- Mention your thesis topic and expected completion\\ndate.\\n\\n- Highlight completed coursework, research\\nexperience, and planned thesis contributions.\\n\\n- Focus on completing the thesis; aim for early\\nsubmission if feasible.\\n\\n- Take up a part-time research assistantship, internship,\\nor remote project to avoid idle time.\\n\\n~ Continue networking with PhD contacts; discuss\\nflexibility in start dates.\\n\\n- Learn a Nordic language (e.g., Swedish, Finnish) if\\nrelevant to your PhD plans.\\n\\n- Submit and defend your thesis.\\n\\n- Obtain an interim or final degree certificate from TUM\\nfor visa and PhD purposes.\\n\\n- Finalize PhD visa application and relocation logistics.\\n\\n- If idle time exists, work on publications, skill\\nupgrades, or short-term academic projects.\\n\\n- Transition smoothly to your PhD in the Nordic country.\\n',\n",
|
||
" 'features = c(B=\\'Ms4a1\\',B=\\'Cd19\\',MM=\\'Cd14\\',MM=\\'Lyz2\\' ,MM=\\'Fcgr3\\',MM=\\'Ms4a7\\' ,MM=\\'Fcer1g\\',MM=\\'Cst3\\',MM=\\'H2-Aa\\',MM=\\'Ly6d\\'\\nrRNA=\\'AY036118\\', rRNA=\\'Gm42418\\' ,Mphase=\\'Cenpa\\' ,Mphase=\\'Ccnb2\\',Mphase=\\'Birc5\\' ,Mphase=\\'Mki67\\',Sphase=\\'Pcna\\',\\nSphase=\\'Mcm3\\', Sphase=\\'Ccne2\\', \\'T\\'=\\'Cd8b\\', \\'T\\'=\\'Cd8a\\', \\'T\\'=\\'Cd4\\', \\'T\\'=\\'Cd3g\\', \\'T\\'=\\'Cd3e\\', \\'T\\'=\\'Cd3d\\')\\n\\n# Convert all gene symbols to uppercase\\nfeatures_upper <- toupper(features)\\n\\n# Now check against the Seurat object\\npresent_features <- features_upper[features_upper %in% rownames(zehn_s) ]\\nmissing_features <- features_upper[!features_upper %in% rownames(zehn_s) ]\\n\\ncat(\"@ Present genes:\\\\n\")\\nprint (present_features)\\n\\ncat(\"\\\\nX Missing genes:\\\\n\")\\nprint (missing_features)\\n\\n# Visualize #co-expression# of two features simultaneously # Yellow = red+green?\\nFeaturePlot(zehn_s, features = present_features[1], blend = TRUE)\\n\\nPresent genes:\\n\\nB MM MM MM Mphase Mphase Mphase- Sphase\\n\"MS4A1\" \"CD14\" \"FCER1G\" \"CST3\" \"CENPA\" \"BIRC5\" \"MKI67\" \"PCNA\"\\nSphase Sphase T T T T T T\\n\"MCM3\" \"CCNE2\" \"CD8B\" \"CD8A\" \"cD4\" \"CD3G\" \"CD3E\" \"CD3D\"\\nXX Missing genes:\\nB MM MM MM MM MM rRNA\\n\"cp19\" \"LYZ2\" “FCGR3\" \"MS4A7\" \"H2-AA\" \"LY6D\" \"AY@36118\"\\nrRNA Mphase\\n\"GM42418\" \"CCNB2\"\\n\\nError in ‘FeaturePlot()>:\\n! Blending feature plots only works with two features\\nTraceback:\\n\\n1. abort(message = \"Blending feature plots only works with two features\")\\n2. signal_abort(cnd, .file)\\n3. signalCondition(cnd)\\n',\n",
|
||
" 'Overall Interpretation\\n\\ne The data show a good proportion of valid Hi-C contacts (17.40%), but a large number of reads\\n(64.87%) are excluded due to low quality (MAPQ). This could be due to sequence complexity,\\n\\ngenome alignment issues, or technical problems during sequencing.\\n\\ne The balance in pair types and dominance of intra-chromosomal contacts indicate proper\\n\\nlibrary preparation and plausible results for downstream analysis.\\n\\ne Long-range contacts provide meaningful insights into chromatin organization and can be\\n\\nused for modeling chromosomal structure.\\n',\n",
|
||
" 'Investigating the Impact of Hexaploidization on Gene Expression in Oat: in this project, we compare gene expression in hexaploid oat\\nspecies with their tetraploid ancestors. The aim is to explore how the addition of a new genome through hybridization has affected gene\\nregulation.\\n',\n",
|
||
" '1. Structural Off-Target Prediction\\n- Use SMAP to scan human structural proteome\\n- Filter metabolic proteins in Recon1\\n- Check overlap between drug-binding and functional sites\\n\\n2. Protein-Ligand Docking\\n- Retrieve/build endogenous substrate structures\\n- Prepare proteins/ligands (AutoDockTools)\\n- Dock with AutoDock Vina\\n- Compare binding energies: drug vs endogenous ligand\\n\\n3. Build Kidney Metabolic Model\\n\\n- Start from Recon! global network\\n- Define renal objective function (literature)\\n- Add missing exchange/transport reactions\\n\\n4. Constrain Kidney Exchanges\\n- Use HMDB metabolomics: allow bloodekidney and kidney-urine exchanges\\n- Set unsupported exchanges to zero\\n- Apply system boundary flux constraint\\n\\nintegrate Gene Expression\\n\\n- Normalize kidney microarray data (GSE803)\\n- Map genes to Recon! reactions\\n\\n- Set expression significance threshold\\n\\nRun GIMME Algorithm\\n\\n- Input: constrained Recon1, gene data, renal objective\\n- Require 290% renal objective flux\\n\\n- Minimize inconsistency with expression\\n\\n- Output: full & reduced kidney model\\n\\nVo\\n\\nimulations\\n\\n- Drug inhibition: set target reaction flux to 0\\n- Run FBA for each renal function\\n\\n- Simulate genetic deficiencies + drug\\n\\n- Identify cryptic genetic risk factors\\n\\n8. Validation\\n- Compare active genes to transcriptomics & proteomics\\n- Cross-validation for recall of high-expression genes\\n- Compare achievable functions to literature\\n- Validate disorder phenotypes vs OMIM\\n\\n9. Sensitivity Analysis\\n- Vary system boundary flux constraint (0-1000)\\n- Vary inhibition degree (0-100% activity)\\n- Check robustness of predictions\\n\\n',\n",
|
||
" '# total or relative numbers of unique clones\\n\\nclonalQuant (combined. TCR_p4,\\n\\ncloneCall=\"strict\",\\n\\n\"both\",\\n)\\n\\nPercent of Unique Clones\\n\\nchain\\nscale\\n\\nor\\nSamples\\n\\nSamples\\n\\nBs\\n',\n",
|
||
" '',\n",
|
||
" \"Scientific interests\\n\\nResearch Interests:\\n\\nDescription: At this stage, which research areas and scientific questions are you most interested in exploring during your PhD? Please describe the techniques and\\nmethods you are currently considering. (min. 100 words - max. 400 words)\\n\\nCURRENT research area (Primary) Computational Biology, Genomes and Evolution\\n\\nScientific Question:\\nClick here to enter your comments (What excites you about doing science?)\\nApplicant's answer:\\n\",\n",
|
||
" 'zcat FitHiC. spline_pass1.res20000.significances.txt.gz | head\\n\\nfragmentMida\\n\\n10000\\n10000\\n10000\\n10000\\n10000\\n10000\\n10000\\n10000\\n10000\\n\\n1\\n\\nchr2\\n30000\\n2470000\\n3070000\\n3130000\\n6270000\\n7470000\\n8790000\\n20830000\\n24010000\\n\\nfragmentMid2 contactCount —_p-va\\n1 1.000000e+00 —1.000000e+00\\n1 3.096889e-01 1. 000000e+00\\n1 2.609742e-01 1. 000000e+00\\n1 2.569911e-01 1. 000000e+00\\n1 1.502626e-01 1. 000000e+00\\n1 1.350215e-01 1. 000000e+00\\n1 1.343277e-01 1. 000000e+00\\n\\n1 8.989326e-02 1.00\\n\\n1 6.377743e-02 1.001\\n\\nInter-chromosomal ones q value <= 0.05\\n\\n#Including the contigs Like 873V4_ctgx\\nzcat FitHiC. spline_pass1.res20000. significances. txt.gz | awk \\'($1\\n\\n2\\n\\n($3 ~ /*[1-91$|*10$ |“B73V4_ctg[1-9] $|~B73V4_ctg10$/) && \\\\\\n\\n($1 != $3) && ($7 <= 0.05)!\\n\\n485\\n\\n| we -U\\n\\n#Not including the contigs Like B73V4_ctgx\\n\\nzcat FitHiC. spline_pass1.res20000.significances.txt.gz | awk \\'($1\\n\\n137\\n\\nUsing q value\\n\\n#Including the contigs Like 873V4_ctgx\\nzcat FitHiC. spline_pass1.res20000. significances. txt.gz | awk \\'($1\\n\\n= 0.15\\n\\n2\\n\\n2\\n\\n($3 ~ /*[1-91$|*10$ |“B73V4_ctg[1-9] $|~B73V4_ctg10$/) && \\\\\\n\\n($1 != $3) && ($7 <= 0.15)!\\n\\n638\\n\\n| we -U\\n\\n#Not including the contigs Like B73V4_ctgx\\n\\nzcat FitHiC. spline_pass1.res20000.significances.txt.gz | awk \\'($1\\n\\n199\\n\\nIntra~-chromosomal ones\\n\\n#Including the contigs Like 873V4_ctgx\\n\\n2\\n\\nlue q-value bias1\\n1.000000e+00\\n1.000000e+00\\n1.000000e+00\\n1.000000e+00\\n1.000000e+00\\n1.000000e+00\\n1.000000e+00\\n\\ne000e+20 © ©— 1. 000000e+00\\ne000e+20 © 1. 000000e+00\\n\\nbias2 —_ Expcc\\n\\n1.000000e+00 26. 44088\\n1.000000e+00 0370613\\n1.000000e+00 0, 302423\\n1.000000e+00 0.297047\\n1.000000e+00 0, 162828,\\n1.000000e+00 0, 145051\\n1.000000e+00 0, 144249\\n1.000000e+00\\n1,000000e+00\\n\\n4\\n\\n9.094193\\n0.065902\\n\\n/*(1-91$|*10$ |“B73V4_ctg [1-9] $|“B73V4_ctg10$/) && \\\\\\n\\n/7(1-91$|*10$/) && ($3 ~ /*[1-91$|*10$/) && ($2\\n\\n$3) 6& ($7\\n\\n0.05)\"\\n\\n/*(1-91$|*10$ |“B73V4_ctg [1-9] $|“B73V4_ctg10$/) && \\\\\\n\\n/7(1-91$|*10$/) && ($3 ~ /*[1-91$|*10$/) && ($2\\n\\n$3) S& ($7 <= 0.15)\"\\n\\n#zcat FitHiC.spline_pass1.res20000.significances.txt.gz | awk ‘($1 ~ /*[1-9]$|*10$|~B73V4_ctg[1-9]$|\"B73V4_ctg10$/) && |\\n#($3 ~ />[1-9]$ |~10$ |\"B73V4_ctg [1-9 ]$|B73V4_ctgl0$/) 6& \\\\\\n\\n#($1 == $3) && ($7 <= 0.05)\"\\n\\n[we -U\\n\\n#Not including the contigs Like B73V4_ctgx\\n\\nzcat FitHiC. spline_pass1.res20000.significances.txt.gz | awk \\'($1~ /*[1-9]$|710$/) && \\\\\\n\\n($3 ~ /711-918|7108/) 66 \\\\\\n\\n(s2\\n89490\\n\\n$3) && ($7 <= 0.05)\"\\n\\n| we -U\\n\\nFiltering the intra chromosomal loops Per chromosome\\n\\nzcat FitHiC. spline_pass1. res20000.significances.txt.gz | awk \\'($1\\n\\n14348\\n5945\\n10496\\n10772\\n10799\\n9160\\n6935\\n7334\\n6818\\n6883\\n\\n1\\n10\\n\\nweyaUuEuNn\\n\\n= $3) && ($1 ~ /*[1-9]1$|%10$/) && ($7 <= 0.05)\"\\n\\n| cut -f1 | sort | unig -\\n',\n",
|
||
" '',\n",
|
||
" 'In [17]: print(np.array(gt_h[:5, :5]))\\n[[-1 -1 -1 1 -1]\\n\\n[-1 1-1 1 -1]\\n[1 ® 1-1 0]\\n[@-1 ® @-1]\\n[@ ®@ ® @ Q]]\\n',\n",
|
||
" '15.\\n15.\\n15.\\n15.\\n15.\\n15.\\n15.\\n15.\\n15.\\n15.\\n\\n78\\n78\\n78\\n78\\n78\\n78\\n8e\\n8@\\n8e\\n8e\\n\\npythons-setuptools pythons-wheel pytnons.10 pytnons.10—-dev\\npython3.1@-minimal readline-common rpcsvc-proto samtools session-migration\\nshared-mime-info systemd systemd-sysv systemd-timesyncd ucf unzip vim\\nvim-common vim-runtime wget x11-common x11-utils x11proto-dev xauth\\nxdg-user-dirs xorg-sgml-doctools xtrans—-dev xxd xz-utils zlibig-dev\\n\\nThe following packages will be upgraded:\\nlibc6é libsystemd@\\n\\n2 upgraded, 288 newly installed, @ to remove and 11 not upgraded.\\n\\nNeed to get 312 MB of archives.\\n\\nAfter this operation, 977 MB of additional disk space will be used.\\n\\nE: You don\\'t have enough free space in /var/cache/apt/archives/.\\n\\n>>> RUN apt-get update && apt-get install -y \\\\\\n\\n>>> build-essential \\\\\\n>>> wget \\\\\\n\\n>>> unzip \\\\\\n\\n>>> bzip2 \\\\\\n\\n>>> gcc \\\\\\n\\n>>> gt+ \\\\\\n\\n>>> openjdk-11-jdk \\\\\\n>>> git \\\\\\n\\n>>> curl \\\\\\n\\n>>> make \\\\\\n\\n>>> ca-certificates \\\\\\n>>> vim \\\\\\n\\n>>> python3 \\\\\\n\\n>>> python3-pip \\\\\\n\\n>>> zlibig-dev \\\\\\n\\n>>> libncurses5-dev \\\\\\n>>> libbz2-dev \\\\\\n\\n>>> liblzma-dev \\\\\\n\\n>>> samtools \\\\\\n\\n>>> locales \\\\\\n\\n>>> && apt-get clean && rm -rf /var/lib/apt/lists/*\\n\\nERROR: failed to solve: process \"/bin/sh -c apt-get update && apt-get install -y\\n\\nes\\n\\nvim python3 python3-pip zlib1g-dev libncurses5-dev libbz2-dev\\n\\nully: exit code: 100\\n\\nbuild-essential\\n\\nliblzma-dev\\n\\nwget\\n\\nView build details: docker-desktop: //dashboard/build/desktop—linux/desktop-—linux/aétrz25acf47qlkc2qiuudoal\\n\\naman@Laptop-von—Aman juicer_hpro %\\n\\nunzip\\nsamtools\\n\\nbzip2\\nlocales\\n\\ngcc gt+ openjdk-11-jdk git curl make ca-certificat\\n&& apt-get clean && rm -rf /var/lib/apt/lists/*\" did not complete successf\\n\\n',\n",
|
||
" 'Expression Level\\n\\nJADE2\\n\\n9N V%\\nIdentity\\n\\nExpression Level\\n\\nDEK\\n\\n9 NV %\\nIdentity\\n\\nExpression Level\\n\\nCDK2AP2\\n\\n9 N V%\\nIdentity\\n',\n",
|
||
" 'After reading the article \"The haplotype-resolved chromosome pairs of a\\nheterozygous diploid African cassava cultivar reveal novel pan-genome\\nand allele-specific transcriptome features”\\n\\n1.Continue working on the Article 1 from previous week reflecting the\\n\\n2. Try to figure out what kinds of sequence data is used in the described\\nresearch\\nexplained in the lecture 1 slides (13-27). Briefly summarize your findings\\n* Your summary can again be a short, written report or one or more\\npresentation slides that describe your thoughts\\n\\nVo = owed to do this exercise in pairs\\n\\nMORE VIDEOS QitAitimertaate\\n\\nCOURSE TOPICS\\n\\noverview\\n\\nSEQUENCIN SEQUENCING\\nDATA. DATA DATA ALIGNMENT\\nQUALITY AND\\n\\nQUALITY\\n\\nCONTROL\\n\\nRNA-SEO, OPCR.\\n\\nMICRO-R, CHIP\\n\\nSEO AND OTHER\\nTECHNIQUES\\n\\nGENOME\\nASSEMBLY\\n\\nGENE\\nEXPRESSION\\nANALYSES\\n\\nASSEMBLY\\nCHALLENGES\\nano oc\\n\\nDIFFERENTIAL\\nEXPRESSION\\n\\nGENOME\\nANNOTATION\\n\\nGENE AND PATHWAY\\nENRICHMENTS\\n\\nGENOME\\nANALYSES\\n\\nSUMMARY OF\\nTHE CLASS\\n\\nata\\n\\nrECH\\n© @ & Youlube +t\\n\\n',\n",
|
||
" 'oom? Adana bX ging\\nPsiasis\\n\\noom: Garant Rear ops lemeses ana Sema$\\nsias0e\\n\\noom:277 Frases tine agg Thay ad Fencton ZH\\n\\nMam earar & Sot rae\\n20:00) PRA apetmen Sani Tuneing Mireapy Aol\\n\\nam ecarae $c Jt\\n(etace Spams a Onan 2 Rete saci\\n\\nooento bmna epee 2\\nvorzsus\\n\\nMamlecnree 2 in, estan\\n\\n‘Poncan came Sasson oon owt\\noom? bare rime a Excremapnts SR\\n\\nmerge aus nse\\n\\noom: Wotens:Conqusorl maging aA ned Came) £3\\n\\nrojssws\\n\\nmeagre\\n\\ntee Sse samme ae\\n\\noom: aa Resonance ape\\n\\nMoin iestrae_ st Foe\\n\\n(ttaee case ct sty rane\\n\\noom mae Patel Couch Leng Medes aan NZI, AM >\\nam enrar ae se\\n\\nTiti swsr Seem seae A utenmas eon Yodo Ue\\n\\noom: ast Sann: Gneaeaton ates one: etn fatten or Mae Gg\\nner mais\\nsezs0s\\n\\nlactane moo Sonew pecs ene\\n\\noom: ain te ypeviad epn a Me ig Tony nd Agtstene\\nmers)\\nsizes\\n\\ncomo eso Antne ening Neumann ADHD MALE)\\nserasne\\n\\nMam enrae_ icine crstin\\nStace win On\\n\\nom: 0) watson Alri Language Made ce aig IU, MAE\\nseas\\nlactaee sce commn ue\\n\\nomo4s ates Dap Luin ferns re ee! aging N27\\n=\\nsia\\n\\noooh Quanta yao fom prs ome nantes\\n\\noom: Race Aavancs a Sle Mlle ag of Potala\\nsrs\\n\\nny mae\\n\\namieorar ee\\nUetaee ost isa Pasa Uae\\n\\noom: same mated agg\\nsire\\n\\nam earar oun. tane\\n\\nUetaee ions Sate & Sou come\\n\\ncet pttin\\n\\ncmt tin\\n\\nnetstat |\\n\\ncet aten\\n\\ncet pin\\n\\ncenter\\n\\ncenter\\n\\ncet aten\\n\\n',\n",
|
||
" '',\n",
|
||
" \"Let M' be the normalized matrix. Then:\\n\\nvbnet\\n\\nThis process is repeated iteratively until:\\n\\nscss\\n\\nsum(M'[i][:]) = constant for all i\\n\",\n",
|
||
" 'H3K27ac bookmarking promotes rapid post-mitotic\\nactivation of the pluripotent stem cell program\\nwithout impacting 3D chromatin reorganization\\n',\n",
|
||
" 'About Blog Examples Plugins Docs ©\\n\\ni no+xX\\nO+X Ser4 ym\\n\\n2e+4\\nte+4\\n5e+3\\n\\n2e+3\\n1e+3\\n5e+2\\n\\n2e+2\\nte+2\\n50 =\\n\\nchr1_chr1.mcool\\n[Current data resolution: 5.12M],\\n',\n",
|
||
" 'In [15]: from sklearn.cluster import KMeans\\n\\n# Perform k-means clustering\\n\\nK = 3. # Modify based on expected populations\\n\\nkmeans = KMeans(n_clusters=K, random_state=42)\\n\\nlabels = kneans.fit_predict (coords) # Assigns clusters to samples\\n\\n# Map clusters to colors\\nimport matplotlib.pyplot as plt\\n\\nplt. figure(figsize=(8, 6))\\n\\nplt-scatter(coords{:,\\'0], coords{:, 1], c=labels, cmap=\\'tab10\", alpha=0.7)\\nplt.title(\\'PCA of Haploid - “H’ Data (Colored by Population) \\')\\n\\nplt.show()\\n\\nPCA of Haploid - \"H” Data (Colored by Population)\\n\\n100\\n\\n$\\n\\n8\\n\\n25\\n\\n15}\\n\\n-100 0-75-5025, 0 2 50 B\\n\\nIn [63]: import pandas as pd\\n\\n# Create a DataFrane with sample names and assigned clusters\\ncluster_data = pd.DataFrame({‘Sample\\': samples, ‘Cluster’: Labels})\\n\\nprint(cluster_data.head(15))\\nprint(*\\\\n\\')\\nprint (cluster_data. tail(15))\\n\\n',\n",
|
||
" '= hh\\n\\n%ssbash\\ntree /mnt/volume/data/group8/references/x. zip\\nfor file in /mnt/volume/data/group8/references/*.zip; do\\nif [[-!-\"$file\"-=~-fasta\\\\.zip$ ]];+then\\nunpigz \"$file\"\\nfi\\ndone\\n\\nvY 0.0s\\n\\nZmnt/volume/data/group8/references/GFA-456c8c45c35aalbf03e9cdd88f428728 fasta.zip [error opening dir\\nZmnt/volume/data/group8/references/GFA-456c8c45c35aalbf03e9cdd88f428728.zip [error opening dir]\\n\\n@ directories, 2 files\\n\\n',\n",
|
||
" 'vvvy\\\\\\n\\nlibrary (CALDER)\\nchrs = paste@(\"chr\", c(1:22, \"X\", \"Y\"))\\nCALDER(contact_file_hic = \"/usr/users/papantonis1/aman/microc_data/nadine_macro/RBP1.hic\",\\nchrs = chrs,\\nbin_size = 25000,\\ngenome = \"hg38\",\\nsave_dir = \"/usr/users/papantonis1/aman/calder_output\",\\nsave_intermediate_data = FALSE,\\nn_cores = 4,\\nsub_domains = TRUE)\\n\\n',\n",
|
||
" 'Genetic context of bacterial aqpN genes TUT\\n\\n44 AQPNsinKEGG (45% in arsenic resistance operons — 55 % in NO operon)\\n\\n57 AQPNs in NCBI (68% in arsenic resistance operons — 32 % in NO operon)\\n\\nAs(V)\\n\\ntransporter\\n\\nAs(ltl)\\n\\n= > > | >> >>>\\nf GipF Aqpz |\\n\\nCrop\\nPhysiology\\n56\\n\\n',\n",
|
||
" 'Case 1: General Case (m 4 0,m # 00)\\n\\nFor one endpoint:\\n\\nZnewl = Lmia + L\\n\\nYnewl = Ymid + ™Mperpendicular * (new1 _ Zmia)\\n\\nFor the second endpoint (opposite direction):\\n\\nZnew2 = Lmia — L\\n\\nYnew2 = Ymid + ™Mperpendicular * (new? _ Zmia)\\n\\nCase 2: Vertical Original Line (m — oo)\\nSince the perpendicular line is horizontal:\\nLnewl = Lmids\\nYnewl = Ymid + L,\\nCase 3: Horizontal Original Line (m = 0)\\nSince the perpendicular line is vertical:\\nYnewl = Ymid>\\n\\nZnewl = Zmia + L,\\n\\nLpew2 = Lmid\\n\\nYnew2 = Ymid — L\\n\\nYnew2 = Ymid\\n\\nZnew2 = Imia — LD\\n',\n",
|
||
" 'git clone https://github. com/ay—lab/mustache\\nconda env create -f ./mustache/environment. yml\\nconda activate mustache\\n',\n",
|
||
" '.. if rainy & wet winters Tu\\nare followed by longer drought periods in spring ...\\n\\n‘Cleanpng.comy/ pag-clous-rain-sterm-clprart-cartoon-chouad-THETRY preime.com/image/L3435\\n\\nspring summer\\n\\nleaching lacking\\nof water & nutrient flux — summer crops\\nnutrients to roots\\n\\nR , + winter-type rapeseed\\nnitrate - boron - sulfate\\n\\nCrop\\nPhysiology\\n16\\n\\n',\n",
|
||
" 'Peroxidase — reaction cycles\\n\\noxidative cycle\\n\\n',\n",
|
||
" '[21]\\n\\n#bcftools\\n\\nfor i in xbowtie.vcf; do\\nbcftools stats \"$i\" | grep \"*SN\"\\necho \"Analysis complete for $i\"\\necho \"\"\\n\\ndone\\n\\n',\n",
|
||
" 'In [83]: merged_seurat\\n\\nAn object of class Seurat\\n\\n42872 features across 3483 samples within 2 assays\\n\\nActive assay: SCT (18998 features, 3000 variable features)\\n3 layers present: counts, data, scale.data\\n\\n1 other assay present: RNA\\n\\n1 dimensional reduction calculated: pca\\n\\nIn [82]: merged_seurat <- IntegrateLayers(\\nobject = merged_seurat,\\nmethod = RPCAIntegration,\\norig. reduction = \"pca\",\\nnew. reduction = “integrated. rpca\",\\nTRUE,\\nRNA\\n\\n)\\n\\nComputing within dataset neighborhoods\\n\\nFinding all pairwise anchors\\n\\nError in UseMethod(generic = \"Assays\", object = object): no applicable method for ‘Assays’ applied to an object of\\nclass \"NULL\"\\nTraceback:\\n\\n1. method(object = object[[assay]], assay = assay, orig = obj.orig,\\n\\n: layers = layers, scale.layer = scale. layer, features = features,\\n\\n2. FindIntegrationAnchors(object.list = object.list, anchor. features = features,\\n\\n: scale = FALSE, reduction = \"rpca\", normalization.method = normalization.method,\\n: dims = dims, k.filter = k.filter, reference = reference,\\n\\n: verbose = verbose, ...)\\n3. pblapply(X = 1:nrow(x = combinations), FUN = anchoring. fxn)\\n4. lapply(X, FUN, ...)\\n5. FUN(X([il],\\n\\n)\\n\\n6. DietSeurat(object = object.list[[il], assays = assay[il, features = anchor. features,\\n: counts = FALSE, scale.data = TRUE, dimreducs = reduction)\\n\\n7. unique(x = unlist(x = lapply(X = Assays(object = object), FUN = function(x) {\\n: return(Layers (object = object[[x]]))\\n\\n»}))\\n8. unlist(x = lapply(X = Assays(object = object), FUN = function(x) {\\n: return(Layers (object = object[[x]]))\\n\\n3)\\n\\n9. lapply(X = Assays(object = object), FUN = function(x) {\\n: return(Layers (object = object[[x]]))\\n\\n» 3)\\n\\n10. Assays(object = object)\\n11. .handleSimpleError(function (cnd)\\nf\\n: watcher$capture_plot_and_output()\\n: cnd <- sanitize_call(cnd)\\n: watcher$push(cnd)\\n: switch(on_error, continue = invokeRestart(\"eval_continue\"),\\n: stop = invokeRestart(\"eval_stop\"), error = NULL)\\n- }, \"no applicable method for \\'Assays\\' applied to an object of class \\\\\"NULL\\\\\"\",\\n: base: :quote(UseMethod(generic = \"Assays\", object = object)))\\n\\n',\n",
|
||
" \"a3) = > QQ. VU 6G euraxess.ec.europa.eu/jobs/382278\\n\\nYou are here: Home > Jobs & Opportunities > Job offer\\n\\nJob offer\\n\\nU nive rsity JOB EXPIRES SOON\\n\\nof An twe rp University of Antwerp | Posted on: 22 October 2025\\n\\nIndustrial PhD in Cancer Multi -omics & Machine Learning\\n\\nDear user, the EURAXESS portal will be offline for maintenance on 13 November 2025, 7:30 - 10:30 CET. We apologize for any inconvenience this may cause. Thank you for\\n\\nyour understanding.\\n\\neo,\\nPAGE CONTENTS hr science4refugees\\n22 OCT 2025\\nJob Information J b | f A\\nob Information\\nOffer Description\\nOrganisation/Company University of Antwerp\\nWhere to apply Research Field Biological sciences » Biology\\n. Biological sciences » Other\\nRequirements .\\nComputer science » Other\\nAdditional Information Researcher Profile First Stage Researcher (R1)\\nWork Location(s) Country Belgium\\nContact Application Deadline 20 Nov 2025 - 22:59 (UTC)\\nType of Contract Temporary\\nContact Job Status Full-time\\nIs the job funded through Not funded by a EU programme\\nthe EU Research\\nContact Framework Programme?\\nIs the Job related to staff No\\nposition within a Research\\nInfrastructure?\\nContact\\nOffer Description\\nContact Let’s shape the future - University of Antwerp\\nThe University of Antwerp (% is a dynamic, forward-thinking university. We offer an innovative academic education to more than 20,000\\nstudents; conduct pioneering scientific research and play an important service-providing role in society. With more than 6000 employees from\\nContact 100 different countries, we are helping to build tomorrow's world every day. Together we push back boundaries and set a course for the future\\n— a future that you can help to shape.\\nThe University of Antwerp’s Center of Medical Genetics, in collaboration with Qurin PCR B.V. % (Leiden, Netherlands), is looking for a full-\\ntime, skilled and motivated Ph.D. student to join our dynamic team. We are offering an exciting industrial Ph.D. position focused on\\nContact developing scalable, Machine Learning (ML) pipelines for genomic and epigenomic biomarker discovery from Oxford Nanopore Technologies\\n(ONT) long-read sequencing data. The project begins with bladder cancer detection from urine-derived DNA and expands towards a broader\\nset of urologic and related cancers, ultimately supporting precision diagnostics from urine to clinic.\\nContact Position\\nThe position includes the following tasks:\\ne Bioinformatics Pipeline Development: Design, build, and maintain a robust, scalable\\nContact bioinformatics pipeline for ONT long-read sequencing data preprocessing and analysis.\\ne Advanced Analytics: Implement analysis modules for methylation calling, structural variant\\ndetection, fragmentation-based signatures and multi-omics integration.\\ne Machine Learning for Biomarker Discovery: Develop and fine-tune machine learning\\nContact . . . . . . :\\nalgorithms for biomarker discovery, cancer classification, and exploring omics features.\\n¢ Collaborative Research: Work in a multidisciplinary setting with scientists, clinical manager, Al\\nresearchers, and industry experts at the interface of academia and biotech.\\nContact e Knowledge Dissemination: Contribute to peer-reviewed publications, conference\\npresentations, IP generation, and knowledge transfer between academic and industrial\\npartners.\\nProfile s\\nContact\\nWe are looking for a motivated and curious researcher with the following attributes:\\ne Master’s degree (or equivalent) in Bioinformatics, Computational Biology, Computer Science,\\nor a related field from an institution in the EEA or Switzerland. q\\nContact\\n\\ne Strong interest in translational cancer research, bioinformatics, and machine learning.\\n\\ne Strong proficiency with R/Python and machine learning frameworks (e.g., PyTorch,\\nTensorFlow).\\nContact . . . [\\ne Prior experience with workflow management tools (e.g., Snakemake, Nextflow).\\ne Familiarity with cloud computing environments (AWS preferred).\\ne Demonstrated independence and problem-solving ability.\\nContact e Eager to engage in cutting-edge research and work at the interface of academia and industry. |\\ne Open-minded and effective in a collaborative, interdisciplinary environment.\\n\\ne Prior experience with ONT data or epigenomic datasets is an advantage but not required.\\n\\ncoat What we offer\\ne A fully funded, 4-year industrial PhD position within a high-impact startup environment, upon\\npositive evaluation after one year.\\nContact e Joint supervision by leading scientists at the University of Antwerp and Qurin PCR B.V.,\\nensuring both academic rigor and industrial relevance.\\ne Access to state-of-the-art sequencing, clinical samples, and high-performance cloud\\ncomputing infrastructure.\\nContact\\ne Training in cutting-edge computational biology, machine learning, and cancer genomics, with\\nopportunities for workshops, seminars, and conferences.\\ne Hands-on exposure to translational R&D, and IP development in a biotech setting. A dynamic,\\nContact innovation-driven workplace with the mission to improve lives through early cancer detection.\\ne As part of this industrial PhD, you will gain valuable experience in a truly multicultural, cross-\\nborder research environment, moving between the Netherlands (Leiden) and Belgium\\n(Antwerp), offering a unique opportunity to collaborate, learn, and build a strong international\\nContact network.\\ne Your monthly scholarship amount is calculated according to the scholarship amounts for\\ndoctoral scholarship holders.\\nContact e You will receive ecocheques, internet-connectivity allowance, and a bicycle allowance or a full\\nreimbursement of public transport costs for commuting.\\ne You will have 40 days of annual leave and collective leave for 1 week when the university is\\nclosed, between Christmas and New Year.\\nContact e Starting date: Q1 2026, with a convenient date will be discussed with the successful candidate\\nFind out more about working at the University of Antwerp here (4. |\\nWant to apply?\\nContact\\ne You can apply for this job through the University of Antwerp’s online job application platform\\nup to and including 20 November 2025 (by midnight Brussels time). Click on the 'Apply'\\nbutton and complete the online application form. Be sure to include the following attachments:\\nContact o adetailed CV\\no aletter of motivation explaining why you are interested in this PhD position in particular\\no the names and contact details of 2 referees [\\nContact e The selection committee reviews all applications as soon as possible after the application\\ndeadline. As soon as a decision is made, we will notify you. If you are still eligible after the pre-\\nselection, you will be informed about the possible next step(s) in the selection procedure.\\nInterviews will take place on the 2nd week of January 2026. f\\nContact e If you have any questions about the position itself, please contact Prof. Dr. Ken Op de Beeck\\nat ken.opdebeeck@uantwerpen.be or Dr. Joe Ibrahim at j.ibrahim@qurin.com. If you have any\\nquestions about the online application form, please check the Frequently Asked Questions or\\nsend an email to jobs@uantwerp.be. a\\nContact\\nThe University of Antwerp is a sustainable, family-friendly organisation which invests in its employees’ growth. We encourage diversity (4 and\\nattach great importance to an inclusive working environment and equal opportunities, regardless of gender identity, disability, race, ethnicity,\\nreligion or belief, sexual orientation or age. We encourage people from diverse backgrounds and with diverse characteristics to apply.\\nContact\\nWhere to apply\\nWebsite https://academicpositions.com/ad/university-of-antwerp/2025/industrial-phd-in-c.. .\\nContact\\nRequirements\\nContact Research Field Biological sciences\\nYears of Research 1-4\\nExperience\\nContact\\nResearch Field Biological sciences\\nYears of Research 1-4\\nContact Experience\\nResearch Field Computer science\\nContact Years of Research 1-4\\nExperience\\noan Additional Information\\nWebsite for additional job https://academicpositions.com\\ndetails\\nContact\\nWork Location(s)\\nNumber of offers available 1\\nContact\\nCompany/Institute University of Antwerp\\nCountry Belgium\\nContact City Antwerp\\nPostal Code 2000\\nStreet Prinsstraat 13\\nContact\\nGeofield\\nContact\\n\\n|\\nLd\\n\\nContact ¥v +\\n\\niN\\n\\nsa = %\\nc “Straats 2 %,\\nontact a & o\\ninsstrag t Oo\\n‘2 ping\\n£ J\\nyo J —-~——f7J w=\\n1 : FS Sis\\nKeiz erstraap 2 < teste\\nao n £ )\\nd <\\n€\\n\\nWebtools | © EC-GISCO | Leaflet | © OpenStreetMap contributors | Disclaimer\\n\\nContact\\n\\nCity Antwerp\\n\\nWebsite https://www.uantwerpen.be/en/\\nPostal Code 2000\\n\\n\",\n",
|
||
" '',\n",
|
||
" 'Breeding Scheme for Line Cultivars\\n\\n',\n",
|
||
" 'Different commercial constructs thal\\n\\naltered cry-Gene\\n\\ne CaMV 35S-Promotor - wound-inducible promoters\\n- inducible promotors\\n- tissue specific promotors\\n\\n=> up to 0,4% of whole protein composition (corn)\\n\\n© Rubisco (SSU)-Promotor + Translationsfusion an Chloroplasten-Transitpeptid\\n\\n=P up to 0,8% of overall protein content (tobacco)\\n\\ne Expression in chloroplasts (unchanged gene!)\\n\\n==> up to 5 % of overall protein content\\n(tobacco)\\n\\ncry2Aa2-Operon\\n\\nBrigitte Poppenberger (TUM) 54\\n',\n",
|
||
" 'MA-plot\\nIn DESeq2, the function plotMA shows the log2 fold changes attributable to a given variable over the mean of normalized counts for all the\\nsamples in the DESeqDataSet. Points will be colored blue if the adjusted p value is less than 0.1. Points which fall out of the window are plotted\\n\\n‘as open triangles pointing either up or down.\\n\\nplotMA(res, ylim=c(-2,2))\\n\\nlog fold change\\n\\nte+01 1e+02 1e+03 1e+04 1¢+05\\n\\nmean of normalized counts\\n\\nIt is more useful to visualize the MA-plot for the shrunken log2 fold changes, which remove the noise associated with log2 fold changes from low\\n‘count genes without requiring arbitrary filtering thresholds.\\n\\nPlotMA(resLFC, ylim=c(-2,2))\\n\\nlog fold change\\n\\nT T T T T\\nte+01 1e+02 1e+03 1e+04 1¢+05\\n\\nmean of normalized counts\\n\\nAfter calling plotMA, one can use the function identify to interactively detect the row number of individual genes by clicking on the plot. One can\\nthen recover the gene identifiers by saving the resulting indices:\\n\\nidx <- identify(res$baseMean, res$log2FoldChange)\\nrownames (res) [idx]\\n',\n",
|
||
" \"& aman — nano ./Downloads/assignment/Ecoli_nano/Ecoli_nano_genome.tsv — 208x63\\n\\n0@°8@\\nW PICO 5.09\\n\\nIKAOHOFI_00087\\nIKAOHOFI_00088\\nIKAOHOFI_00089\\nIKAOHOFI_00090\\nIKAOHOFI_00091\\nIKAOHOFI_00092\\nIKAOHOFI_00093\\nIKAOHOFI_00094\\nIKAOHOFI_00095\\nIKAOHOFI_00096\\nIKAOHOFI_00097\\nIKAOHOFI_00098\\nIKAOHOFI_00099\\nIKAOHOFJ_00100\\nIKAOHOFI_00101\\nIKAOHOFJ_00102\\nIKAOHOFI_00103\\nIKAOHOFI_00104\\nIKAOHOFI_00105\\nIKAOHOFI_00106\\nIKAOHOFJ_00107\\nIKAOHOFI_00108\\nIKAOHOFI_00109\\nIKAOHOFJ_00110\\nIKAOHOFI_00111\\nIKAOHOFI_00112\\nIKAOHOFI_00113\\nIKAOHOFI_00114\\nIKAOHOFI_00115\\nIKAOHOFI_00116\\nIKAOHOFI_00117\\nIKAOHOFI_00118\\nIKAOHOFI_00119\\nIKAOHOFI_00120\\nIKAOHOFI_00121\\nIKAOHOFI_00122\\nIKAOHOFI_00123\\nIKAOHOFI_00124\\nIKAOHOFI_00125\\nIKAOHOFI_00126\\nKAOHOFI_00127\\nIKAOHOFI_00128\\nIKAOHOFI_00129\\nIKAOHOFI_00130\\nIKAOHOFI_00131\\nIKAOHOFI_00132\\nIKAOHOFI_00133\\nIKAOHOFI_00134\\nIKAOHOFI_00135\\nIKAOHOFI_00136\\nIKAOHOFI_00137\\nIKAOHOFI_00138\\nIKAOHOFI_00139\\nIKAOHOFI_00140\\nIKAOHOFI_00141\\nIKAOHOFI_00142\\nIKAOHOFI_00143\\nIKAOHOFI_00144\\n\\nWie) Get Help\\nWed Exit\\n\\ncDS\\ncDS\\ncDS\\ncDS\\ncDS\\ncDS\\ncDS\\ncDS\\ncDS\\ncDS\\ncDS\\ncDS\\ncDS\\ncDS\\ncDS\\ncDS\\ncDS\\ncDS\\ncDS\\ncDS\\ncDS\\ncDS\\ncDS\\ncDS\\ncDS\\ncDS\\ncDS\\ncDS\\ncDS\\ncDS\\ncDS\\ncDS\\ncDS\\ncDS\\ncDS\\ncDS\\ncDS\\ncDS\\ncDS\\ncDS\\ncDS\\ncDS\\ncDS\\ncDS\\ncDS\\ncDS\\ncDS\\ncDS\\ncDS\\ncDS\\ncDS\\ncDS\\ncDS\\ncDS\\ncDS\\ncDS\\ncDS\\ncDS\\n\\n1074\\n3495\\n711\\n252\\n258\\n696\\n540\\n1305\\n543\\n1026\\n825\\n642\\n384\\n210\\n936\\n1257\\n444\\n1023\\n132\\n702\\n1005\\n642\\n1023\\n810\\n1293\\n105\\n132\\n735\\n621\\n312\\n954\\n1041\\n174\\n522\\n570\\n960\\n1494\\n1686\\n651\\n309\\n1029\\n618\\n942\\n192\\n636\\n699\\n468\\n315\\n756\\n1209\\n696\\n405\\n417\\n249\\n294\\n417\\n1536\\n924\\n\\nycfT\\nmfd\\nycefS_1\\nycfS_2\\nbhsA_1\\ncomR\\n\\nndh\\n\\nC0G4763\\n3.6.4.- C0G1197\\n- C0G1376\\n- C0G1376\\n\\nC0G3417\\n\\n3.9.1.- C0G@537\\nC0G4773\\nC0G4773\\nC0G1263\\nC0G1263\\n\\n--.- COG@084\\n\\nCOGe@84\\n\\nC0GQ470\\n\\nCc0Ge125\\n\\nCOG1559\\n\\n)\\n\\n1.1.1.1\\n2.3.1.3\\n2.3.1.3\\n2.3.1.180\\n2.3.1.274\\nC0GE333\\nC0G1399\\n6.1.- C0G@424\\n+4.99.24\\n-1.26.12\\n1.26.12\\nC0G1344\\n\\n2\\n9\\n9\\n8!\\n7\\n\\n3.\\n5\\n3\\n3\\n\\nC0G1256\\nC0G1256\\n3.2.1.- C0G1705\\n\\nCOG4786\\nCOG4786\\nC0G4787\\nC0G1749\\nC0G1843\\nc0G1558\\nC0G1815\\n\\nC0G2747\\nC0G3418\\nC0G728\\n1.-.-.- C0G@673\\n\\nWe) WriteOut\\nWe) Justify\\n\\n/Downloads/as nment/Ecoli_n\\n\\nInner membrane protein YcfT\\n\\nTranscription-repair—coupling factor\\n\\nputative L,D-transpeptidase YcfS\\n\\nputative L,D-transpeptidase YcfS\\n\\nMultiple stress resistance protein BhsA\\n\\nHTH-type transcriptional repressor ComR\\n\\nhypothetical protein\\n\\nC0G1252 NADH dehydrogenase\\n\\nhypothetical protein\\n\\nC0G1472 Beta—hexosaminidase\\n\\nCOG@51@ Thiamine kinase\\n\\nPenicillin-binding protein activator LpoB\\n\\nhypothetical protein\\n\\nPurine nucleoside phosphoramidase\\n\\nFhuE receptor\\n\\nFhuE receptor\\n\\nPTS system glucose-specific EIICB component\\n\\nPTS system glucose-specific EIICB component\\n\\nputative metal-dependent hydrolase YcfH\\n\\nputative metal-dependent hydrolase YcfH\\n\\nDNA polymerase III subunit delta'\\n\\nThymidylate kinase\\n\\nEndolytic murein transglycosylase\\n\\nC0G@115 Aminodeoxychorismate lyase\\n\\nC0G@304 3-oxoacyl-[acyl-carrier-protein] synthase 2\\n\\nAcyl carrier protein\\n\\nAcyl carrier protein\\n3-oxoacyl-[acyl-carrier-protein] reductase FabG\\n\\nC0G@331 Malonyl CoA-acyl carrier protein transacylase\\n\\nC0G@331 Malonyl CoA-acyl carrier protein transacylase\\n\\nC0G@332 3-oxoacyl-[acyl-carrier-protein] synthase 3\\n\\nC0G@416 Phosphate acyltransferase\\n\\n5@S ribosomal protein L32\\n\\nLarge ribosomal RNA subunit accumulation protein YceD\\n\\n7-methy1-GTP pyrophosphatase\\n\\nC0G@564 Ribosomal large subunit pseudouridine synthase C\\n\\nC0G153@ Ribonuclease E\\n\\nC0G153@ Ribonuclease E\\n\\nFlagellar hook-associated protein 3\\n\\nhypothetical protein\\n\\nFlagellar hook-associated protein 1\\n\\nFlagellar hook-associated protein 1\\n\\nPeptidoglycan hydrolase FlgJ\\n\\nFlagellar P-ring protein\\n\\nFlagellar P-ring protein\\n\\nFlagellar L-ring protein\\n\\nFlagellar basal-body rod protein FlgG\\n\\nFlagellar basal-body rod protein FlgG\\n\\nFlagellar basal-body rod protein FlgF\\n\\nFlagellar hook protein F1lgE\\n\\nBasal-body rod modification protein FlgD\\n\\nFlagellar basal-body rod protein FlgC\\n\\nFlagellar basal body rod protein FlgB\\n\\nhypothetical protein\\n\\nNegative regulator of flagellin synthesis\\n\\nFlagella synthesis protein F1gN\\n\\nLipid II flippase MurJ\\n\\nPutative oxidoreductase YceM\\n\\nWs) Read File Way Prev Pg\\nWi] Where is WAY Next Pg\\n\\nAKI\\nAU\\n\\n_genome.tsv\\n\\nCut Text\\nUnCut Text\\n\\nme Cur Pos\\nWay To Spell\\n\",\n",
|
||
" 'In [258]: merged_seurat <- merge(seurat_list[[1]], seurat_list[[21]])\\n\\nIn [260]: merged_seurat <— SCTransform(merged_seurat, verbose = TRUE)\\n\\n“Steration limit reached” a ee\\nWarning message in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace = control$trace >:\\n“iteration limit reached”\\n\\nSecond step: Get residuals using fitted parameters for 10554 genes\\n\\nComputing corrected count matrix for 10554 genes\\n\\nCalculating gene attributes\\n\\nWall clock passed: Time difference of 12.56345 secs\\n\\nDetermine variable features\\n\\nCentering data matrix\\n\\nCentering data matrix\\n\\nCentering data matrix\\n\\nIn [262]: DefaultAssay(merged_seurat) <- \"RNA\"\\n\\nIn [263]: merged_seurat <— FindVariableFeatures(merged_seurat)\\n\\nFinding variable features for layer counts.patient3\\nFinding variable features for layer counts.patient4\\n\\nWarning message in simpleLoess(y, x, w, span, degree\\n“pseudoinverse used at -2.2082”\\n\\nWarning message in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,\\n“neighborhood radius 0.30103”\\n\\nWarning message in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,\\n“reciprocal condition number 1.651e-16”\\n\\ndegree, parametric = parametric,\\n\\nIn [264]: merged_seurat <— RunPCA(merged_seurat, npcs = 35)\\nWarning message:\\n“No layers found matching search pattern provided”\\n\\n. ey\\n\\n',\n",
|
||
" '[pst14@frontend ref_gen]$ head -n 3@ alignment_stats_1.txt | grep *SN | cut -f 2-\\nraw total sequences:\\nfiltered sequences:\\nsequences: 360832\\nis sorted: 1\\n\\n1st fragments: 180416\\nlast fragments: 180416\\n\\nreads\\nreads\\nreads\\nreads\\nreads\\nreads\\nreads\\nreads\\n\\nMapped: 354643\\nmapped and paired:\\nunmapped: 6189\\nproperly paired:\\npaired: 360832\\nduplicated:\\n\\nMQe: 24875\\nQC failed:\\n\\nnon-primary alignments:\\n\\ntotal\\ntotal\\ntotal\\nbases\\nbases\\nbases\\nbases\\n\\nlength: 46826773\\nfirst fragment len\\nlast fragment leng\\nMapped: 46114887\\nmapped (cigar):\\ntrimmed: 0\\nduplicated:\\n\\nmismatches: 844553\\n\\n360832\\n7)\\n\\n349470 # paired-end technology bit set + both mates mapped\\n\\n256930 # proper-pair bit set\\n# paired-end technology bit set\\n\\n(7) # PCR or optical duplicate bit set\\n# mapped and MQ=@\\n\\nQ\\n\\n7)\\n\\n# ignores clipping\\ngth: 23420423 # ignores clipping\\n\\nth: 23406350 # ignores clipping\\n# ignores clipping\\n44158288 # more accurate\\n\\nQ\\n# from NM fields\\n',\n",
|
||
" '156,000 KB 155,000 KB 154,000 KB 153,000 KB 152,000 KB 151,000 KB\\n\\n157,000 KB\\n\\n148 MB 149 MB\\n\\n150 MB.\\n\\n151 MB\\n\\n152 MB\\n\\n153 MB\\n\\n154 MB\\n\\n155 MB\\n\\n156 MB\\n\\n157 MB\\n\\n158 MB\\n\\n159 MB.\\n\\n»\\n\\na>\\n\\n',\n",
|
||
" 'new.cluster.ids <- c(\"Naive CD4 T\", \"CD14+ Mono\", \"Memory CD4 T\", \"B\", \"CD8 T\", \"FCGR3A+ Mono\",\\n\"NK\", \"DC\", \"Platelet\")\\n\\nnames(new.cluster.ids) <- levels (pbmc)\\n\\npbmc <- RenameIdents(pbmc, new.cluster. ids)\\n\\nDimPlot(pbmc, reduction = \"umap\", label = TRUE, pt.size = @.5) + NoLegend()\\n',\n",
|
||
" '',\n",
|
||
" 'Figure 1: Kink and Bend in Arabidopsis Thaliana\\n\\n',\n",
|
||
" \"Chromatin-based gene regulation\\n\\nEstablishment Maintenance\\n\\nClosed chromatin Closed chromatin Closed chromatin\\n\\n— 9 al sh ln al sh ln\\n, di } pa Mitotic / Meiotic cell divisions pa\\n=== eee —_—_———— eee\\n\\nTranscription OFF Transcription OFF Transcription OFF\\n\\nOpen chromatin Open chromatin pen chromatin\\n\\nL j bs» ° bs» °\\n“é *) - S Mitotic / Meiotic cell divisions SS\\n\\n‘Transcription ON Transcription ON Transcription ON\\n\\n(Part of) the solution\\n\\nChromatin remodeling Chromatin modifications\\n\\nATP-dependent DNA methylation Histone modifications\\n\\nHATs HDACs\\n4 . ¥\\n‘\\n\\nf he , :\\nST Sy ' a\\n\\nHMTs HOMs\\n\\n\",\n",
|
||
" 'Session restored from your saved work on 2025-Apr-1@ 13:00:58 UTC (2 hours ago)\\n> .libPathsO\\n\\n[1] \"/home/aman/R/x86_64-pc-Linux-gnu-library/4.4\" \"/usr/Local/lib/R/site-library\"\\n[3] \"/usr/local/lib/R/library\"\\n>\\n',\n",
|
||
" \"@) Aman Shamil Nalakath\\n\\nMon 3/3, 1:31 PM\\nDear Dr. Papantonis,\\nThe recommended duration for a thesis as per our program is 6 months, that is once registered, the submission is due within this period.\\nIf it is okay, I'd like to begin at the start of April.\\n\\nBest Regards,\\nAman\\n\\n@) Papantonis, Argyris <argyris.papantonis@med.uni-goettingen.de>\\nMon 3/3, 8:11 AM\\n\\nAman Shamil Nalakath ¥\\n\\nHi Aman,\\nYou need to tell me what the expected duration by your program is and what would be your preferred starting date — we are flexible.\\n\\nA.\\n\\nArgyris Papantonis, PhD\\n\\nProfessor for Translational Epigenetics & Genome Architecture,\\nInstitute of Pathology, University Medical Center Géttingen,\\nRobert-Koch-Str. 40, 37075 Géttingen, Germany\\n\\nTel.: +49 551 39 65734\\n\\nWeb: https://papantonislab.eu\\n\",\n",
|
||
" \"ImportError Traceback (most recent call last)\\n\\nFile ~/anaconda3/envs/cool_notebook/lib/python3.10/site-packages/cooltools/lib/plotting. py:6\\n5 try:\\n\\n----> 6 from matplotlib.cm import register_cmap\\n7 except ImportError:\\n\\nImportError: cannot import name 'register_cmap' from 'matplotlib.cm' (/usr/users/papantonis1/anaconda3/envs/cool_notebook/lib/python3.10/site-packages/matp lot lib/cm. py)\\n\\nDuring handling of the above exception, another exception occurred:\\n\\nModuleNotFoundError Traceback (most recent call last)\\nCell In{11], line 3\\n\\n1 ### plot the corrected data in fall heatmap and compare to the white-red colormap ###\\n\\n2 ### thanks for the alternative collormap naming to https://twitter.com/HiC_memes/status/1286326919122825221/photo/1###\\n3 import cooltools. lib. plotting\\n\\n5 vmax = 5000\\n\\n6 norm = LogNorm(vmin=1, vmax=100_000)\\n\\nFile ~/anaconda3/envs/cool_notebook/lib/python3.10/site-packages/cooltools/lib/plotting. py:8\\n\\n6 from matplotlib.cm import register_cmap\\n7 except ImportError:\\n--—-> 8 from matplotlib.colormaps import register\\n\\n10 import matplotlib as mpl\\nimport matplotlib.pyplot as plt\\n\\ni=\\n\\nModuleNotFoundError: No module named 'matplotlib.colormaps'\\n\",\n",
|
||
" 'Thank you for using QUAST!\\nQUAST results for wood_sample_1:\\nAll statistics are based on contigs of size >= 5@@ bp, unless otherwise noted (e.g., \"# contigs (>= @ bp)\" and \"Total length (>= @ bp)\" include all contigs).\\n\\nAssembly scaffolds\\n# contigs (>= @ bp)\\n\\n# contigs (>= 1000 bp)\\n\\n# contigs (>= 5000 bp)\\n\\n# contigs (>= 10000 bp)\\n# contigs (>= 25000 bp)\\n# contigs (>= 50000 bp)\\nTotal length (>= @ bp) 137003\\nTotal length (>= 1000 bp) 135394\\nTotal length (>= 5000 bp) 135394\\nTotal length (>= 10000 bp) 135394\\nTotal length (>= 25000 bp) 116346\\nTotal length (>= 50000 bp) 90525\\n\\nPNWWWO\\n\\n# contigs 3\\nLargest contig 90525\\nTotal length 135394\\nGC (%) 35.69\\nN50 90525\\nN90 19048\\nauN 68129.5\\nL50 1\\n\\nL90 3\\n\\n#N\\'s per 100 kbp @.00\\n',\n",
|
||
" 'Arbuscular mycorrhiza development\\n\\n(Lipo)chitooligo-\\nsaccharides\\n\\nRhizodermis\\n\\nOuter cortex\\n\\nInner cortex\\n\\nNucleus High-frequency Arbuscule\\ncalcium spiking\\n\\nLow-frequency\\ncalcium spiking Gutjahr & Parniske 2013, Ann. Rev. Cell Dev. Biol.\\n',\n",
|
||
" \"Y Speed Dial\\n\\na) e- 7 &) © @ NotSecure 172.17.0.4:8887 o1 {J ~ Search Startpage\\n\\nvY Imported From ...\\n\\nvY Imported From ...\\n\\nOnline Bewerbung QGIS API Docume... aqgis - Trying to pe... New Script - Earth... Pastebin.com - #1... TargetP 2.0 - DTU...\\n\\n172.17.0.4 doesn't support a secure connection\\n\\ne Attackers can see and change information you send or receive from the site.\\ne It's safest to visit this site later if you're using a public network. There is less risk\\nfrom a trusted network, like your home or work Wi-Fi.\\n\\nYou might also contact the site owner and suggest they upgrade to HTTPS. Learn more\\nabout this warning\\n\\nContinue to site\\n\\nhttps://www.mood...\\n\\nOnePlus 12R revie...\\n\\nBb*®¢of & G@ OA\\n\\nWho is “Indian” in ...\\n\",\n",
|
||
" 'for iin {1..5}; do\\nscaffolds_path=\"/data/proj2/home/students/pst14/wood_sample_${i}_spades_out/scaffolds. fasta\"\\nquast_output_path=\"/data/proj2/home/students/pst14/illegal_logging_trees/fastqc_raw/quast_output_sample_${i}_spades\"\\n# Run QUAST on the scaffolds file\\n$HOME/quast-5.3.0/quast.py $scaffolds_path -o $quast_output_path\\n# Display the QUAST results\\necho \"QUAST results for wood_sample_${i}:\"\\ncat ${quast_output_path}/report. txt\\necho \"\"\\ndone\\n',\n",
|
||
" 'Mean Methylation Level\\n\\nMean Methylation Levels - CG Context Mean Methylation Levels - CHG Context Mean Methylation Levels - CHH Context\\n\\n0.100\\n\\n0.04\\n\\nMean Methylation Level\\ng\\n\\n0.025\\n\\n0.000.\\n\\n0.00.\\n\\n%,\\n\\nFile\\n\\nFile File\\n',\n",
|
||
" \"In [418]: names(combined.TCR_p3[[1]])\\nnames (combined. TCR_p4[[1]])\\n\\n‘parcode': '‘sample': 'TCR1': ‘cdr3_aa1'- ‘cdr3_nti1'- 'TCR2'- ‘cdr3_aa2'- '‘cdr3_nt2'- 'CTgene'- ‘CTnt'- '‘CTaa'- 'CTstrict'\\n\\n‘parcode': '‘sample': 'TCR1': ‘cdr3_aa1'- ‘cdr3_nti1'- 'TCR2'- ‘cdr3_aa2'- '‘cdr3_nt2'- 'CTgene'- ‘CTnt'- '‘CTaa'- 'CTstrict'\\n\",\n",
|
||
" 'Map of retinal cell\\nchromatin contacts\\n\\n',\n",
|
||
" 'In [272]: merged_seurat <— IntegrateLayers(\\nobject = merged_seurat,\\nmethod = RPCAIntegration,\\norig. reduction = \"pca\",\\nnew. reduction = “integrated. rpca\",\\nverbose = TRUE\\n\\n)\\n\\nComputing within dataset neighborhoods\\n\\nFinding all pairwise anchors\\n\\nError in UseMethod(generic = \"Assays\", object = object): no applicable method for ‘Assays\\' applied to an object of\\nclass \"NULL\"\\n\\nTrarpharck:\\n',\n",
|
||
" 'ASSIGNMENT 1 10\\nDL: check the Moodle ay\\n\\n©\\n1\\n1\\n\\nppn7 4\\nexercise n fastq rma ou ng folde HPC\\n2. Run FastQC and check the report for data quality\\n\\nOo\\noO\\n5\\nrey\\no\\na.\\n= |\\n1\\nL\\n\\nD\\nQO\\nCc\\n\\n4]\\n2\\nra)\\nri)\\nD\\nCc\\n\\n3\\n1D\\nCc\\n>\\ni\\not\\n|\\no\\n|\\no\\n>\\no\\n+\\n\\nr\\no\\nr\\n\\nc\\n\\nt\\n@\\nCc\\nrm\\n\\nOfSOOSOOOOS\\n\\nrim the file using Trimmomatic\\n\\n4. Run FastQC again and check the report\\n\\nMORE VIDEOS a\\n| MORE VIDEOS | TA\\nTECH\\n> i) 26:31/ 28:38 © @ & Voulube ++\\n\\n',\n",
|
||
" '[2]:\\n\\nhicPlotDistVsCounts —-matrix /mnt/storage3/aman/data_mcool.h5 —-outFileName contact_decay.png\\nusage: hicPlotDistVsCounts --matrices MATRICES [MATRICES ...] --plotFile file\\n\\nname [--labels LABELS [LABELS ...]]\\n[--skipDiagonal] [--maxdepth INT bp] [--perchr]\\n\\n[--chromosomeExclude CHROMOSOMEEXCLUDE [CHROMOSOMEEXCLUDE ...]]\\n[--outFileData OUTFILEDATA]\\n[--plotsize PLOTSIZE PLOTSIZE] [--help] [--version]\\n\\nhicPlotDistVsCounts: error: the following arguments are required: —-matrices/—m, —-plotFile/-o\\nB 2\\n',\n",
|
||
" 'Read Counts - subset of 1e+05 interactions\\n\\n5000\\n\\n4000\\n\\n3000\\n\\n2000\\n\\n1000\\n\\nValid Pairs —- Fragment size distribution\\n\\n0 50 100 150 200 250 300 350 400 450 500\\n\\n600\\n\\ndata\\n\\n800\\n\\n1200\\n\\n1400\\n\\n>1500\\n',\n",
|
||
" 'R° ResearchGate < > : x\\n\\nPRC1 PRC2\\n\\n2)\\n\\nMajor components of PRC1 and PRC2. Compositions of\\nthe two major types... | Download Scientific Diagram\\n\\n',\n",
|
||
" \"@ ZoomWorkplace Meeting View Edit Window Help Se Wed Feb 12 22:27\\n\\nee v4 Meeting @® Ratula Ray's screen Signin | View i\\n\\n[==] Layout ¥ a . 7 = Pp Find iB A\\n\\n‘© Reset , 5| ¥ ab Replace v\\nNew 5 Arrange Create PDF Create PDF and Add-ins\\n\\nSlide v & Section v a I$ Select and Share link Share via Outlook\\n\\nSlides Paragraph Drawing Editing Adobe Acrobat Add-ins\\n\\nSummary\\n\\n> There is growth along PD axis in patch B of epidermis\\n\\n> The growth is driven by both increase in cell number and tissue expansion in\\npatch B of epidermis\\n\\n> [0.75-1.00] interval range in patch B of epidermis shows highest cell\\nexpansion\\n\\n> We could not spot isotropic cells (on average) along the PD axis for the\\nepidermis\\n\\n> But there is clearly tissue growth at patch B (already hints for some sort of PD\\nsignal in the epidermis), which is mostly driven by cell expansion at [0.75-\\n1.00] interval\\n\\n3°\\nce?\\n\\nParticipants React Share\\n\\n\",\n",
|
||
" 'Check if the filtered termagene table has any rows\\nprint (paste(\"Filtered term2gene table for\", level, \"has\", nrow(tempset), “rows\"))\\nif (nrow(tempset) > 0) {\\nprint(\"First few rows of filtered termagene:\")\\nprint (head(tempset, 3))\\n\\nenrich <- enricher(\\nRona,\\npAdjustMethod = \"BH\",\\naincssize = 10,\\nsansssize = 500,\\nGain S02,\\n‘TERM2GENE = tempset)\\n\\n#print(\"Enrichment results structure\\n#print(str(enrich))\\nres <- data. frame(enrich)\\n\\nfprint(\"First few rows of enrichment results:\")\\n#print(head(res) )\\ndir.create(\"\"/mnt/volume/data/group8/enrichnents/\")\\n# Save results if enrichment results exist\\nif (nrow(res) >= 1) {\\nfilename <- paste0(\"\\'/mnt/volume/data/group8/enrichments/\", level, \"_DESeq2 Mercator_clusterprof.csv\")\\nwrite.csv(res, file = filename)\\n\\nplot_filename <- paste0(\"/mnt/volune/data/group8/enrichnents/\", level,\\nggsave( filename = plot_filenane,\\n\\nplot = dotplot(enrich),\\n\\nwidth = 15, height = 10)\\n\\n\"_DESeq2_Mercator_plot.pdf\")\\n\\n}\\n\\n} else {\\nprint(paste(\"No enrichnent data found for\", level, \"skipping.\")\\nnext\\n\\nprint(\"AUL processing completed.\\n\\n® oss\\n\\n[1] \"Summary of termgene before filtering:\\n‘data. frame\\': 358250 obs. of 4 variables:\\n\\n$ Gene + chr \"HORVU.MOREX. r3.2HG0150570\" \"HORVU.MOREX. r3.2HG0113330\" “HORVU.MOREX. r3.3HG0319250\" \"HORVU.MOREX. r3.6HG0546260\" ...\\n$ Term: chr \"Photosynthesis\" \"Photosynthesis\" \"Photosynthesis\" \"Photosynthesis\"\\n\\n$ Filename: chr “nercator_levell_barley.csv\" \"nercator_level1_barley.csv\" \"“mercator_level1_barley.csv\" \"nercator_level1_bartey.csv\"\\n$level : chr “level” “level” \"level\" “level”...\\n\\nNULL\\n\\n[2] \"Unique levels in termagene:”\\n\\n[1] \"leveli\" ‘Level2\\' “level3\" ‘level4\" “levels\"\\n\\n[6] \"levelé\" ‘level7 \"levels\" \\'protscriber\" \"swissprot\"\\n\\n[2] \"Unique filenames in termagene:”\\n\\n[1] \"mercator_level1_barley.csv\" “mercator_level2_barley.csv\"\\n\\n[3] \"mercator_level3_barley.csv\" “mercator_level4_barley.csv\"\\n\\n[5] \"mercator_levelS_barley.csv\" “mercator_level6_barley.csv\"\\n\\n[7] \"mercator_level7_barley.csv\" “mercator_level8_barley.csv\"\\n\\n[9] \"mercator_protscriber_barley.csv\" \"mercator_swissprot_barley.csv\"\\n[1] \"Processing enrichment for level\"\\n[1] \"Filtered termagene table for level1 has 35825 rows\"\\n[1] “First few rows of filtered termagene:\"\\nTerm Gene\\n1 Photosynthesis HORVU.MOREX. r3.2HG0150570\\n2 Photosynthesis HORVU.MOREX. r3.2HG0113330\\n3 Photosynthesis HORVU.MOREX. r3.3HG0319250\\n\\nError: object ‘§EHBWEE\\' not found\\n\\nTraceback:\\n\\n1. enricher_internal(gene = gene, pvalueCutoff = pvalueCutoff, pAdjustMethod = pAdjustMethod,\\n+ universe = universe, minGSSize = minGSSize, maxGSSize = maxGSSize,\\n+ qvalueCutoff = qvalueCutoff, USER_DATA = USER_DATA)\\n\\n2. unique(gene)\\n\\n3. .handleSimpleError( function (cnd)\\nft\\n. watcher$capture_plot_and_output()\\n\\n. cnd <- sanitize_call(cnd)\\n\\n. watcher$push(cnd)\\n\\n+ switch(on_error, continue = invokeRestart(\"eval_continue\"),\\n\\n. stop = invokeRestart(\"eval_stop\"), error ULL)\\n\\n+}, object \"§BRBWEE\\' not found\", base: :quote(eval(expr, envir)))\\n\\n',\n",
|
||
" \"In\\n\\n[6]:\\n\\ngt_haploid = allel.HaplotypeArray(callset['calldata/GT'])\\n\\nTypeError Traceback (most recent call last)\\nInput In [6], in <cell line: 1>()\\n----> 1 gt_haploid =\\n\\nFile ~/.conda/envs/test_allel_env/lib/python3.9/site-packages/allel/model/ndarray.py:1976, in HaplotypeArray._ init\\n__(self, data, copy, *xkwargs)\\n\\n1974 super(HaplotypeArray, self). _init__(data, copy=copy, **kwargs)\\n\\n1975 check_integer_dtype(self.values)\\n-> 1976 check_ndim(self.values, 2)\\n\\nFile ~/.conda/envs/test_allel_env/lib/python3.9/site-packages/allel/util.py:63, in check_ndim(a, ndim)\\n61 def check_ndim(a, ndim):\\n62 if a.ndim != ndim:\\n\\n---—> 63 raise TypeError('bad number of dimensions: expected %s; found %s' % (ndim, a.ndim) )\\n\\nTypeError: bad number of dimensions: expected 2; found 3\\n\",\n",
|
||
" '+241 PREFIX=/usr/local/anaconda\\n\\n-448 Unpacking payload .\\n\\n+451 qemu-x86_64: Could not open \\'/1ib64/1d-linux-x86-64.s0.2\\': No such file or directory\\n+452 qemu-x86_64: Could not open \\'/1ib64/1d-linux-x86-64.s0.2\\': No such file or directory\\n\\n6.165 S2600K . 12.1M Os\\n8.189 82700K . 12.2M @s\\n8.193 82750K . 16.3M Qs\\n8.196 82800K 9.28M @s\\n8.201 82850K 22.1M Qs\\n8.205 82900K . 8.94M @s\\n8.209 82950K . 12.5M @s\\n8.213 83000K . 13.7M Qs\\n8.216 83050K 191M=7.7s\\n8.217\\n\\n8.217 2025-04-03 13:04:05 (10.6 MB/s) - ‘/tmp/miniconda.sh’ saved [85055499/85055499]\\n8.217\\n\\n8\\n\\n8\\n\\n8\\n\\n8\\n\\n42 | #miniconda\\n43 | >>> RUN wget https://repo.continuum. io/miniconda/Miniconda3-py37_4.8.2-Linux-x86_64.sh -O /tmp/miniconda.sh && \\\\\\n44 | >>> bash /tmp/miniconda.sh -b -p /usr/local/anaconda && \\\\\\n45 | >>> rm /tmp/miniconda.sh\\n46 |\\nERROR: failed to solve: process \"/bin/sh -c wget https://repo.continuum.io/miniconda/Miniconda3-py37_4.8.2-Linux-x86_64.sh -O /tmp/miniconda.sh && bash /tmp/miniconda.sh -b -p /usr/local/anaconda &&\\n\\nm /tmp/miniconda.sh\" did not complete successfully: exit code: 1\\n\\nView build details: docker-desktop: //dashboard/build/desktop—linux/desktop—linux/xjx9q2juztcdkzzj8dtq7w5a4\\n-aman@Laptop-von—Aman juicer_hpro %\\n',\n",
|
||
" '[pst14@frontend ~]$ time bbmap.sh -Xmx8g threads=8 trd ref=/data/proj2/home/students/pst14/ref_gen/sample2/ncbi_dataset/data/GCA_032401905.1/GCA_@32401905.1_ASM324019@v1_genomic.fna nodisk in=$HOME/illegal_lo\\ngging_trees/fastqc_raw/trimmomatic/wood_sample_2/wood_sample_2_forward_paired.fq.gz in2=$HOME/illegal_logging_trees/fastqc_raw/trimmomatic/wood_sample_2/wood_sample_2_reverse_paired.fq.gz out=sample2_global.s\\nam\\n',\n",
|
||
" 'PRBRPANTHER arenes aie feet stent o®\\nSette a er ener ee\\n\\nPANTHERI9.0 Released, Click fr more details.\\n\\n\"Carent Relese: PANTHER 199 | species | News\\n\\nPANTHER GENE LIST® Customise Gane list Click o view Enhancer Data ©\\n\\nConvert Us [saess Sanita eas =) BS\\n\\nDesi: (38) heme par pape Rating Search\\note 1-30 of 224{ page: (2) 2.4 567.8). Narber of ppd i found 22910 nt fn (5)\\n\\nry |\\n‘cane ‘Gene Name [PANTHER Famiy/Subtamy [PANTHER Protein Cis Seas\\n‘Gene Sombot\\nPersistent it\\n‘onmoiogs\\n\\nA. ARAIMIIALR=icus=2203542\\\\Unrata=Q9C8y. ‘ATIGOGACD Probabecaloum-bndng protein CHLZS \" rabdopae\\n. Erxaoo0s17 el ‘atone\\nshoes\\na ares |AT5625260 ols protein 2 ‘LOULLIN-UIKE PROTEIN RELATED (OURISRDS:SE) = prabdopss\\no a ‘ation\\nfnhlaoe\\n( & agamstratn-inus=20760421Uibrnkn-neren4 1G51090 Heavy meta tranaport detoxication HEAVY METAL TRANSPORT DETOMIFICATION raidonss\\n‘pct cn Siseneamitr anor oronaaas Se ‘tons\\npar\\n‘A. ARATHITAIRGIocur=504954695|UnProteB P5679 ‘arec00720 Cytsenrome 86 Ccrrociome w ceriai9271:SF16) E ‘esos\\n9 oe a\\nARATHITAIK=locus=20928801UnirotKt=Q91 109 (872639820 abinogalecton protein 12 ARABINOGALACTAN PROTEIN 12 (#TH24124:SF10) rabdopss\\no« locus ‘ATeGO0080 Photosystem I reacon center prot I MOTOSYSIEM 1 REACTION CENTER PROTEIN rabdopss\\nea Fiaiirsrraaty ‘Sotona\\n\\nFinopzusszox\\nencoos\\n',\n",
|
||
" \"Analysis Brief Summary\\nJob ID: 701014775.1 [Useful within 7 days]\\nSpecies: Arabidopsis thaliana\\nGO type: Completed GO\\nGO version:2016\\nBackground/ Reference: TAIR genome locus (TAIR10 2017)\\nAnnotated number in query list: 213 [ ' Download ]\\nAnnotated number in background/reference: 28362\\n\\nSignificant GO terms: 45 [ 4 Details ]\\n\\nGraphical Results ©\\nSelect Category\\n@ Biological Process O Cellular Component © Molecular Function\\nAdvanced Parameter Settings\\nGraphic result format: © PNG O ppF O3pEG O GIF O svG\\nGraph rank direction: © Top to Bottom © Left to Right O Bottom to Top © Right to Left\\nGraph font size (pt): O7 Os O9 @1io0 O11 O12\\n\\nGO flash Chart ©\\nSelect Category\\nBiological Process (Cellular Component () Molecular Function\\nAdvanced Parameter Settings\\nBar style: © Glass Bar © Filled Bar © 3D Bar © Cylinder Bar\\nBg/Ref bar color: [HEX format only] [ default ]\\n\\nX legend content: © GO annotation O GO accession _ font\\n\\nDetail information ©\\n\\nYou can [\\n\\nBrowse in tree traversing mode ] [ _d Browse all GO terms ] [ (3 Download ]\\n\\n-XAMPLE\\n\\nOr select from following significant terms to [ Draw graphical results ;2: ] [ Create bar chart ji; ] [Scatter Plots analysis «| ]\\n\\nOGoterm Ontology Description Number in input list Number in BG/Ref | p-value FDR\\n\\n© Go:0010200 | P response to chitin at 134 1.5e-20 | 1.5e-17\\n0Go:0015979 | P photosynthesis 25 253 8e-20 4e-17\\n0 Go:0010243 P response to organonitrogen compound 21 166 8.1e-19 2.7e-16\\nOGo:1901698 | P response to nitrogen compound 21 264 4.5e-15 1.le-12\\n© Go:0019684 | P photosynthesis, light reaction 11 134 1.3e-08 2.6¢-06\\n© Go0:0006091 P generation of precursor metabolites and energy 16 364 3.1e-08 5.2e-06\\n© G0:0009769 P photosynthesis, light harvesting in photosystem II 5 10 6.2e-08 8.9e-06\\n\\n\",\n",
|
||
" '© multiqe\\n\\nvi.25.2\\n\\nGeneral Stats\\n\\nFastQC\\n\\nSequence Counts\\nSequence Quality Histograms\\n\\nPer Sequence Quality Scores\\n\\nPer Base Sequence Content\\n\\nPer Sequence GC Content\\n\\nPer Base N Content\\n\\nSequence Length Distribution\\nSequence Duplication Levels\\nOverrepresented sequences by sample\\nTop overrepresented sequences\\nAdapter Content\\n\\nStatus Checks\\n\\nSoftware Versions\\n\\nPer Sequence GC Content MES\\nThe average GC content of reads. Normal random library typically have a roughly normal distribution of GC content.\\n\\nCounts\\n\\n5%\\n\\n4%\\n\\n3%\\n\\nPercentage\\n\\n2%\\n\\n0%\\n\\nPer Base N Content (ES\\n\\nThe percentage of base calls at each position for which an N. was called.\\n\\nPercentage N-Count\\n\\nObp 20 bp 40 bp\\n\\nFastQC: Per Sequence GC Content\\n\\n60 bp\\n\\nPercentages, 20 samples\\n\\n% GC\\n\\nFastQC: Per Base N Content\\n\\n20 samples\\n\\n80 bp\\n\\nPosition in Read (hn)\\n\\n100 bp\\n\\n80%\\n\\n120 bp\\n\\n140 bp\\n\\n100%\\n\\nCreated with Multiac\\n\\n> % Toolbox\\n\\na\\n\\no«v Ff ke\\n\\n',\n",
|
||
" 'Heatmap of the sample-to-sample distances\\n\\nAnother use of the transformed data is sample clustering. Here, we apply the dist function to the transpose of the transformed count matrix to get\\nsample-to-sample distances.\\n\\nsampleDists <- dist(t(assay(vsd)))\\n\\nA heatmap of this distance matrix gives us an overview over similarities and dissimilarities between samples. We have to provide a hierarchical\\nclustering he to the heatmap function based on the sample distances, or else the heat map function would calculate a clustering based on the\\ndistances between the rows/columns of the distance matrix.\\n\\nLibrary(\"RColorBrewer\")\\n\\nsampleDistMatrix <- as.matrix(sampleDists)\\n\\nrownames(sampleDistMatrix) <- paste(vsd$condition, vsdétype, sep:\\n\\ncolnames(sampleDistMatrix) <- NULL\\n\\ncolors <- colorRampPalette( rev(brewer.pal(9, \"Blue\\n\\npheatmap(sampleDistMatrix,\\nclustering_distance_rows=sampleDists,\\nclustering_distance_cols=sampleDists,\\ncol=colors)\\n\\n)) (255)\\n\\na\\n\\n30\\nuntreated-single-read\\n\\n25\\nUntreated-single-read 4,\\n\\nuntreated-paired-end |) 15\\n\\n10\\nuntreated-paired-end\\n\\n5\\ntreated-single-read °\\n\\ntreated-paired-end\\n\\ntreated-paired-end\\n\\n',\n",
|
||
" 'Sample Distance Matrix\\n\\nC—O\\n\\nO8r9981ZuUS\\n\\n8Ly99817HUS\\n\\n6LY9981Z74US ]\\n\\nLLY998174uS\\n\\nLepoogizuus\\n\\n‘y8998LZ7NUS\\n\\n9gpo9g1ZuUS\\n\\n£87998 1Z4US\\n\\nozy9ogizuus\\n\\nS879981ZHUS\\n\\nLavoogizuus\\n\\nZLP998LZHUS\\n\\nELY99817HUS\\n\\nzgyoog1ZuUs\\n\\n€8p9981Z4US\\n\\n9Ly9981ZuUS\\n\\nvLv998LZUUS\\n\\nrs\\n4\\na\\n3\\n8\\n3\\ng\\ng\\n5\\n\\nSRR21866480\\n\\nSRR21866478\\n\\n| sara\\n\\n‘SRR21866477\\n\\n‘SRR21866481\\n\\nSRR21866484\\n\\nSRR21866486\\n\\nSRR21866487\\n\\nSRR21866470\\n\\nSRR21866485,\\n\\nSRR21866471\\n\\nSRR21866472\\n\\nSRR21866473\\n\\n‘SRR21866482\\n\\nSRR21866483\\n\\n‘SRR21866476\\n\\nSRR21866474\\n\\n‘SRR21866475,\\n\\n| 250\\n\\n200\\n150\\n100\\n\\n',\n",
|
||
" 'Library Complexity\\n\\nIf you preformed a shallow sequencing experiment (e.g. 2M reads) and running a QC analysis to\\ndecide which library to use for deep sequencing (DS), it is recommended to evaluate the complexity\\nof the library before moving to DS.\\n\\nThe Ic_extrap utility of the preseq package aims to predict the complexity of sequencing libraries.\\npreseq options:\\n\\nParameter Value Function\\n\\nSpecifies that the input file type is bam. Please note that for\\na bam file to be a recognized input fil htsib sould be\\n\\nbam installed as well and preseq should be built with htslib\\nsupport (for more details see preseq documentation or our\\ninstallDep.sh script as example)\\n\\npe Specifies that paired end data is used\\nextrap 2.106409 Maximum extrapolation\\n\\nstep 1.00608 Extrapolation step size\\n\\nseg.len 1000000000 maximum segment length when merging paired end bam\\noutput output file\\n\\nPlease note that the input bam file should be a version prior to dups removal.\\n\\npreseq \\\\c_extrap command example for extrapolating library complexity:\\n\\nCommand:\\nrap -bam -pe ~extrap 2.1¢9 ~step 1e8 -seo_len 1000000000 -output <outpu input bam\\nExample:\\n\\npreseq \\\\cextrap -ban -pe ~extrap 2.1e9 -step 1e8 ~seg_len 1000000000 -output out-preseq mappec\\n\\nIn this example the output file out.preseq will detail the extrapolated complexity curve of your\\nlibrary, with the number of reads in the first column and the expected distinct read value in the\\nsecond column, For a typical experiment (human sample) check the expected complexity at 300M\\nreads (to show the content of the file, type cat out.preseq). Expected unique pairs at 300M\\nsequencing is at least ~ 120 million\\n\\nOER _0\\n\\n200004\\n60000000%\\n\\n400000004\\n\\n5000000%\\n\\n60000000\\n0000\\n\\n3000000\\n190000\\n\\n',\n",
|
||
" '# VCF analysis\\nfor i in xbowtie.vcf; do\\n\\ngrep -c \\'*##\\' \"$i\" # Count the lines starting with \\'##\\'\\n\\ngrep --color \\'*#CHROM\\' \"$i\" # Show the line starting with \\'#CHROM\\'\\ngrep -v \"*#\" -c \"$i\" ## Count the lines not starting with \\'#\\'\\n\\ngrep -v \"#\" \"$i\" | grep -c \"TYPE=snp\"\\n\\necho \"SNPS above\"\\n\\ngrep -v \"#\" \"$i\" | grep -c \"TYPE=mnp\"\\n\\ngrep -v \"#\" \"$i\" | grep -c \"TYPE=ins\"\\n\\ngrep -v \"#\" \"$i\" | grep -c \"TYPE=del\"\\n\\ngrep -v \"#\" \"$i\" | grep -c \"TYPE=complex\"\\n\\necho \"Analysis complete for $i\"\\n\\ndone\\n[20] bash\\n\\n',\n",
|
||
" 'Hi-C Signal\\n\\n25\\n\\nN\\n°\\n\\nrR\\nuw\\n\\n10\\n\\nInteraction Decay\\n\\n—— Row Sum (interactions by position)\\n—— Column Sum (Interactions by position)\\n\\n2 3 4 5 6\\nPosition Relative to Anchor\\n',\n",
|
||
" 'Objectives\\n\\n¢ Role of epidermal cells directly overlaying the Chalaza\\n¢ Pipeline development for quantifying the Kink angle\\n',\n",
|
||
" 'DATE OF ISSUE OF THE THESIS\\n\\n30.04.2025\\n\\n',\n",
|
||
" 'gchri x1 x2 chr2 yi y2 name score strand1 strand2 color observed expectedBL expectedDonut expectedH expectedV fdrBL fdrDonut fdrH fdrv >|\\n# juicer_tools version 2.20.00\\n\\n1@ 81900000 81925000 10 82000000 82025000 : : : : @,255,255 68.0 33.178383 26.434643 27 .220037 26.573101 @.00123%\\n1@ 90400000 90425000 10 90650000 90675000 . : . : @,255,255 70.0 37.90534 26.942131 24.12056 42.41541 4.63850\\n1@ 88760000 88770000 10 88830000 88840000 : : : : @,255,255 39.0 11.372163 10.639409 11.276559 14.075678 8.81380\\n1 97025000 97050000 10 97175000 97200000 . : . : @,255,255 47.0 8.009961 11.851077 7.445743 16.6185 2.89565\\n1@ 67010000 67020000 10 67160000 67170000 : : : : @,255,255 18.0 4.8370414 4.939827 4.4404607 3.3834674 @.00484e)\\n1 16400000 16425000 10 16700000 16725000 : : : : @,255,255 37.0 12.562549 10.18508 12.40789 10.247947 @.00145\\n1 75575000 75600000 10 75750000 75775000 : : : : @,255,255 45.0 17 .23853 15.4369 17.639162 19.101158 2.8978288E-4\\n\\n1@ 124410000 124420000 10 124510000 124520000 : . : . @,255,255 25.0 5.7766066 6.4035115 9.261499 5.8576255 2.999738\\n1@ 86975000 87000000 10 91600000 91625000 : : : : @,255,255 39.0 14.040091 8.2300205 16.515059 10.243796 3.47586\\n1@ 62500000 62525000 10 62825000 62850000 . : . : @,255,255 34.0 10.289001 10.045276 12.961728 9.120338 3.734135\\n1@ 142070000 142080000 10 142130000 142140000 : : : : @,255,255 25.0 5.9950814 6.083401 4.012844 8.395354 2.999736 |\\n1 83030000 83040000 10 83120000 83130000 . : . : @,255,255 30.0 8.352281 8.583961 10.941416 8.548521 9.629116\\n1@ 89400000 89425000 10 89550000 89575000 : : : : @,255,255 58.0 29.796576 25.57457 28.6617 28.369272 9.790702E-4\\n\\n1 76670000 76680000 10 76730000 76740000 : : : : @,255,255 33.0 13.534072 14.624428 14.626635 14.696733 @.00540e\\n1 113280000 113290000 10 113460000 113470000 : : : : @,255,255 21.0 5.724504 4.855233 4.8697643 7.073086 @.0019988\\n1@ 149075000 149100000 10 149175000 149200000 : . : . @,255,255 93.0 41.124496 39.492096 43 .833366 33.744488 2.55326)\\n1@ 76005000 76010000 10 76080000 76085000 : : : : @,255,255 19.0 4.6013317 4.7987475 7.4135013 2.817095 @.00167%\\n1@ 78700000 78705000 10 78800000 78805000 . : . : @,255,255 15.0 4.0699563 2.8393102 4.9301353 1.8224925 @.05222¢8\\n1@ 19950000 19960000 10 20010000 20020000 : : : : @,255,255 29.0 7 .8098607 7 «9720793 8.3413925 10.416836 7.49668)\\n1 100050000 100060000 10 100210000 100220000 . : . : @,255,255 22.0 6.154141 5.54728 6.8347993 4.8881307 7 .241937E-4\\n\\n10 65880000 65890000 10 65970000 65980000 . . . . @,255,255 20.0 7.5167155 5.4971323 5.80507 7.652623 @.029825231\\n\\n',\n",
|
||
" '@ Vivaldi File Edit View Bookmarks Mail Tools Window Help OBeertzk 6 BAB ® S Sat Mar 22 13:41\\n\\neco (ff — > OQ OU Y localhost:8785/graphics/plot_zoom A & Search Startpage Be¢oft & © @ 2\\n\\nSpeed Dial Y Imported From... Y Imported From... Y Online Bewerbung QGIS API Docume... qgis - Trying to pe... New Script - Earth... Pastebin.com - #1... TargetP 2.0-DTU... https://www.mood... OnePlus12R revie... Whois “Indian\"in.. A v »\\n\\nnFeature_RNA nCount_RNA percent.mt\\n\\n2000\\n4000\\n&\\n6\\n1500 3000 e\\nas)\\n=\\n4000 2000 @\\n@\\n1000\\n500 NT\\n©\\nie “*\\nIdentity Identity O\\n\\nS seurat_in3_vign... v Analysis, visualization, anc & RStudio Server ~© Expelled! on Steam ~© ENDER MAGNOLIA: Bloom + @ Ww\\n\\nS\\n\\n& 00 @ 8 © CI Ci Resett @ 100% 13:41\\n',\n",
|
||
" 'lal:\\n\\nimport pandas as pd\\nimport matplotlib.pyplot as plt\\n\\n# Load the BEDPE file into a pandas DataFrame\\nfile_path = \"/mnt/storage3/aman/wdbasejuicer_new/hiccups_output/merged_loops. bedpe\"\\ncolumns = [\\nMehr\", \"xa\", \"2\", \"ehr2\", \"yl\", \"y2\"\\n“name”, “score”, “strandi\", “strand2\",\\n\\n\"color\", \"observed\", “expectedBL\", “expectedDonut\",\\n“expectedi\", “expectedV\", \"fdrBL\", “fdrDonut\",\\n“fdr, “fdrv\"\\n\\ndata = pd.read_csv(file_path, sep=\"\\\\t\", comment=\\'#\\', names=columns)\\n\\n# Calculate the distance based on the midpoints of upstream and downstream loci\\ndata[\"distance\"] = data.apply(\\nLambda row: abs(((row[\"x1\"] + rowl\"x2\"1) / 2) - ((row[\"y1\"] + row(\"y2\"1) / 2))\\nif row[\"chri\"] == row{\"chr2\"] else None,\\naxis=1\\n\\n)\\n|\\n# Drop rows with missing distance (interchromosomal loops or invalid rows)\\ndata = data.dropna(subset=(\"distance\"])\\n\\n# Convert distances to integers\\ndata[\"distance\"] = data[\"distance\"].astype( int)\\n\\n# Plot the distances\\nplt.figure(figsize=(10, 6))\\nplt.hist(data[\"distance\"], bins=100, edgecolor=\"black\")\\n\\nplt.title(\"Distribution of Loop Distances from BEDPE File\")\\nplt.xlabel(\"Distance (bp)\")\\nplt.ylabel(\"Frequency\")\\n\\nplt.grid(axis=\"y\", Linestyle:\\n\\n\", alpha=0.7)\\n\\nplt.show()\\nDistribution of Loop Distances from BEDPE File\\n0.04\\n0.02\\n>\\nFs\\n2\\n3 0.00\\nS\\n&\\n-0.02\\n0.04\\n\\n0.0 0.2 0.4 0.6\\nDistance (bp)\\n\\n08\\n\\n1.0\\n\\n',\n",
|
||
" 'umap_2\\n\\n-2.5\\n\\n2.5\\n\\n5.0\\n',\n",
|
||
" '',\n",
|
||
" 'clonalAbundance\\n\\nWe can also examine the relative distribution of clones by abundance. Here clonalAbundance() will produce a line graph with a total\\nnumber of clones by the number of instances within the sample or run. Like above, we can also group.by this by vectors within the\\ncontig object using the group.by variable in the function.\\n\\nclonalAbundance(combined. TCR,\\ncloneCall = \"gene\",\\nscale = FALSE)\\n\\n5000\\n4000\\nSamples\\n— P17B\\n3 PI7L\\n5 3000\\nro) — P18B\\nao) — P18L\\n3 — P19B\\n2000\\n5 — PI19L\\nZz\\n— P20B\\n— P20L\\n1000\\noo §\\n1 10 100 1000\\nAbundance\\n\\nclonalAbundance() output can also be converted into a density plot, which may allow for better comparisons between different\\nrepertoire sizes, by setting scale = TRUE.\\n\\nclonalAbundance(combined.TCR, cloneCall = \"gene\", scale = TRUE)\\n',\n",
|
||
" 'Aligned_sequences: 2\\n\\n1: Zm00001eb360640_RP\\n\\n2: Zm00001eb360640_RPHt4\\nMatrix: EBLOSUM62\\nGap_penalty: 10.0\\nExtend_penalty: 0.5\\n\\nLength: 480\\n\\nIdentity: 476/480 (99.2%)\\nSimilarity: 478/480 (99.6%)\\nGaps: 0/480 ( 0.0%)\\n\\nScore: 2547.0\\n',\n",
|
||
" 'Figure 4 Genetic separation between\\npopulation pairs. (a) Relative cross\\ncoalescence rates in and out of Africa.\\nAfrican-non-African pairs are shown in red,\\nand pairs within Africa are shown in purple.\\n(b) Relative cross coalescence rates between\\npopulations outside Africa. European—East\\nAsian pairs are shown in blue, Asian-MXL\\npairs are shown in green, and other\\nnon-African pairs are shown in other\\n\\ncolors, as indicated. The pairs that include\\nMXL are masked to include only the putative\\nNative American components. In a and b,\\nthe most recent population separations\\n\\nare inferred from eight haplotypes, that is,\\nfour haplotypes from each population, and\\ncorresponding pairs are indicated by a\\n\\ncross. (c) Comparison of the African—non-\\nAfrican split with simulations of clean splits.\\nWe simulated three scenarios, at split times\\n50,000, 100,000 and 150,000 years ago.\\nThe comparison demonstrates that the history\\nof relative cross coalescence rate between\\nAfrican and non-African ancestors\\n\\nis incompatible with a clean split model\\n\\nand suggests it progressively decreased from\\n\\nRelative cross coalescence rate\\n\\nRelative cross coalescence rate ©\\n\\n0.8\\n\\n0.6\\n\\nO4\\n\\n0.2\\n\\n— MXL-YRI\\n— CEU-YRI\\n— CHB-YRI\\n— CEU-MKK\\n— CEU-LWK\\n~ YRI-MKKT\\n= LWK-MKKt\\n= YRI-LWkt\\n\\n10°\\n\\nTime (years ago)\\n\\n100\\n\\nTime (x1 o years ago)\\n\\n150\\n\\na\\nfs 1.0\\n2\\n2 08 — CHB-CEU\\n8 ~ MXL-CEU\\n8 0.6 — CHB-MXL\\n8 — GIH-MXL\\n8 04 = CHB-GIH\\n3 — GIH-CEut\\n2 02 - CHB-UPT!\\n= CEU-TSI\\n2 o CEU-TSI\\n10°\\n200\\n® 100\\n~ CEU-YRI Fy\\n~ 50,000 years ago, %b 50\\nsimulation XK\\n— 100,000 years ago, 3 20\\nsimulation E\\n= 150,000 years ago, 10\\n\\n200\\n\\nsimulation\\n\\n250\\n\\nbeyond 150,000 years ago to approximately 50,000 years ago. (d) Schematic of population separations. Timings of splits, population separations,\\ngene flow and bottleneck are shown along a logarithmic axis of time.\\n\\n',\n",
|
||
" '39]:\\n\\n41):\\n\\n41):\\n\\n42):\\n\\n42):\\n\\n45]:\\n\\ng\\n]\\n\\nnp.count_nonzero(ac.max_allele() > 1)\\n\\n18628\\n\\nnp. count_nonzero((ac.max_allele() == 1) & ac.is_singleton(1))\\n45120\\n\\n#This is the filtering step\\n\\nflt = (ac.max_allele() == 1) & (ac[:, :2].min(axis=1) > 1)\\n\\ngf = g.compress(flt, axis=0)\\ngn = gf.to_n_alt()\\n',\n",
|
||
" '3.3 SUVRS5 affects the gene region more than the TE region\\n\\neceltrated metation level\\n\\nFecalbrated metiyation level\\n\\n',\n",
|
||
" 'Description\\n\\nContent\\n\\nPrevious knowledge expected\\n\\nObjective\\n\\nTeaching and learning method\\n\\nCourse Criteria and Registration\\n\\nFurther information\\n\\nMinimum of 6-8 weeks research project in laboratory with hands on training in the\\nanalysis of neuroscience data and the building of network models.\\n\\nDepending on the aim of the research project, different methods and questions will\\nbe in focus. For instance:\\n\\n- simulating network models in Julia, Python or Matlab\\n\\n- designing differential equation descriptions of network interactions\\n\\n- mathematical analysis based on dynamical systems\\n\\n- image analysis using ImageJ software\\n\\n- statistical analysis with Julia, Python or Matlab\\n\\n- dimensionality reduction techniques of high-dimensional data\\n\\n- extracting model parameters from experimental data\\n\\n- conceptual discussion and literature searches to understand and propose\\nideas, results, hypotheses\\n\\nStudents are expected to have some mathematical knowledge (linear algebra,\\ndifferential equations) and some programming skills (Matlab, Python or C/C++).\\n\\nUpon successful participation the students can:\\n\\n- Analyze neuroscience data from electrophysiological or calcium imaging\\nrecordings\\n\\n- Build network models of connected excitatory and inhibitory neurons in\\nnumerical simulations\\n\\n- Include synaptic plasticity rules in the network models for the self-organization\\nof network connectivity\\n\\n- Analyze the output of the networks in terms of activity and connectivity\\n\\n- Interpret the numerical results to make predictions for experiments\\n\\nStudents will work in the lab and learn from PhD students.\\n\\nThey will be given detailed instructions and sample numerical code to perform the\\nsimulations.\\n\\nThey will read scientific literature to determine new parameters for their models.\\nThey will learn mathematical methods for writing down differential equations,\\nanalyzing them using dynamical systems and visualizing them from PhD students\\nand sample code from related projects.\\n\\nThey will have weekly meetings with their other PhD students and give regular\\npresentations on their progress to get feedback.\\n\\nThey will get regular help with checking their code and analysis.\\n',\n",
|
||
" 'ORIGINAL RESEARCH article\\n\\nFront. Plant Sci. , 15 December 2022 This article is part of the Research Topic\\nSec. Plant Breeding Genetics and Molecular Breeding in Cereal Crops\\n\\nVolume 13 - 2022 | https://doi.org/10.3389/fpls.2022.1048939 . .\\nVatkirsittsong Ge View all 29 articles >\\n\\nGenome-wide association scan and transcriptome\\nanalysis reveal candidate genes for waterlogging\\ntolerance in cultivated barley\\n\\nHaiye Luan Changyu Chen\" Ju Yang! Hailong Qiao? Hongtao Li*\\n\\nShufeng Li* Junyi Zheng‘4 Huiquan Shen? Xiao Xu2* Jun Wang**\\n\\na College of Marine and Biological Engineering, Yancheng Teachers University, Yancheng, Jiangsu, China\\n\\n2 Institute of Agricultural Science in Jiangsu Coastal Areas, Yancheng, China\\n\\n3 Lianyungang academy of agricultural sciences, Lianyungang, China\\n\\na Jiangsu Provincial Key Laboratory of Coastal Wetland Bioresources and Environmental Protection, Yancheng, Jiangsu, China\\n',\n",
|
||
" 'Core Promoter\\n\\ninsulator\\n\\nat\\n\\nenhancer\\n\\na+\\n\\nsilencer enhancer insulator\\n',\n",
|
||
" '',\n",
|
||
" 'Labs/Group Leaders that interest\\nyou (up to 5)\\n\\nLabs/Group Leaders that interest\\nyou (up to 5)\\n\\nLabs/Group Leaders that interest\\nyou (up to 5)\\n\\nLabs/Group Leaders that interest\\nyou (up to 5)\\n\\nChrista Buecker\\n\\nDaniel Gerlich\\n\\nMarco Hein\\n\\nYan Ma\\n',\n",
|
||
" '100 MB\\n\\n200 MB\\n\\n300 MB\\n\\nChromosomes Show Normalization (Obs | Ctrl) Resolution (BP) Color Range\\n—\\n1 Bp Observed None None Pivrb ttre teins\\n2.5MB 500KB 100KB 25KB 5KB 1KB 200BP a\\n0 MB 100 MB 200 MB 300 MB\\n\\n',\n",
|
||
" '@chri\\n\\n18\\n10\\n18\\n18\\n18\\n18\\n18\\n10\\n18\\n10\\n18\\n18\\n18\\n18\\n18\\n10\\n18\\n10\\n18\\n18\\n18\\n18\\n18\\n10\\n18\\n10\\n18\\n18\\n18\\n18\\n18\\n10\\n18\\n10\\n18\\n18\\n18\\n18\\n18\\n10\\n18\\n10\\n18\\n18\\n18\\n18\\n18\\n10\\n18\\n10\\n18\\n18\\n\\nx1 x2 chr2 y1\\n# juicer_tools version 2.20.00\\n\\n96350000 96375000\\n146950000 146975000\\n137105000 137110000\\n97025000 97050000\\n6630000 6640000 10 6700000\\n118850000 118875000\\n7890008 7900000 10 8010000\\n138700000 138725000\\n136480000 136490000\\n23575000 23600000\\n83125000 83150000\\n76080000 76090000\\n76670000 76680000\\n105200000 105225000\\n96650000 96675000\\n130010000 130020000\\n115920000 115930000\\n25420000 25430000\\n53990000 54000000\\n122850000 122875000\\n130670000 130680000\\n139925000 139930000\\n15550000 15575000\\n135740000 135750000\\n6290000 6300000 10 6340000\\n1810000 1820000 10 2030000\\n136625000 136650000\\n138150000 138175000\\n95120000 95130000\\n112650000 112675000\\n13790000 13800000\\n100225000 100250000\\n139500000 139525000\\n15735000 15740000\\n135830000 135840000\\n88370000 88380000\\n132110000 132120000\\n88000000 88025000\\n129675000 129700000\\n124025000 124050000\\n144250000 144260000\\n81525000 81550000\\n108600000 108625000\\n114275000 114300000\\n145315000 145320000\\n142870000 142875000\\n91490000 91500000\\n140675000 140700000\\n73700000 73725000\\n1390008 1400000 10 1440000\\n127350000 127375000\\n143160000 143165000\\n83525000 83550000\\n\\n18\\n\\nah\\n\\nerananaaAn\\n\\nArAarcAAA\\n\\ny2\\n\\n10\\n10\\n10\\n10\\n6710000\\n10\\n8020000\\n10\\n10\\n10\\n10\\n10\\n10\\n10\\n10\\n10\\n10\\n10\\n10\\n10\\n10\\n10\\n10\\n10\\n6350000\\n2040000\\n10\\n10\\n10\\n10\\n10\\n10\\n10\\n10\\n10\\n10\\n10\\n10\\n10\\n10\\n10\\n10\\n10\\n10\\n10\\n10\\n10\\n10\\n10\\n1450000\\n10\\n10\\n10\\n\\nan\\n\\nname score\\n96500000\\n147225000\\n137165000\\n97175000\\n\\n119050000\\n\\n138875000\\n136880000\\n23750000\\n83225000\\n76130000\\n76730000\\n105400000\\n97200000\\n130080000\\n116180000\\n25480000\\n54060000\\n123025000\\n130920000\\n139980000\\n15875000\\n135830000\\n\\n136875000\\n138300000\\n95190000\\n112875000\\n13870000\\n100375000\\n139650000\\n15875000\\n135890000\\n88560000\\n132190000\\n88100000\\n129800000\\n124200000\\n144310000\\n82000000\\n108825000\\n114675000\\n145390000\\n142955000\\n91590000\\n140825000\\n73925000\\n\\n127500000\\n143200000\\n83675000\\n\\nAEAnNEAAA\\n\\nstrand1 strand2 color\\n\\n96525000\\n147250000\\n137170000\\n97200000\\n\\n119075000\\n\\n138900000\\n136890000\\n23775000\\n83250000\\n76140000\\n76740000\\n105425000\\n97225000\\n130090000\\n116190000\\n25490000\\n54070000\\n123050000\\n130930000\\n139985000\\n15900000\\n135840000\\n\\n136900000\\n138325000\\n95200000\\n112900000\\n13880000\\n100400000\\n139675000\\n15880000\\n135900000\\n88570000\\n132200000\\n88125000\\n129825000\\n124225000\\n144320000\\n82025000\\n108850000\\n114700000\\n145395000\\n142960000\\n91600000\\n140850000\\n73950000\\n\\n127525000\\n143205000\\n83700000\\n\\neEnaAAAA\\n\\n@, 255,255\\n\\n0,255,255\\n\\n0,255,255\\n0,255,255\\n\\n0,255,255\\n\\nobserved\\n\\n13.\\n\\n14.\\n\\n28.\\n29.\\n\\n19.\\n\\n()\\n\\nC)\\n\\n)\\n7)\\n\\n()\\n\\nexpected\\n\\n3.632588\\n\\n1.795993\\n\\n4.812076\\n6.575267\\n\\n5.44378\\n\\nBL expectedDonut expectedH expectedV\\n@,255,255 29.0 6.908377 10@.151942\\n@,255,255 27.0 9.154789 8.617996\\n@,255,255 14.0 2.158902 2.7971497\\n@,255,255 41.0 7 .2368855 8.743596\\n41 2.4897213 @.8575747 @.8734342\\n@,255,255 28.0 13.456251 10.144169\\n3 2.0402029 @.80188763 3.493806\\n@,255,255 51.0 22.70316 18.342337\\n@,255,255 56.0 5.8624353 4.0235314\\n@,255,255 29.0 11.724993 11.466535\\n@,255,255 29.0 7 .7183127 10.377976\\n@,255,255 18.0 4.8504276 4.9768763\\n@,255,255 26.0 6.9975615 8.650545\\n@,255,255 32.0 14.664846 12.554932\\n@,255,255 28.0 4.0343704 5.9642544\\n@,255,255 19.0 4.2099385 5.5361204\\n@,255,255 18.0 5.1179023 3.862153\\n@,255,255 25.0 8.612685 8.873278\\n@,255,255 12.0 0.8 1.1661497 1\\n@,255,255 57.0 16.95546 16.73514\\n@,255,255 16.0 2.5194237 2.3019972\\n@,255,255 24.0 2.3304455 2.4300778\\n@,255,255 26.0 7.914436 6.2744865\\n@,255,255 25.0 7 «3391337 6.318109\\n6 4.8888364 10.467556 10.242416\\n5.331487 10.049363 5.0511484\\n@,255,255 32.0 6.915425 8.965726\\n@,255,255 28.0 10.776969 9.31679 9\\n@,255,255 17.0 3.5197115 3.931131\\n@,255,255 35.0 17.027737 13.964403\\n@,255,255 25.0 6.944053 6.3366694\\n@,255,255 40.0 21.412172 16.181568\\n@,255,255 38.0 12.548413 10.104653\\n@,255,255 14.0 2.6595442 2.7529666\\n@,255,255 34.0 8.192702 7 «7228875\\n@,255,255 16.0 3.9849634 2.7454743\\n@,255,255 37.0 10.342418 6.290118\\n@,255,255 74.0 16.834267 20.73217\\n@,255,255 42.0 15 .604897 13.728988\\n@,255,255 24.0 6.7028656 6.406737\\n@,255,255 15.0 1.6143898 1.8003566\\n@,255,255 33.0 7.416659 8.165755\\n@,255,255 37.0 14.713646 15.724737\\n@,255,255 39.08 14.375554 12.771065\\n@,255,255 15.0 2.1382039 1.4367672\\n@,255,255 15.0 3.1051931 2.2679374\\n@,255,255 16.0 3.9473543 3.485225\\n@,255,255 33.0 11.217789 11.812868\\n@,255,255 30.0 13.629446 12.344826\\n6.0781612 3.9163964 8.624782 7)\\n@,255,255 28.0 6.3772235 9.014559\\n@,255,255 20.0 3.8732347 2.6100166\\n@,255,255 31.0 14.015409 11.044764\\n\\na AEE AEE\\n\\nan\\n\\na\\n\\na @Frleaas\\n\\na GAMEnAL\\n\\nfdrBL fdrDonut fdrH fdrv\\n10.472101 12.340119 3.47026\\n9.679461 11.48677 7.35876\\n3.8800046 0.8 2.8562572E-4\\n4.869102 13.810099 3.54995\\n@.01728987 @.001242975 5.33635\\n13.075987 12.055305 0.05465\\n4.3050753E-5 3.251599E-4 6.60865\\n25.18047 20.083973 8.8781¢\\n9.664011 6.9882493 2.12045\\n12.26452 11.205023 0.00277\\n11.7499895 11.715163 3.4702¢\\n5.691984 5.5311675 5.48686\\n7.492755 9.163101 2.48813\\n12.808813 13.2220125 0.00637\\n5.991131 9.22503 2.7857003E-9\\n4.5337906 5.16895 1.8311806E-4\\n3.3995097 2.1366236 @.00425\\n6.3163705 7.722182 0.00164\\n+ 9533825 1.5174333 @.0016104523\\n19.956165 23.627089 1.49798\\n2.236289 4.558328 1.0232¢\\n3.4856222 0.0 1.0080401E-11\\n7.141891 5.541783 0.0016¢\\n5.6332297 4.524137 7.11818\\n8.6972335E-10 5.615786E-10 0.00345\\n9.0612576E-7 8.57902E-9 0.00168\\n9.6514015 13.68228 9.12031\\n-459834 12.041199 @.005384676\\n3.6762645 4.0894384 2.29942\\n17 .076668 16.887 0.019946687\\n6.3153477 8.540193 7.11818\\n12.59978 23.563076 0.03405\\n16.899843 8.591589 9.93427\\n2.8329241 3.0919983 @.0021°5\\n11.096409 15.422808 2.66138\\n3.1387365 2.706753 0.0042:\\n13.432438 7 .5078983 1.87813\\n23.39707 26.79889 9.43818\\n16.223768 20.301794 3.47344\\n9.599116 6.978916 6.54658\\n2.9674728 2.0819232 7.78812\\n8.763162 9.38747 2.5513604E-8\\n15.199178 18.900688 2.11388\\n13.735151 17.078064 4.40505\\n1.8874567 1.2252359 7.11086\\n1.9953543 3.4748816 6.31051\\n4.167455 4.286915 0.0042:\\n14.095939 11.337819 1.22108\\n14.721695 14.512271 0.01982\\n- 0016947468 @.001145698 2.288088E-5\\n11.647564 6.3732767 1.08306\\n2.5964506 5.1224613 1.31442\\n7 .6308675 14.382173 @.01131\\n\\nmy ANAEALE\\n\\na SA. aRE a aQnEea:\\n',\n",
|
||
" 'In [10]:\\n\\nlibrary (Seurat)\\nlibrary (SeuratObject)\\n\\nWarning message:\\n“package ‘Seurat’ was built under R version 4.4.2”\\nLoading required package: SeuratObject\\n\\nWarning message:\\n\\n“package ‘SeuratObject’ was built under R version 4.4.2”\\nError: package or namespace load failed for ‘SeuratObject’:\\nobject ‘recvData’ is not exported by \\'namespace:parallel\\'\\n\\nError: package ‘SeuratObject’ could not be loaded\\nTraceback:\\n\\n1. .getRequiredPackages2(pkgInfo, quietly = quietly, lib.loc = c(lib.loc,\\n. . LibPaths()))\\n\\n2. stop(gettextf(\"package %s could not be loaded\", sQuote(pkg)),\\n. call. = FALSE, domain = NA)\\n\\n3. »handleSimpleError(function (cnd)\\n~f{\\n. watcher$capture_plot_and_output ()\\n\\ncnd <— sanitize_call(cnd)\\n\\nwatcher$push(cnd)\\n\\nswitch(on_error, continue = invokeRestart(\"eval_continue\"),\\n\\n. stop = invokeRestart(\"eval_stop\"), error = NULL)\\n\\n. }, “package ‘SeuratObject’ could not be loaded\", base::quote(NULL) )\\n',\n",
|
||
" 'Leaf Hi-C\\n\\nN=OS (fihered) 347 (unique) 347 (total), PALL = 0,909\\n\\neQTL-gene —\\nlinks >20 kb .\\n\\nshuffled pairs\\n\\n',\n",
|
||
" '254\\n\\n255 head(sub_combined[ ,c(1,2,13)])\\n\\n256 lLibrary(Seurat)\\n\\n257 DimPlot(sub_combined, group.by = \"TRA_cLuster\") +\\n\\n258 scale_color_manual(values = hcl.colors(n=Length(unique(scRep_example@meta.data[, \"TRA_cluster\"]>\\n259 NoLegend()\\n\\n260 filast few code blocks not run\\n\\n261 #Combining Clones and Single-Cell Objects\\n\\n262\\n260:1 = (Top Level) = R Script +\\nConsole’ Terminal Background Jobs elo\\nR~ R4.4.2 - ~/\\n\\nAttaching package: ‘SeuratObject’\\nThe following objects are masked from ‘package:base’:\\nintersect, t\\n\\n> DimPlotCsub_combined, group.by = \"TRA_cluster\") +\\n+ scale_color_manual(values = hcl.colors(n=LengthCunique(scRep_example@meta.data[,\"TRA_cluster\"])),\\n\"inferno\")) +\\n+ NoLegend()\\nError in UseMethod(generic = \"DefaultAssay\", object = object) :\\nno applicable method for \\'DefaultAssay\\' applied to an object of class \"data.frame\"\\n',\n",
|
||
" 'percent.mt\\n\\n§ group1\\n( group2\\n\\nIdentity\\n',\n",
|
||
" 'In [66]: contig_list <- lapply(contig_list, function(df) {\\n\\ndf$chain <- as.character(df$chain)\\n\\ndf$cdr3_nt <— as.character(df$cdr3_nt)\\ndf$cdr3_aa <— as.character(df$cdr3_aa)\\ndf$barcode <— as.character(df$barcode)\\ndf$v_gene <- as.character(df$v_gene)\\n\\ndf$j_gene <- as.character(df$j_gene)\\n\\ndf$d_gene <- as.character(df$d_gene)\\n\\ndf$c_gene <- as.character(df$c_gene)\\n\\ndt\\n})\\nError in ‘$<-.data.frame*(**xtmpx*, \"cdr3_aa\", value = character(@)): replacement has @ rows, data has 1104\\nTraceback:\\n1. FUN(X[[il], ...)\\n2. ~$<->(**tmp*, \"cdr3_aa\", value = character(Q))\\n3. \\\\$<-.data. frame’ (**tmp**, \"cdr3_aa\", value = character(Q))\\n4. stop(sprintf(ngettext(N, \"replacement has %d row, data has %d\",\\n\\n. “replacement has %d rows, data has %d\"), N, nrows), domain = NA)\\n5. .«handleSimpleError(function (cnd)\\n\\n»f\\nwatcher$capture_plot_and_output()\\ncnd <- sanitize_call(cnd)\\nwatcher$push(cnd)\\nswitch(on_error, continue = invokeRestart(\"eval_continue\"),\\n\\n. stop = invokeRestart(\"eval_stop\"), error = NULL)\\n\\n. }, “replacement has @ rows, data has 1104\", base::quote(*$<-.data. frame* (**xtmp**,\\n\"cdr3_aa\", value = character(Q))))\\n',\n",
|
||
" 'In [172]: combined_seurat <- combineExpression(\\ncombined_TCR,\\ncombined_seurat,\\ncloneCall = \"strict\",\\nproportion = FALSE\\n)\\n\\nError in combineExpression(combined_TCR, combined_seurat, cloneCall = \"strict\", : Adjust the cloneSize parameter —\\nthere are groupings < 1\\nTraceback:\\n\\n1. stop(\"Adjust the cloneSize parameter - there are groupings < 1\")\\n2. .handleSimpleError(function (cnd)\\n-{\\nwatcher$capture_plot_and_output()\\ncnd <- sanitize_call(cnd)\\nwatcher$push(cnd)\\nswitch(on_error, continue = invokeRestart(\"eval_continue\"),\\nstop = invokeRestart(\"eval_stop\"), error = NULL)\\n. }, \"Adjust the cloneSize parameter - there are groupings < 1\",\\nbase: : quote(combineExpression(combined_TCR, combined_seurat,\\ncloneCall = \"strict\", proportion = FALSE) ))\\n',\n",
|
||
" 'Loading and Processing Contig Data\\n\\nWhat data to load into scRepertoire?\\n\\nscRepertoire functions using the filtered_contig_annotations.csv output from the 10x Genomics Cell Ranger. This file is located in the\\n./outs/ directory of the VDJ alignment folder. To generate a list of contigs to use for scRepertoire:\\n\\n+ load the filtered_contig_annotations.csv for each of the samples.\\n* make a list in the R environment.\\n\\nsl <- /Sample1/outs/filtered_contig_annotations.csv\")\\n$2 <- /Sample2/outs/filtered_contig_annotations.csv\")\\n$3 <- /Sample3/outs/filtered_contig_annotations.csv\")\\nS4<- ../Sample4/outs/filtered_contig_annotations.csv\")\\n\\ncontig_list <- list(S1, $2, $3, $4)\\n\\nOther alignment workflows\\n\\nBeyond the default 10x Genomic Cell Ranger pipeline outputs, scRepertoire supports the following single-cell formats:\\n\\n+ AIRR\\n\\nBD Rhapsody Multiomic Immune Profiling\\nImmcantation\\n\\nJSON-formatted contig data\\n\\nMiXCR\\n\\nOmniscope OS-T/0S-B\\n\\nParse Evercode TCR/BCR\\n\\nTRUST4\\n\\nWAT3R.\\n\\nloadContigs() can be given a directory where the sequencing experiments are located and it will recursively load and process the\\ncontig data based on the file names. Alternatively, loadContigs() can be given a list of data frames and process the contig data\\n\\n#Directory example\\n\\ncontig. output <- c(\"~/Documents/MyExper iment\")\\n\\ncontig. list <- loadContigs(input = contig. output,\\nformat = \"TRUST4\")\\n\\n#List of data frames example\\n\\nSl <- ‘/Documents/MyExperiment/Sample1/outs/barcode_results.csv\")\\n$2 <- ‘/Documents/MyExperiment/Sample2/outs/barcode_results.csv\")\\n$3 <- /Documents/MyExperiment/Sample3/outs/barcode_results.csv\")\\n\\n$4 <- read.csv(\"~/Documents/MyExperiment/Sample4/outs/barcode_results.csv\")\\n\\ncontig_list <- list(S1, $2, $3, $4)\\ncontig. list <- loadContigs(input = contig. output,\\nformat = \"WAT3R\")\\n\\n',\n",
|
||
" '2]\\n\\nv\\n\\nLibrary(tidyverse)\\n\\n# import gene_contrast table\\nDEGs <- list. files(pattern = (\"/mnt/volume/data/group8/funcourse/workf lows/scripts/deseq2_results.csv\"))\\nstr(DEGs)\\ndatalist_deg = list()\\nfor (i in DEGs) {\\ndeg_df <- read.table(i, se\\ndeg_df <- deg_df %%\\nselect(ID, module_name) %>%\\n#mutate (Accession = str_split_n(module_name, \"\\nfilter(padj < 0.05)\\ndatalist_deg[[i]] <- deg_df\\n\\nheader = TRUE, stringsAsFactors = FALSE)\\n\\n\", 1))\\n\\n}\\ngene_contrast <- do.call(rbind, datalist_deg)\\nrownames(gene_contrast) <~ NULL\\ndim(gene_contrast)\\nhead(gene_contrast)\\n# import term2gene tables\\n# and fuse them into single df per species\\nsetwd(\"/mnt/volume/data/group8/references/\")\\nmercator_names <- list.files(pattern = (\"mercatorx\"))\\nstr(mercator_names)\\ndatalist_termagene = list()\\nfor (i in mercator_names) {\\nmercator_df <- read.table(i, se\\nmercator_df <- mercator_df %%\\nmutate(Filename = paste0(i)) %o%\\nrename(Term = V2, Gene = V1) %>%\\n#mutate(Level = str_extract(Filename, regex(\"level[1-8]\"))) %>%\\nmutate(Level = str_split_i(Filename, \"_\", 2)) #%%\\n#select (-Filename)\\ndatalist_term2gene[[i]] <- mercator_df\\n\\n,\", stringsAsFactors = FALSE)\\n\\n}\\n\\nterm2gene <- do.call(rbind, datalist_term2gene)\\nrownames(term2gene) <- NULL\\n\\ndim(termagene)\\n\\nhead (term2gene)\\n\\nHt\\n\\nprint(\"Imports are finished\")\\n\\n#\\n\\n17s\\n\\nchr(@)\\n\\nNULL\\n\\nNULL\\n\\nchr [1:10] \"mercator_level1_barley.csv\" \"mercator_level2_barley.csv\" ...\\n\\n358250 - 4\\n\\nan ona\\n\\nA data.frame: 6 x 4\\nGene Term Filename Level\\n\\n<chr> <chr> <chr> <chr>\\nHORVU.MOREX.r3.2HGO150570 Photosynthesis mercator_level1_barley.csv _ level\\nHORVU.MOREX.r3.2HG0113330 Photosynthesis _mercator_level1_barley.csv _levelt\\nHORVU.MOREX.r3.3HG0319250 Photosynthesis mercator_level1_barley.csv _ level\\nHORVU.MOREX.r3.6HG0546280 Photosynthesis mercator_levelt_barley.csv _ level\\nHORVU.MOREX.r3.7HGO710620 Photosynthesis mercator levell barlev.csv _ level\\n',\n",
|
||
" 'Metrics for 2V_COMPLETE\\n\\n40\\n16\\n0.75-1.0 30\\n65 .\\n0.5-0.75 © Metric\\n= 20 P)) aREA\\n> 1 maxmin\\n0.25-0.5\\n10 92\\n0-0.25\\n0 \"1 19 18\\n0-0.25 0.50-0.75 —-0.75-1.0\\n\\nnterval\\n',\n",
|
||
" '(1900)\\n\\nae)\\n\\n§ g\\n\\nono] uone nine poresqyeooy,\\n\\n(1900)\\n\\nae)\\n\\nHI 4 5\\n\\nono] uoneninew poresqyeooK,\\n\\n(188)\\n\\nTen)\\n\\n(saa)\\n‘samples — methyome_ 16 suvis TE — mettylome. merged. WT_AILTE — mettylome.mett_TE\\n\\ncae\\n\\n5 Fy g\\n\\nono] uone nine poresqyeooy,\\n',\n",
|
||
" 'Dy Du ow\\n\\nb water_res <- results(dds, contrast = c(\"isolate\", \"treatment\", \"time\"))\\nwater_up <- subset(water_res, padj < 0.05 & log2FoldChange > 1) # Upregulated in drought\\nwater_down <- subset(water_res, padj < @.@5 & log2FoldChange < -1)| # Downregulated in drought\\n\\n(25]| @ 00s R\\n\\nError in cleanContrast(object, contrast, expanded = isExpanded, listValues = listValues, : treatment and time should be levels of isolate such that isolate_treatment_vs_Franklin and isolate_time_vs_Franklin are contained in ‘result\\nTraceback:\\n\\n1. cleanContrast(object, contrast, expanded = isExpanded, listValues = listValues,\\n. test = test, useT = useT, minmu = minmu)\\n\\n2. stop(paste(contrastNumLevel, \"and\", contrastDenomLevel, \"should be levels of\",\\n. contrastFactor, \"such that\", contrastNumColumn, “and\", contrastDenomColumn,\\n. “are contained in \\'resultsNames(object)\\'\"))\\n\\n3. «handleSimpleError(function (cnd)\\naf\\n. watcher$capture_plot_and_output()\\n\\n. cnd <- sanitize_call(cnd)\\n\\n. watcher$push(cnd)\\n\\n. switch(on_error, continue = invokeRestart(\"eval_continue\"),\\n\\n. stop = invokeRestart(\"eval_stop\"), error = NULL)\\n\\n. }, \"treatment and time should be levels of isolate such that isolate_treatment_vs_Franklin and isolate_time_vs_Franklin are contained in \\'resultsNames(object)\\'\",\\n. base: :quote(cleanContrast(object, contrast, expanded = isExpanded,\\n\\n. listValues = listValues, test = test, useT = useT, minmu = minmu)))\\n\\n',\n",
|
||
" 'Method\\n\\nHeterozygosity\\n\\nNucleotide diversity (tt)\\n\\nSite Frequency Spectrum (SFS)\\nLinkage Disequilibrium (LD)\\nTajima’s D\\n\\nRuns of Homozygosity (ROH)\\n\\nEffective Population Size (Ne)\\n\\nSignature of Bottleneck\\n\\nDecreased heterozygosity\\n\\nReduced genetic diversity\\n\\nSkew toward intermediate alleles\\n\\nIncreased LD, slower decay\\n\\nPositive values due to allele frequency shift\\nLonger ROH in bottlenecked populations\\n\\nSudden decrease in Ne\\n',\n",
|
||
" 'S\\n\\nray infection with P. syringae pv. tomato DC3000\\n\\n600\\n3\\n2D 400\\n2 § P Syringae\\nze DC3000\\n23\\n2 200\\nsy\\ns\\na\\n\\ni}\\n\\nEVI A*LORE-OE2\\n(n=12, OD600= 0,02, 3 dpi)\\n',\n",
|
||
" 'Insights from the study\\n\\n> Total identified loops according to the study (long-range\\nloops 2 20 kb): 1,177;\\n\\n> Paper only analyzed chromatin loops 2 20 kb in length in\\nthe Hi-C dataset\\n\\n> Resolutions and parameters not mentioned in paper and\\nSl\\n\\n> Less no. of chromatin loops identified due to limited\\nsequencing depth.HenceHiChIP to detect more loops\\n\\n> H3K4me3-HiChIP dataset: 24,141 loops;\\n> H3K27me3-HiChIP dataset: 18,106 loops\\n\\n> FitHiC2 on resolution 20kb : 89000 loops\\n',\n",
|
||
" \"Where you're going broad:\\n\\n— Integrating multiple loop callers (Peakachu, Mustache, SIP, FitHiC2, Cooltools) and comparing overlaps\\nwithout a narrowed hypothesis or defined benchmark.\\n\\n— Using RNA-seq, Micro-C, ChIP-seq, compartment analysis, insulation scores, saddle plots, pileups, and\\nmultiple chromatin states simultaneously without prioritization.\\n\\n— Experimenting with too many ML techniques (XGBoost, MLP, CNN, GNN, clustering, autoencoders, NMF,\\n\\nt-SNE, UMAP) without selecting one core predictive or generative objective.\\n\\n- Combining tasks from loop classification, differential loop analysis, feature interpretation, and integrative\\nmodeling into the same sprint, diluting focus.\\n\\nFix:\\n\\n— Freeze one analytical axis per phase. Example: “This week = classify loops using only structural\\n\\nfeatures via XGBoost. No RNA-seq, no CNN, no compartments.”\\n\\n— Lock hypotheses. Example: “Gained loops with increased H3K27me3 predict gene repression.” Don’t\\n\\ndeviate until falsified or validated.\\n\\n- Version objectives. Tag experimental branches (e.g., loop_model_v1_xgb_only ,\\nloop_model_v2_with_ctcf ), treat each as immutable snapshots.\\n\\n— Kill parallel prototypes. Run one model end-to-end. Archive others. Resume only if the current fails\\nreproducibility or signal.\\n\\n— Define exit criteria. Every analysis must have a measurable endpoint—e.g., loop F1-score = baseline,\\n\\nAexpression correlation with loop gain = threshold. Don’t continue beyond that frontier without cause.\\n\",\n",
|
||
" '= F, compares the average expected heterozygosity of\\nindividual subpopulations (S) to the total expected\\nheterozygosity if the subpopulations are combined (T).\\n\\npy, = n= Hs) -\\\\-(%)\\nH, H,\\n',\n",
|
||
" '100\\n\\n75\\n\\n25\\n\\nPCA of Genotype Data (K-Means Clusters)\\n\\n2.00\\n\\n1.75\\n\\n1.50\\n\\n1.25\\n\\n0.75\\n\\n0.50\\n\\n0.25\\n\\n0.00\\n\\nCluster\\n',\n",
|
||
" 'Ancestry Proportion\\n\\n08\\n\\n0.6\\n\\n0.4\\n\\n0.2\\n\\n0.0\\n\\nAncestry Proportions by PCA Clusters (K=3)\\n\\n10\\n\\n20 30 40\\nIndividuals (grouped by PCA clusters)\\n\\n50\\n\\n60\\n\\nCluster 0\\n@@m™ Cluster 1\\nMH Cluster 2\\n',\n",
|
||
" 'Visualization: HiGlass\\n\\nHICCUPS juicer_tools:\\n\\nbbedpe file\\n\\nEnrichmnet: Juicer\\nAPA,\\n‘TADs: Arrowhead\\n\\nJuicer\\n\\nVisualization: JuiceBox\\nAnalysis: HIC Straw\\n\\nTiimmomatic, FastQc\\n\\nHic-Pro\\n(Current)\\n\\nvalidpairs file\\n\\nAnalysis: Cooler\\nlibrary python\\n\\n>\\n\\nFithiC2 loop caller\\n\\nEnrichment:\\ncoolpup.py\\n',\n",
|
||
" 'In [892]: intersect(combined.TCR_p3[[i]]$barcode, rownames(patient3_transform@meta. data) )\\n\\nlength( intersect (combined. TCR_p3[[i]]$barcode, rownames(patient3_transform@meta.data) ))\\n']"
|
||
]
|
||
},
|
||
"execution_count": 6,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"text_scr"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
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|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
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||
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|
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|
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||
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|
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|
||
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|
||
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|
||
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|
||
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|
||
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||
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|
||
" <td>Dy\\n\\n# Import python package for working with...</td>\n",
|
||
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|
||
" <tr>\n",
|
||
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|
||
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|
||
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|
||
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|
||
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|
||
" <td>= F, compares the average expected heterozygos...</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>785</th>\n",
|
||
" <td>100\\n\\n75\\n\\n25\\n\\nPCA of Genotype Data (K-Mea...</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>786</th>\n",
|
||
" <td>Ancestry Proportion\\n\\n08\\n\\n0.6\\n\\n0.4\\n\\n0.2...</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>787</th>\n",
|
||
" <td>Visualization: HiGlass\\n\\nHICCUPS juicer_tools...</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>788</th>\n",
|
||
" <td>In [892]: intersect(combined.TCR_p3[[i]]$barco...</td>\n",
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" cols\n",
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"0 Generate .pairs and bam files\\n\\nThe pairtools...\n",
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"2 4. scRepertoire on patient 4\\n\\nIn [51]: libra...\n",
|
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|
||
"4 Dy\\n\\n# Import python package for working with...\n",
|
||
".. ...\n",
|
||
"784 = F, compares the average expected heterozygos...\n",
|
||
"785 100\\n\\n75\\n\\n25\\n\\nPCA of Genotype Data (K-Mea...\n",
|
||
"786 Ancestry Proportion\\n\\n08\\n\\n0.6\\n\\n0.4\\n\\n0.2...\n",
|
||
"787 Visualization: HiGlass\\n\\nHICCUPS juicer_tools...\n",
|
||
"788 In [892]: intersect(combined.TCR_p3[[i]]$barco...\n",
|
||
"\n",
|
||
"[789 rows x 1 columns]"
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|
||
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|
||
"df"
|
||
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|
||
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{
|
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"cell_type": "code",
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|
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"text": [
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"<>:5: SyntaxWarning: invalid escape sequence '\\s'\n",
|
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"<>:6: SyntaxWarning: invalid escape sequence '\\S'\n",
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"<>:5: SyntaxWarning: invalid escape sequence '\\s'\n",
|
||
"<>:6: SyntaxWarning: invalid escape sequence '\\S'\n",
|
||
"/var/folders/2b/p6vqq2k12pg8k1hzfrp2smbw0000gn/T/ipykernel_38112/3689161515.py:5: SyntaxWarning: invalid escape sequence '\\s'\n",
|
||
" text = re.sub('\\s+', ' ', text) # Remove extra spaces\n",
|
||
"/var/folders/2b/p6vqq2k12pg8k1hzfrp2smbw0000gn/T/ipykernel_38112/3689161515.py:6: SyntaxWarning: invalid escape sequence '\\S'\n",
|
||
" text = re.sub('\\S*@\\S*\\s?', '', text) # Remove emails\n"
|
||
]
|
||
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|
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|
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|
||
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|
||
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|
||
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||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
" <th>2</th>\n",
|
||
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|
||
" <td>screpertoire on patient in library ...</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
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|
||
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|
||
" <td>jf i a aligned seq...</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>4</th>\n",
|
||
" <td>Dy\\n\\n# Import python package for working with...</td>\n",
|
||
" <td>dy import python package for working with co...</td>\n",
|
||
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|
||
" <tr>\n",
|
||
" <th>...</th>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>784</th>\n",
|
||
" <td>= F, compares the average expected heterozygos...</td>\n",
|
||
" <td>f compares the average expected heterozygos...</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>785</th>\n",
|
||
" <td>100\\n\\n75\\n\\n25\\n\\nPCA of Genotype Data (K-Mea...</td>\n",
|
||
" <td>pca of genotype data k means cluste...</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>786</th>\n",
|
||
" <td>Ancestry Proportion\\n\\n08\\n\\n0.6\\n\\n0.4\\n\\n0.2...</td>\n",
|
||
" <td>ancestry proportion ancestr...</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>787</th>\n",
|
||
" <td>Visualization: HiGlass\\n\\nHICCUPS juicer_tools...</td>\n",
|
||
" <td>visualization higlass hiccups juicer tools b...</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>788</th>\n",
|
||
" <td>In [892]: intersect(combined.TCR_p3[[i]]$barco...</td>\n",
|
||
" <td>in intersect combined tcr p i barco...</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"<p>789 rows × 2 columns</p>\n",
|
||
"</div>"
|
||
],
|
||
"text/plain": [
|
||
" cols \\\n",
|
||
"0 Generate .pairs and bam files\\n\\nThe pairtools... \n",
|
||
"1 .\\n\\n@ Vivaldi File Edit View Bookmarks Mail T... \n",
|
||
"2 4. scRepertoire on patient 4\\n\\nIn [51]: libra... \n",
|
||
"3 jf====\\n\\n#\\n#\\n#\\n#\\n#\\n#\\n#\\n#\\n#\\n#\\ni\\n#\\n... \n",
|
||
"4 Dy\\n\\n# Import python package for working with... \n",
|
||
".. ... \n",
|
||
"784 = F, compares the average expected heterozygos... \n",
|
||
"785 100\\n\\n75\\n\\n25\\n\\nPCA of Genotype Data (K-Mea... \n",
|
||
"786 Ancestry Proportion\\n\\n08\\n\\n0.6\\n\\n0.4\\n\\n0.2... \n",
|
||
"787 Visualization: HiGlass\\n\\nHICCUPS juicer_tools... \n",
|
||
"788 In [892]: intersect(combined.TCR_p3[[i]]$barco... \n",
|
||
"\n",
|
||
" cleaned_cols \n",
|
||
"0 generate pairs and bam files the pairtools sp... \n",
|
||
"1 vivaldi file edit view bookmarks mail tools ... \n",
|
||
"2 screpertoire on patient in library ... \n",
|
||
"3 jf i a aligned seq... \n",
|
||
"4 dy import python package for working with co... \n",
|
||
".. ... \n",
|
||
"784 f compares the average expected heterozygos... \n",
|
||
"785 pca of genotype data k means cluste... \n",
|
||
"786 ancestry proportion ancestr... \n",
|
||
"787 visualization higlass hiccups juicer tools b... \n",
|
||
"788 in intersect combined tcr p i barco... \n",
|
||
"\n",
|
||
"[789 rows x 2 columns]"
|
||
]
|
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},
|
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"execution_count": 8,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
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],
|
||
"source": [
|
||
"import re\n",
|
||
"\n",
|
||
"# Preprocess the text data\n",
|
||
"def preprocess_text(text):\n",
|
||
" text = re.sub('\\s+', ' ', text) # Remove extra spaces\n",
|
||
" text = re.sub('\\S*@\\S*\\s?', '', text) # Remove emails\n",
|
||
" text = re.sub('\\'', '', text) # Remove apostrophes\n",
|
||
" text = re.sub('[^a-zA-Z]', ' ', text) # Remove non-alphabet characters\n",
|
||
" text = text.lower() # Convert to lowercase\n",
|
||
" return text\n",
|
||
"\n",
|
||
"df['cleaned_cols'] = df['cols'].apply(preprocess_text)\n",
|
||
"df"
|
||
]
|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
{
|
||
"name": "stderr",
|
||
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|
||
"text": [
|
||
"[nltk_data] Downloading package stopwords to /Users/aman/nltk_data...\n",
|
||
"[nltk_data] Package stopwords is already up-to-date!\n"
|
||
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|
||
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|
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|
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|
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|
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|
||
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|
||
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|
||
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|
||
" <tr>\n",
|
||
" <th>0</th>\n",
|
||
" <td>Generate .pairs and bam files\\n\\nThe pairtools...</td>\n",
|
||
" <td>generate pairs and bam files the pairtools sp...</td>\n",
|
||
" <td>[generate, pairs, bam, files, pairtools, split...</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>1</th>\n",
|
||
" <td>.\\n\\n@ Vivaldi File Edit View Bookmarks Mail T...</td>\n",
|
||
" <td>vivaldi file edit view bookmarks mail tools ...</td>\n",
|
||
" <td>[vivaldi, file, edit, view, bookmarks, mail, t...</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>2</th>\n",
|
||
" <td>4. scRepertoire on patient 4\\n\\nIn [51]: libra...</td>\n",
|
||
" <td>screpertoire on patient in library ...</td>\n",
|
||
" <td>[screpertoire, patient, library, screpertoire,...</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>3</th>\n",
|
||
" <td>jf====\\n\\n#\\n#\\n#\\n#\\n#\\n#\\n#\\n#\\n#\\n#\\ni\\n#\\n...</td>\n",
|
||
" <td>jf i a aligned seq...</td>\n",
|
||
" <td>[jf, aligned, sequences, zm, eb, rp, zm, eb, r...</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>4</th>\n",
|
||
" <td>Dy\\n\\n# Import python package for working with...</td>\n",
|
||
" <td>dy import python package for working with co...</td>\n",
|
||
" <td>[dy, import, python, package, working, cooler,...</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>...</th>\n",
|
||
" <td>...</td>\n",
|
||
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|
||
" <td>...</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>784</th>\n",
|
||
" <td>= F, compares the average expected heterozygos...</td>\n",
|
||
" <td>f compares the average expected heterozygos...</td>\n",
|
||
" <td>[compares, average, expected, heterozygosity, ...</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>785</th>\n",
|
||
" <td>100\\n\\n75\\n\\n25\\n\\nPCA of Genotype Data (K-Mea...</td>\n",
|
||
" <td>pca of genotype data k means cluste...</td>\n",
|
||
" <td>[pca, genotype, data, means, clusters, cluster]</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>786</th>\n",
|
||
" <td>Ancestry Proportion\\n\\n08\\n\\n0.6\\n\\n0.4\\n\\n0.2...</td>\n",
|
||
" <td>ancestry proportion ancestr...</td>\n",
|
||
" <td>[ancestry, proportion, ancestry, proportions, ...</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>787</th>\n",
|
||
" <td>Visualization: HiGlass\\n\\nHICCUPS juicer_tools...</td>\n",
|
||
" <td>visualization higlass hiccups juicer tools b...</td>\n",
|
||
" <td>[visualization, higlass, hiccups, juicer, tool...</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>788</th>\n",
|
||
" <td>In [892]: intersect(combined.TCR_p3[[i]]$barco...</td>\n",
|
||
" <td>in intersect combined tcr p i barco...</td>\n",
|
||
" <td>[intersect, combined, tcr, barcode, data, leng...</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
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|
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|
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|
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|
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" cols \\\n",
|
||
"0 Generate .pairs and bam files\\n\\nThe pairtools... \n",
|
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|
||
"2 4. scRepertoire on patient 4\\n\\nIn [51]: libra... \n",
|
||
"3 jf====\\n\\n#\\n#\\n#\\n#\\n#\\n#\\n#\\n#\\n#\\n#\\ni\\n#\\n... \n",
|
||
"4 Dy\\n\\n# Import python package for working with... \n",
|
||
".. ... \n",
|
||
"784 = F, compares the average expected heterozygos... \n",
|
||
"785 100\\n\\n75\\n\\n25\\n\\nPCA of Genotype Data (K-Mea... \n",
|
||
"786 Ancestry Proportion\\n\\n08\\n\\n0.6\\n\\n0.4\\n\\n0.2... \n",
|
||
"787 Visualization: HiGlass\\n\\nHICCUPS juicer_tools... \n",
|
||
"788 In [892]: intersect(combined.TCR_p3[[i]]$barco... \n",
|
||
"\n",
|
||
" cleaned_cols \\\n",
|
||
"0 generate pairs and bam files the pairtools sp... \n",
|
||
"1 vivaldi file edit view bookmarks mail tools ... \n",
|
||
"2 screpertoire on patient in library ... \n",
|
||
"3 jf i a aligned seq... \n",
|
||
"4 dy import python package for working with co... \n",
|
||
".. ... \n",
|
||
"784 f compares the average expected heterozygos... \n",
|
||
"785 pca of genotype data k means cluste... \n",
|
||
"786 ancestry proportion ancestr... \n",
|
||
"787 visualization higlass hiccups juicer tools b... \n",
|
||
"788 in intersect combined tcr p i barco... \n",
|
||
"\n",
|
||
" tokens \n",
|
||
"0 [generate, pairs, bam, files, pairtools, split... \n",
|
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|
||
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|
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|
||
"4 [dy, import, python, package, working, cooler,... \n",
|
||
".. ... \n",
|
||
"784 [compares, average, expected, heterozygosity, ... \n",
|
||
"785 [pca, genotype, data, means, clusters, cluster] \n",
|
||
"786 [ancestry, proportion, ancestry, proportions, ... \n",
|
||
"787 [visualization, higlass, hiccups, juicer, tool... \n",
|
||
"788 [intersect, combined, tcr, barcode, data, leng... \n",
|
||
"\n",
|
||
"[789 rows x 3 columns]"
|
||
]
|
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|
||
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|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"# tokenize ## Why not use spacy here?\n",
|
||
"import gensim\n",
|
||
"import nltk\n",
|
||
"from nltk.corpus import stopwords\n",
|
||
"\n",
|
||
"# Download NLTK stopwords\n",
|
||
"nltk.download('stopwords')\n",
|
||
"stop_words = stopwords.words('english')\n",
|
||
"\n",
|
||
"# Tokenize and remove stopwords\n",
|
||
"def tokenize(text):\n",
|
||
" tokens = gensim.utils.simple_preprocess(text, deacc=True)\n",
|
||
" tokens = [token for token in tokens if token not in stop_words]\n",
|
||
" return tokens\n",
|
||
"\n",
|
||
"df['tokens'] = df['cleaned_cols'].apply(tokenize)\n",
|
||
"df"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 10,
|
||
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|
||
"outputs": [
|
||
{
|
||
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|
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|
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|
||
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|
||
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|
||
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|
||
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|
||
" <th>cleaned_cols</th>\n",
|
||
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|
||
" <th>lemmas</th>\n",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
" <th>0</th>\n",
|
||
" <td>Generate .pairs and bam files\\n\\nThe pairtools...</td>\n",
|
||
" <td>generate pairs and bam files the pairtools sp...</td>\n",
|
||
" <td>[generate, pairs, bam, files, pairtools, split...</td>\n",
|
||
" <td>[generate, pair, bam, file, pairtool, split, c...</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>1</th>\n",
|
||
" <td>.\\n\\n@ Vivaldi File Edit View Bookmarks Mail T...</td>\n",
|
||
" <td>vivaldi file edit view bookmarks mail tools ...</td>\n",
|
||
" <td>[vivaldi, file, edit, view, bookmarks, mail, t...</td>\n",
|
||
" <td>[vivaldi, file, edit, view, bookmark, mail, to...</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>2</th>\n",
|
||
" <td>4. scRepertoire on patient 4\\n\\nIn [51]: libra...</td>\n",
|
||
" <td>screpertoire on patient in library ...</td>\n",
|
||
" <td>[screpertoire, patient, library, screpertoire,...</td>\n",
|
||
" <td>[screpertoire, patient, library, screpertoire,...</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>3</th>\n",
|
||
" <td>jf====\\n\\n#\\n#\\n#\\n#\\n#\\n#\\n#\\n#\\n#\\n#\\ni\\n#\\n...</td>\n",
|
||
" <td>jf i a aligned seq...</td>\n",
|
||
" <td>[jf, aligned, sequences, zm, eb, rp, zm, eb, r...</td>\n",
|
||
" <td>[jf, align, sequence, zm, eb, rp, zm, eb, rpht...</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>4</th>\n",
|
||
" <td>Dy\\n\\n# Import python package for working with...</td>\n",
|
||
" <td>dy import python package for working with co...</td>\n",
|
||
" <td>[dy, import, python, package, working, cooler,...</td>\n",
|
||
" <td>[dy, import, python, package, work, cool, file...</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>...</th>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>784</th>\n",
|
||
" <td>= F, compares the average expected heterozygos...</td>\n",
|
||
" <td>f compares the average expected heterozygos...</td>\n",
|
||
" <td>[compares, average, expected, heterozygosity, ...</td>\n",
|
||
" <td>[compare, average, expect, heterozygosity, ind...</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>785</th>\n",
|
||
" <td>100\\n\\n75\\n\\n25\\n\\nPCA of Genotype Data (K-Mea...</td>\n",
|
||
" <td>pca of genotype data k means cluste...</td>\n",
|
||
" <td>[pca, genotype, data, means, clusters, cluster]</td>\n",
|
||
" <td>[pca, genotype, datum, mean, cluster, cluster]</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>786</th>\n",
|
||
" <td>Ancestry Proportion\\n\\n08\\n\\n0.6\\n\\n0.4\\n\\n0.2...</td>\n",
|
||
" <td>ancestry proportion ancestr...</td>\n",
|
||
" <td>[ancestry, proportion, ancestry, proportions, ...</td>\n",
|
||
" <td>[ancestry, proportion, ancestry, proportion, p...</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>787</th>\n",
|
||
" <td>Visualization: HiGlass\\n\\nHICCUPS juicer_tools...</td>\n",
|
||
" <td>visualization higlass hiccups juicer tools b...</td>\n",
|
||
" <td>[visualization, higlass, hiccups, juicer, tool...</td>\n",
|
||
" <td>[visualization, higlass, hiccup, juicer, tools...</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>788</th>\n",
|
||
" <td>In [892]: intersect(combined.TCR_p3[[i]]$barco...</td>\n",
|
||
" <td>in intersect combined tcr p i barco...</td>\n",
|
||
" <td>[intersect, combined, tcr, barcode, data, leng...</td>\n",
|
||
" <td>[intersect, combine, tcr, barcode, data, lengt...</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"<p>789 rows × 4 columns</p>\n",
|
||
"</div>"
|
||
],
|
||
"text/plain": [
|
||
" cols \\\n",
|
||
"0 Generate .pairs and bam files\\n\\nThe pairtools... \n",
|
||
"1 .\\n\\n@ Vivaldi File Edit View Bookmarks Mail T... \n",
|
||
"2 4. scRepertoire on patient 4\\n\\nIn [51]: libra... \n",
|
||
"3 jf====\\n\\n#\\n#\\n#\\n#\\n#\\n#\\n#\\n#\\n#\\n#\\ni\\n#\\n... \n",
|
||
"4 Dy\\n\\n# Import python package for working with... \n",
|
||
".. ... \n",
|
||
"784 = F, compares the average expected heterozygos... \n",
|
||
"785 100\\n\\n75\\n\\n25\\n\\nPCA of Genotype Data (K-Mea... \n",
|
||
"786 Ancestry Proportion\\n\\n08\\n\\n0.6\\n\\n0.4\\n\\n0.2... \n",
|
||
"787 Visualization: HiGlass\\n\\nHICCUPS juicer_tools... \n",
|
||
"788 In [892]: intersect(combined.TCR_p3[[i]]$barco... \n",
|
||
"\n",
|
||
" cleaned_cols \\\n",
|
||
"0 generate pairs and bam files the pairtools sp... \n",
|
||
"1 vivaldi file edit view bookmarks mail tools ... \n",
|
||
"2 screpertoire on patient in library ... \n",
|
||
"3 jf i a aligned seq... \n",
|
||
"4 dy import python package for working with co... \n",
|
||
".. ... \n",
|
||
"784 f compares the average expected heterozygos... \n",
|
||
"785 pca of genotype data k means cluste... \n",
|
||
"786 ancestry proportion ancestr... \n",
|
||
"787 visualization higlass hiccups juicer tools b... \n",
|
||
"788 in intersect combined tcr p i barco... \n",
|
||
"\n",
|
||
" tokens \\\n",
|
||
"0 [generate, pairs, bam, files, pairtools, split... \n",
|
||
"1 [vivaldi, file, edit, view, bookmarks, mail, t... \n",
|
||
"2 [screpertoire, patient, library, screpertoire,... \n",
|
||
"3 [jf, aligned, sequences, zm, eb, rp, zm, eb, r... \n",
|
||
"4 [dy, import, python, package, working, cooler,... \n",
|
||
".. ... \n",
|
||
"784 [compares, average, expected, heterozygosity, ... \n",
|
||
"785 [pca, genotype, data, means, clusters, cluster] \n",
|
||
"786 [ancestry, proportion, ancestry, proportions, ... \n",
|
||
"787 [visualization, higlass, hiccups, juicer, tool... \n",
|
||
"788 [intersect, combined, tcr, barcode, data, leng... \n",
|
||
"\n",
|
||
" lemmas \n",
|
||
"0 [generate, pair, bam, file, pairtool, split, c... \n",
|
||
"1 [vivaldi, file, edit, view, bookmark, mail, to... \n",
|
||
"2 [screpertoire, patient, library, screpertoire,... \n",
|
||
"3 [jf, align, sequence, zm, eb, rp, zm, eb, rpht... \n",
|
||
"4 [dy, import, python, package, work, cool, file... \n",
|
||
".. ... \n",
|
||
"784 [compare, average, expect, heterozygosity, ind... \n",
|
||
"785 [pca, genotype, datum, mean, cluster, cluster] \n",
|
||
"786 [ancestry, proportion, ancestry, proportion, p... \n",
|
||
"787 [visualization, higlass, hiccup, juicer, tools... \n",
|
||
"788 [intersect, combine, tcr, barcode, data, lengt... \n",
|
||
"\n",
|
||
"[789 rows x 4 columns]"
|
||
]
|
||
},
|
||
"execution_count": 10,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"# lemmatize # what's the diff.?\n",
|
||
"\n",
|
||
"import spacy\n",
|
||
"\n",
|
||
"# Load spaCy model\n",
|
||
"nlp = spacy.load('en_core_web_sm', disable=['parser', 'ner'])\n",
|
||
"\n",
|
||
"def lemmatize(tokens):\n",
|
||
" doc = nlp(\" \".join(tokens))\n",
|
||
" return [token.lemma_ for token in doc]\n",
|
||
"\n",
|
||
"df['lemmas'] = df['tokens'].apply(lemmatize)\n",
|
||
"df"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 11,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"# create dict. and corpus from l.tokens\n",
|
||
"\n",
|
||
"import gensim.corpora as corpora\n",
|
||
"\n",
|
||
"# Create dictionary and corpus\n",
|
||
"id2word = corpora.Dictionary(df['lemmas'])\n",
|
||
"texts = df['lemmas']\n",
|
||
"corpus = [id2word.doc2bow(text) for text in texts]\n",
|
||
"# corpus"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 12,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"# Build LDA model\n",
|
||
"lda_model = gensim.models.ldamodel.LdaModel(corpus=corpus,\n",
|
||
" id2word=id2word,\n",
|
||
" num_topics=5, \n",
|
||
" random_state=100,\n",
|
||
" update_every=1,\n",
|
||
" chunksize=100,\n",
|
||
" passes=10,\n",
|
||
" alpha='auto',\n",
|
||
" per_word_topics=True)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 13,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"(0, '0.017*\"datum\" + 0.012*\"file\" + 0.010*\"gene\" + 0.009*\"read\" + 0.009*\"kb\" + 0.009*\"error\" + 0.008*\"df\" + 0.008*\"pair\" + 0.008*\"level\" + 0.008*\"aman\"')\n",
|
||
"(1, '0.038*\"chr\" + 0.021*\"gene\" + 0.020*\"bed\" + 0.018*\"cd\" + 0.014*\"prodigal\" + 0.013*\"loop\" + 0.012*\"anchor\" + 0.010*\"protein\" + 0.009*\"d\" + 0.009*\"aa\"')\n",
|
||
"(2, '0.012*\"plt\" + 0.011*\"rw\" + 0.011*\"import\" + 0.010*\"count\" + 0.009*\"feature\" + 0.009*\"pst\" + 0.009*\"sample\" + 0.008*\"nan\" + 0.007*\"matplotlib\" + 0.007*\"plot\"')\n",
|
||
"(3, '0.018*\"sequence\" + 0.013*\"raw\" + 0.009*\"sample\" + 0.009*\"base\" + 0.009*\"sh\" + 0.008*\"content\" + 0.008*\"gz\" + 0.008*\"level\" + 0.007*\"mean\" + 0.007*\"methylation\"')\n",
|
||
"(4, '0.008*\"contact\" + 0.006*\"protein\" + 0.006*\"ikaohofi\" + 0.006*\"go\" + 0.005*\"genome\" + 0.005*\"cd\" + 0.004*\"al\" + 0.004*\"analysis\" + 0.004*\"gene\" + 0.003*\"time\"')\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"# Print the topics\n",
|
||
"topics = lda_model.print_topics(num_words=10)\n",
|
||
"for topic in topics:\n",
|
||
" print(topic)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 14,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"\n",
|
||
"Coherence Score: 0.37045797849380857\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"from gensim.models import CoherenceModel\n",
|
||
"\n",
|
||
"# Compute coherence score\n",
|
||
"coherence_model_lda = CoherenceModel(model=lda_model, texts=df['lemmas'], dictionary=id2word, coherence='c_v')\n",
|
||
"coherence_lda = coherence_model_lda.get_coherence()\n",
|
||
"print('\\nCoherence Score: ', coherence_lda)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 15,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stderr",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"/Users/aman/lib/python3.13/site-packages/sklearn/manifold/_mds.py:677: FutureWarning: The default value of `n_init` will change from 4 to 1 in 1.9.\n",
|
||
" warnings.warn(\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
"text/html": [
|
||
"\n",
|
||
"<link rel=\"stylesheet\" type=\"text/css\" href=\"https://cdn.jsdelivr.net/gh/bmabey/pyLDAvis@3.4.0/pyLDAvis/js/ldavis.v1.0.0.css\">\n",
|
||
"\n",
|
||
"\n",
|
||
"<div id=\"ldavis_el3811256570595367867532825\" style=\"background-color:white;\"></div>\n",
|
||
"<script type=\"text/javascript\">\n",
|
||
"\n",
|
||
"var ldavis_el3811256570595367867532825_data = {\"mdsDat\": {\"x\": [0.1764540892259312, -0.043693691162114315, -0.13679487462575315, -0.2589009916525375, 0.2629354682144737], \"y\": [-0.19739835393308358, 0.2626514030194425, -0.2266200427422442, 0.04453556929193725, 0.116831424363948], \"topics\": [1, 2, 3, 4, 5], \"cluster\": [1, 1, 1, 1, 1], \"Freq\": [27.084471173618894, 26.423012435045766, 18.25637930724759, 14.967899172434079, 13.268237911653669]}, \"tinfo\": {\"Term\": [\"chr\", \"bed\", \"gene\", \"cd\", \"raw\", \"sequence\", \"prodigal\", \"datum\", \"plt\", \"rw\", \"protein\", \"loop\", \"anchor\", \"import\", \"df\", \"combine\", \"content\", \"feature\", \"level\", \"kb\", \"ikaohofi\", \"research\", \"thesis\", \"phd\", \"university\", \"application\", \"year\", \"information\", \"science\", \"ago\", \"degree\", \"also\", \"case\", \"network\", \"prc\", \"go\", \"learn\", \"leaf\", \"human\", \"et\", \"bind\", \"vv\", \"project\", \"genome\", \"contact\", \"al\", \"chromatin\", \"search\", \"time\", \"protein\", \"analysis\", \"help\", \"cd\", \"pdf\", \"use\", \"gene\", \"datum\", \"cell\", \"combine\", \"filter\", \"mb\", \"srr\", \"cnd\", \"fastq\", \"alignment\", \"jun\", \"str\", \"grep\", \"patient\", \"mercator\", \"stop\", \"java\", \"gld\", \"null\", \"hiseq\", \"eval\", \"horvu\", \"morex\", \"na\", \"df\", \"output\", \"frame\", \"map\", \"csv\", \"kb\", \"error\", \"read\", \"datum\", \"juicer\", \"tcr\", \"seurat\", \"function\", \"file\", \"pair\", \"result\", \"aman\", \"gene\", \"level\", \"run\", \"object\", \"false\", \"content\", \"methylation\", \"fastqc\", \"gc\", \"pro\", \"could\", \"oz\", \"wood\", \"cry\", \"miniconda\", \"normalize\", \"opt\", \"local\", \"awk\", \"serverapp\", \"install\", \"zip\", \"ctg\", \"rule\", \"context\", \"raw\", \"quality\", \"build\", \"sh\", \"mean\", \"sequence\", \"gz\", \"base\", \"per\", \"signal\", \"hic\", \"sample\", \"level\", \"python\", \"txt\", \"run\", \"plt\", \"rw\", \"pst\", 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\"sequence\", \"serverapp\", \"seurat\", \"seurat\", \"sh\", \"sh\", \"sh\", \"shape\", \"shape\", \"signal\", \"signal\", \"site\", \"site\", \"site\", \"srr\", \"stop\", \"str\", \"tcr\", \"tcr\", \"thesis\", \"tige\", \"time\", \"time\", \"time\", \"trac\", \"traj\", \"trav\", \"trbc\", \"trbj\", \"trbv\", \"txt\", \"txt\", \"txt\", \"undetermined\", \"university\", \"use\", \"use\", \"use\", \"use\", \"value\", \"value\", \"value\", \"value\", \"variance\", \"vv\", \"vv\", \"wood\", \"year\", \"zip\"]}, \"R\": 20, \"lambda.step\": 0.01, \"plot.opts\": {\"xlab\": \"PC1\", \"ylab\": \"PC2\"}, \"topic.order\": [5, 1, 4, 3, 2]};\n",
|
||
"\n",
|
||
"function LDAvis_load_lib(url, callback){\n",
|
||
" var s = document.createElement('script');\n",
|
||
" s.src = url;\n",
|
||
" s.async = true;\n",
|
||
" s.onreadystatechange = s.onload = callback;\n",
|
||
" s.onerror = function(){console.warn(\"failed to load library \" + url);};\n",
|
||
" document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
|
||
"}\n",
|
||
"\n",
|
||
"if(typeof(LDAvis) !== \"undefined\"){\n",
|
||
" // already loaded: just create the visualization\n",
|
||
" !function(LDAvis){\n",
|
||
" new LDAvis(\"#\" + \"ldavis_el3811256570595367867532825\", ldavis_el3811256570595367867532825_data);\n",
|
||
" }(LDAvis);\n",
|
||
"}else if(typeof define === \"function\" && define.amd){\n",
|
||
" // require.js is available: use it to load d3/LDAvis\n",
|
||
" require.config({paths: {d3: \"https://d3js.org/d3.v5\"}});\n",
|
||
" require([\"d3\"], function(d3){\n",
|
||
" window.d3 = d3;\n",
|
||
" LDAvis_load_lib(\"https://cdn.jsdelivr.net/gh/bmabey/pyLDAvis@3.4.0/pyLDAvis/js/ldavis.v3.0.0.js\", function(){\n",
|
||
" new LDAvis(\"#\" + \"ldavis_el3811256570595367867532825\", ldavis_el3811256570595367867532825_data);\n",
|
||
" });\n",
|
||
" });\n",
|
||
"}else{\n",
|
||
" // require.js not available: dynamically load d3 & LDAvis\n",
|
||
" LDAvis_load_lib(\"https://d3js.org/d3.v5.js\", function(){\n",
|
||
" LDAvis_load_lib(\"https://cdn.jsdelivr.net/gh/bmabey/pyLDAvis@3.4.0/pyLDAvis/js/ldavis.v3.0.0.js\", function(){\n",
|
||
" new LDAvis(\"#\" + \"ldavis_el3811256570595367867532825\", ldavis_el3811256570595367867532825_data);\n",
|
||
" })\n",
|
||
" });\n",
|
||
"}\n",
|
||
"</script>"
|
||
],
|
||
"text/plain": [
|
||
"PreparedData(topic_coordinates= x y topics cluster Freq\n",
|
||
"topic \n",
|
||
"4 0.176454 -0.197398 1 1 27.084471\n",
|
||
"0 -0.043694 0.262651 2 1 26.423012\n",
|
||
"3 -0.136795 -0.226620 3 1 18.256379\n",
|
||
"2 -0.258901 0.044536 4 1 14.967899\n",
|
||
"1 0.262935 0.116831 5 1 13.268238, topic_info= Term Freq Total Category logprob loglift\n",
|
||
"545 chr 393.000000 393.000000 Default 20.0000 20.0000\n",
|
||
"1610 bed 187.000000 187.000000 Default 19.0000 19.0000\n",
|
||
"502 gene 458.000000 458.000000 Default 18.0000 18.0000\n",
|
||
"1113 cd 298.000000 298.000000 Default 17.0000 17.0000\n",
|
||
"1156 raw 178.000000 178.000000 Default 16.0000 16.0000\n",
|
||
"... ... ... ... ... ... ...\n",
|
||
"502 gene 196.710432 458.544810 Topic5 -3.8416 1.1735\n",
|
||
"128 protein 89.488563 209.567704 Topic5 -4.6292 1.1689\n",
|
||
"173 contig 70.718433 141.755787 Topic5 -4.8646 1.3244\n",
|
||
"33 name 74.072136 211.699232 Topic5 -4.8183 0.9697\n",
|
||
"31 matrix 61.297732 168.611984 Topic5 -5.0076 1.0079\n",
|
||
"\n",
|
||
"[208 rows x 6 columns], token_table= Topic Freq Term\n",
|
||
"term \n",
|
||
"1086 4 0.979491 I\n",
|
||
"294 1 0.028762 aa\n",
|
||
"294 4 0.162985 aa\n",
|
||
"294 5 0.805339 aa\n",
|
||
"1608 3 0.074783 ab\n",
|
||
"... ... ... ...\n",
|
||
"585 1 0.953134 vv\n",
|
||
"585 4 0.020279 vv\n",
|
||
"802 3 0.977820 wood\n",
|
||
"2672 1 0.987045 year\n",
|
||
"1557 3 0.973348 zip\n",
|
||
"\n",
|
||
"[322 rows x 3 columns], R=20, lambda_step=0.01, plot_opts={'xlab': 'PC1', 'ylab': 'PC2'}, topic_order=[5, 1, 4, 3, 2])"
|
||
]
|
||
},
|
||
"execution_count": 15,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"import pyLDAvis\n",
|
||
"import pyLDAvis.gensim_models as gensimvis\n",
|
||
"\n",
|
||
"pyLDAvis.enable_notebook()\n",
|
||
"\n",
|
||
"vis = gensimvis.prepare(\n",
|
||
" lda_model, \n",
|
||
" corpus, \n",
|
||
" id2word,\n",
|
||
" mds='mmds', # or 'pcoa'\n",
|
||
" R=20 # top terms per topic\n",
|
||
")\n",
|
||
"\n",
|
||
"vis"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"---"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 29,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"(5, 1)"
|
||
]
|
||
},
|
||
"execution_count": 29,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"import pandas as pd\n",
|
||
"pd.DataFrame(text_scr).shape"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 23,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"import spacy\n",
|
||
"nlp = spacy.load(\"en_core_web_sm\")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 44,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"['Generate .pairs and bam files',\n",
|
||
" 'The pairtools split command is used to split the final .pairsam into two files: .sam (or .bam )\\nand .pairs ( .pairsam has two extra columns containing the alignments from which the Micro-C\\npair was extracted, these two columns are not included in .pairs files)',\n",
|
||
" 'pairtools split options:',\n",
|
||
" 'Output pairs file. If the path ends with .gz or .Iz4 the output is pbgzip-/lz4c-\\n-output-pairs compressed. If you wish to pipe the command and output the pairs fils to\\nstdout use - instead of file name',\n",
|
||
" 'Output sam file. If the file name extension is .bam, the output will be written\\nin bam format. If you wish to pipe the command, use - instead of a file name.',\n",
|
||
" '-output-sam please note that in this case the sam format will be used (and can be later\\nconverted to bam file e.g. with the command samtools view -bS -@16 -o\\ntemp.bam',\n",
|
||
" 'pairtools split —-nproc-in <cores> —-nproc-out <cores> —-output-pairs <mapped.pairs> \\\\\\n--output-sam <unsorted.bam> <dedup.pairsam>',\n",
|
||
" 'pairtools split --nproc-in 8 —-nproc-out 8 --output-pairs mapped.pairs -—-output-sam unsorted. bz',\n",
|
||
" 'The .pairs file can be used for generating contact matrix',\n",
|
||
" '@ Vivaldi File Edit View Bookmarks Mail Tools Window’ Help CS @ s} -¥ 3} © €& @)) Q & SunNov 10 18:04\\n© taltech moodle @ alternative to igv browse fi pannzer2 [ Pannzer2 fa ekhidna2.biocenter.helsi li ekhidna2.biocenter.helsi li ekhidna2.biocenter.helsi',\n",
|
||
" '(a) —_ > YD VY @NotSecure ekhidna2.biocenter.helsinki.fi/barcosel/tmp//S2Z0dlJVpH4/index.html |_a > wo Se ly © F @ @',\n",
|
||
" 'Y Speed Dial ¥Y Imported From... Y Imported From... Online Bewerbung QGIS API Docume... qgis - Trying to pe... New Script - Earth... Pastebin.com - #1... TargetP 2.0- DTU... https://www.mood... OnePlus 12R revie... Whois “Indian”in.. v A »',\n",
|
||
" 'Title: ecoli_nano\\nProteins: 5204\\nDatabase: uniprot.Oct2024 consisting of 88511531046 letters and 248838886 sequences\\nURL: _http://ekhidna2.biocenter.helsinki .fi/barcosel/tmp//S2Z0d1J V pH4\\nChecksum: 3c404bc04b8e94f66e48ce69ea3b988bd4b7b699bbededd9fc52bSaf\\nSubmitted: Sun Nov 10 19:07:35 EET 2024\\nStarted: Sun Nov 10 19:07:55 EET 2024\\nProcessed: 5204\\nFinished: Sun Nov 10 19:19:54 EET 2024\\nE-mail: _helsinki.o34if@passinbox.com',\n",
|
||
" '¢ HTML summary:\\n© Queries 1 to 1000\\nQueries 1001 to 2000\\nQueries 2001 to 3000\\nQueries 3001 to 4000\\nQueries 4001 to 5000\\nQueries 5001 to 5204\\n¢ Download:\\no Annotations (parseable)\\no DE prediction details\\no GO prediction details\\n¢ Logs:\\no Uploaded sequences\\no STDOUT\\no STDERR',\n",
|
||
" '4. scRepertoire on patient 4',\n",
|
||
" 'In [51]: library(scRepertoire)\\n#51 <- read.delim(\"/home/rstudio/run@71-nsclc-4_VDJ_Dominant_Contigs_AIRR.tsv\", sep = \"\\\\t\", header = TRUE, stringsAs',\n",
|
||
" '#contig_list <- list(S1)\\n#contig.list <- loadContigs(input = S1,\\n# format = \"AIRR\")',\n",
|
||
" 'In [60]: contig_list <- loadContigs(\\ninput = \"/home/rstudio/runQ@71\",\\nformat = \"BD\"\\n)',\n",
|
||
" 'In [61]: combined.TCR <— combineTCR(contig_list,\\nremoveNA = FALSE,\\nremoveMulti FALSE,\\nfilterMulti = FALSE)',\n",
|
||
" '# output = a list of contig data frames that will be reduced to the reads associated with a single cell barcode. It |\\nhead(combined.TCR[[1]])',\n",
|
||
" 'Error: Expecting a string vector: [type=integer; required=STRSXP].\\nTraceback:',\n",
|
||
" '1. .constructConDfAndParseTCR(data2)',\n",
|
||
" '2. rcppConstructConDfAndParseTCR(data2 %>% dplyr::arrange(., chain,\\n. cdr3_nt), unique(data2[[1]]))',\n",
|
||
" '3. stop(structure(list(message = \"Expecting a string vector: [type=integer; required=STRSXP].\",\\n. call = eval(expr, envir), cppstack = NULL), class = c(\"Rcpp::not_compatible\",\\n. \"C++Error\", “error”, \"condition\") ))',\n",
|
||
" '#\\n#\\n#\\n#\\n#\\n#\\n#\\n#\\n#\\n#\\ni\\n#\\n#',\n",
|
||
" '1: Zm00001eb360630_RP',\n",
|
||
" '2: Zm00001eb360630_RPHt4\\nMatrix: EBLOSUM62\\nGap_penalty: 10.0\\nExtend_penalty: 0.5',\n",
|
||
" 'Identity: 460/460 (100.0%)\\nSimilarity: 460/460 (100.0%)\\nGaps: 0/460 ( 0.0%)',\n",
|
||
" '# Import python package for working with cooler files and tools for analysis\\nimport cooler\\nimport cooltools.Lib.plotting',\n",
|
||
" 'ImportError Traceback (most recent call last)',\n",
|
||
" 'File ~/anaconda3/envs/cool_notebook/lib/python3.10/site-packages/cooltools/lib/plotting. py:6\\n5 try:',\n",
|
||
" '----> 6 from matplotlib.cm import register_cmap',\n",
|
||
" '7 except ImportError:',\n",
|
||
" \"ImportError: cannot import name 'register_cmap' from 'matplotlib.cm' (/usr/users/papantonis1/anaconda3/envs/cool_notebook/lib/python3.10/site-packages/matp lot lib/cm. py)\\nDuring handling of the above exception, another exception occurred:\",\n",
|
||
" 'ModuleNotFoundError Traceback (most recent call last)\\nCell In{5], line 3\\n1 # Import python package for working with cooler files and tools for analysis\\nimport cooler\\nimport cooltools. lib. plotting',\n",
|
||
" 'File ~/anaconda3/envs/cool_notebook/lib/python3.10/site-packages/cooltools/lib/plotting. py:8\\nfrom matplotlib.cm import register_cmap',\n",
|
||
" '—- from matplotlib.colormaps import register\\n10 import matplotlib as mpl\\n11 import matplotlib.pyplot as plt',\n",
|
||
" \"ModuleNotFoundError: No module named 'matplotlib.colormaps'\"]"
|
||
]
|
||
},
|
||
"execution_count": 44,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"# 1) Segment into blocks\n",
|
||
"blocks = []\n",
|
||
"for text in text_scr:\n",
|
||
" for block in text.split(\"\\n\\n\"): # split on blank lines\n",
|
||
" b = block.strip()\n",
|
||
" if len(b) > 20: # keep only meaningful blocks\n",
|
||
" blocks.append(b)\n",
|
||
"\n",
|
||
"blocks"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 45,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"[{'text': 'Generate .pairs and bam files',\n",
|
||
" 'entities': [('Generate .pairs', 'PERSON')]},\n",
|
||
" {'text': 'The pairtools split command is used to split the final .pairsam into two files: .sam (or .bam )\\nand .pairs ( .pairsam has two extra columns containing the alignments from which the Micro-C\\npair was extracted, these two columns are not included in .pairs files)',\n",
|
||
" 'entities': [('two', 'CARDINAL'),\n",
|
||
" ('.pairs', 'PERSON'),\n",
|
||
" ('two', 'CARDINAL'),\n",
|
||
" ('two', 'CARDINAL')]},\n",
|
||
" {'text': 'pairtools split options:', 'entities': []},\n",
|
||
" {'text': 'Output pairs file. If the path ends with .gz or .Iz4 the output is pbgzip-/lz4c-\\n-output-pairs compressed. If you wish to pipe the command and output the pairs fils to\\nstdout use - instead of file name',\n",
|
||
" 'entities': [('pbgzip-/lz4c-', 'ORG')]},\n",
|
||
" {'text': 'Output sam file. If the file name extension is .bam, the output will be written\\nin bam format. If you wish to pipe the command, use - instead of a file name.',\n",
|
||
" 'entities': []},\n",
|
||
" {'text': '-output-sam please note that in this case the sam format will be used (and can be later\\nconverted to bam file e.g. with the command samtools view -bS -@16 -o\\ntemp.bam',\n",
|
||
" 'entities': []},\n",
|
||
" {'text': 'pairtools split —-nproc-in <cores> —-nproc-out <cores> —-output-pairs <mapped.pairs> \\\\\\n--output-sam <unsorted.bam> <dedup.pairsam>',\n",
|
||
" 'entities': []},\n",
|
||
" {'text': 'pairtools split --nproc-in 8 —-nproc-out 8 --output-pairs mapped.pairs -—-output-sam unsorted. bz',\n",
|
||
" 'entities': [('8', 'CARDINAL'), ('8', 'CARDINAL')]},\n",
|
||
" {'text': 'The .pairs file can be used for generating contact matrix',\n",
|
||
" 'entities': []},\n",
|
||
" {'text': '@ Vivaldi File Edit View Bookmarks Mail Tools Window’ Help CS @ s} -¥ 3} © €& @)) Q & SunNov 10 18:04\\n© taltech moodle @ alternative to igv browse fi pannzer2 [ Pannzer2 fa ekhidna2.biocenter.helsi li ekhidna2.biocenter.helsi li ekhidna2.biocenter.helsi',\n",
|
||
" 'entities': [('3} © €& @', 'MONEY'),\n",
|
||
" ('10 18:04', 'TIME'),\n",
|
||
" ('li', 'PERSON'),\n",
|
||
" ('li', 'PERSON')]},\n",
|
||
" {'text': '(a) —_ > YD VY @NotSecure ekhidna2.biocenter.helsinki.fi/barcosel/tmp//S2Z0dlJVpH4/index.html |_a > wo Se ly © F @ @',\n",
|
||
" 'entities': []},\n",
|
||
" {'text': 'Y Speed Dial ¥Y Imported From... Y Imported From... Online Bewerbung QGIS API Docume... qgis - Trying to pe... New Script - Earth... Pastebin.com - #1... TargetP 2.0- DTU... https://www.mood... OnePlus 12R revie... Whois “Indian”in.. v A »',\n",
|
||
" 'entities': [('API Docume', 'PERSON'),\n",
|
||
" ('New Script - Earth', 'GPE'),\n",
|
||
" ('Pastebin.com', 'ORG'),\n",
|
||
" ('2.0-', 'CARDINAL'),\n",
|
||
" ('DTU', 'ORG'),\n",
|
||
" ('OnePlus 12R', 'PERSON')]},\n",
|
||
" {'text': 'Title: ecoli_nano\\nProteins: 5204\\nDatabase: uniprot.Oct2024 consisting of 88511531046 letters and 248838886 sequences\\nURL: _http://ekhidna2.biocenter.helsinki .fi/barcosel/tmp//S2Z0d1J V pH4\\nChecksum: 3c404bc04b8e94f66e48ce69ea3b988bd4b7b699bbededd9fc52bSaf\\nSubmitted: Sun Nov 10 19:07:35 EET 2024\\nStarted: Sun Nov 10 19:07:55 EET 2024\\nProcessed: 5204\\nFinished: Sun Nov 10 19:19:54 EET 2024\\nE-mail: _helsinki.o34if@passinbox.com',\n",
|
||
" 'entities': [('5204', 'CARDINAL'),\n",
|
||
" ('88511531046', 'DATE'),\n",
|
||
" ('248838886', 'DATE'),\n",
|
||
" ('http://ekhidna2.biocenter.helsinki .fi/barcosel', 'ORG'),\n",
|
||
" ('2024', 'DATE'),\n",
|
||
" ('Nov 10', 'DATE'),\n",
|
||
" ('2024', 'DATE'),\n",
|
||
" ('5204', 'CARDINAL'),\n",
|
||
" ('Nov 10', 'DATE'),\n",
|
||
" ('2024', 'DATE')]},\n",
|
||
" {'text': '¢ HTML summary:\\n© Queries 1 to 1000\\nQueries 1001 to 2000\\nQueries 2001 to 3000\\nQueries 3001 to 4000\\nQueries 4001 to 5000\\nQueries 5001 to 5204\\n¢ Download:\\no Annotations (parseable)\\no DE prediction details\\no GO prediction details\\n¢ Logs:\\no Uploaded sequences\\no STDOUT\\no STDERR',\n",
|
||
" 'entities': [('¢ HTML', 'PERSON'),\n",
|
||
" ('1001', 'DATE'),\n",
|
||
" ('2000', 'DATE'),\n",
|
||
" ('2001', 'DATE'),\n",
|
||
" ('3000', 'CARDINAL'),\n",
|
||
" ('3001', 'DATE'),\n",
|
||
" ('4000', 'CARDINAL'),\n",
|
||
" ('5000', 'CARDINAL'),\n",
|
||
" ('5001', 'DATE'),\n",
|
||
" ('5204', 'CARDINAL'),\n",
|
||
" ('Download', 'PERSON'),\n",
|
||
" ('STDERR', 'ORG')]},\n",
|
||
" {'text': '4. scRepertoire on patient 4',\n",
|
||
" 'entities': [('4', 'CARDINAL'), ('4', 'CARDINAL')]},\n",
|
||
" {'text': 'In [51]: library(scRepertoire)\\n#51 <- read.delim(\"/home/rstudio/run@71-nsclc-4_VDJ_Dominant_Contigs_AIRR.tsv\", sep = \"\\\\t\", header = TRUE, stringsAs',\n",
|
||
" 'entities': [('51', 'CARDINAL'), ('51', 'MONEY')]},\n",
|
||
" {'text': '#contig_list <- list(S1)\\n#contig.list <- loadContigs(input = S1,\\n# format = \"AIRR\")',\n",
|
||
" 'entities': [('#', 'CARDINAL'), ('AIRR', 'ORG')]},\n",
|
||
" {'text': 'In [60]: contig_list <- loadContigs(\\ninput = \"/home/rstudio/runQ@71\",\\nformat = \"BD\"\\n)',\n",
|
||
" 'entities': [('60', 'CARDINAL')]},\n",
|
||
" {'text': 'In [61]: combined.TCR <— combineTCR(contig_list,\\nremoveNA = FALSE,\\nremoveMulti FALSE,\\nfilterMulti = FALSE)',\n",
|
||
" 'entities': [('61', 'CARDINAL'), ('TCR', 'ORG')]},\n",
|
||
" {'text': '# output = a list of contig data frames that will be reduced to the reads associated with a single cell barcode. It |\\nhead(combined.TCR[[1]])',\n",
|
||
" 'entities': []},\n",
|
||
" {'text': 'Error: Expecting a string vector: [type=integer; required=STRSXP].\\nTraceback:',\n",
|
||
" 'entities': []},\n",
|
||
" {'text': '1. .constructConDfAndParseTCR(data2)',\n",
|
||
" 'entities': [('1', 'CARDINAL')]},\n",
|
||
" {'text': '2. rcppConstructConDfAndParseTCR(data2 %>% dplyr::arrange(., chain,\\n. cdr3_nt), unique(data2[[1]]))',\n",
|
||
" 'entities': [('2', 'CARDINAL')]},\n",
|
||
" {'text': '3. stop(structure(list(message = \"Expecting a string vector: [type=integer; required=STRSXP].\",\\n. call = eval(expr, envir), cppstack = NULL), class = c(\"Rcpp::not_compatible\",\\n. \"C++Error\", “error”, \"condition\") ))',\n",
|
||
" 'entities': [('3', 'CARDINAL'), ('envir', 'PERSON'), ('NULL', 'ORG')]},\n",
|
||
" {'text': '#\\n#\\n#\\n#\\n#\\n#\\n#\\n#\\n#\\n#\\ni\\n#\\n#',\n",
|
||
" 'entities': [('#\\n#', 'MONEY'), ('#\\n#', 'MONEY')]},\n",
|
||
" {'text': '1: Zm00001eb360630_RP', 'entities': [('1', 'CARDINAL')]},\n",
|
||
" {'text': '2: Zm00001eb360630_RPHt4\\nMatrix: EBLOSUM62\\nGap_penalty: 10.0\\nExtend_penalty: 0.5',\n",
|
||
" 'entities': [('2', 'CARDINAL'),\n",
|
||
" ('Zm00001eb360630_RPHt4', 'CARDINAL'),\n",
|
||
" ('EBLOSUM62\\nGap_penalty', 'PERSON'),\n",
|
||
" ('10.0', 'CARDINAL'),\n",
|
||
" ('0.5', 'CARDINAL')]},\n",
|
||
" {'text': 'Identity: 460/460 (100.0%)\\nSimilarity: 460/460 (100.0%)\\nGaps: 0/460 ( 0.0%)',\n",
|
||
" 'entities': [('460/460', 'CARDINAL'),\n",
|
||
" ('100.0%', 'PERCENT'),\n",
|
||
" ('460/460', 'CARDINAL'),\n",
|
||
" ('100.0%', 'PERCENT'),\n",
|
||
" ('0/460', 'CARDINAL'),\n",
|
||
" ('0.0%', 'PERCENT')]},\n",
|
||
" {'text': '# Import python package for working with cooler files and tools for analysis\\nimport cooler\\nimport cooltools.Lib.plotting',\n",
|
||
" 'entities': []},\n",
|
||
" {'text': 'ImportError Traceback (most recent call last)', 'entities': []},\n",
|
||
" {'text': 'File ~/anaconda3/envs/cool_notebook/lib/python3.10/site-packages/cooltools/lib/plotting. py:6\\n5 try:',\n",
|
||
" 'entities': [('5', 'CARDINAL')]},\n",
|
||
" {'text': '----> 6 from matplotlib.cm import register_cmap',\n",
|
||
" 'entities': [('6', 'CARDINAL'), ('matplotlib.cm', 'ORG')]},\n",
|
||
" {'text': '7 except ImportError:',\n",
|
||
" 'entities': [('7', 'CARDINAL'), ('ImportError', 'ORG')]},\n",
|
||
" {'text': \"ImportError: cannot import name 'register_cmap' from 'matplotlib.cm' (/usr/users/papantonis1/anaconda3/envs/cool_notebook/lib/python3.10/site-packages/matp lot lib/cm. py)\\nDuring handling of the above exception, another exception occurred:\",\n",
|
||
" 'entities': []},\n",
|
||
" {'text': 'ModuleNotFoundError Traceback (most recent call last)\\nCell In{5], line 3\\n1 # Import python package for working with cooler files and tools for analysis\\nimport cooler\\nimport cooltools. lib. plotting',\n",
|
||
" 'entities': [('3', 'CARDINAL'), ('lib', 'PERSON')]},\n",
|
||
" {'text': 'File ~/anaconda3/envs/cool_notebook/lib/python3.10/site-packages/cooltools/lib/plotting. py:8\\nfrom matplotlib.cm import register_cmap',\n",
|
||
" 'entities': [('matplotlib.cm', 'ORG')]},\n",
|
||
" {'text': '—- from matplotlib.colormaps import register\\n10 import matplotlib as mpl\\n11 import matplotlib.pyplot as plt',\n",
|
||
" 'entities': [('10', 'CARDINAL'),\n",
|
||
" ('11', 'CARDINAL'),\n",
|
||
" ('matplotlib.pyplot', 'PRODUCT')]},\n",
|
||
" {'text': \"ModuleNotFoundError: No module named 'matplotlib.colormaps'\",\n",
|
||
" 'entities': []}]"
|
||
]
|
||
},
|
||
"execution_count": 45,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"\n",
|
||
"# 2) Run spaCy on each block and extract entities\n",
|
||
"entity_map = []\n",
|
||
"for block in blocks:\n",
|
||
" doc = nlp(block)\n",
|
||
" entity_map.append({\n",
|
||
" \"text\": block,\n",
|
||
" \"entities\": [(ent.text, ent.label_) for ent in doc.ents]\n",
|
||
" })\n",
|
||
"\n",
|
||
"entity_map"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 32,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"['Generate',\n",
|
||
" '.pairs',\n",
|
||
" 'and',\n",
|
||
" 'bam',\n",
|
||
" 'files',\n",
|
||
" '\\n\\n',\n",
|
||
" 'The',\n",
|
||
" 'pairtools',\n",
|
||
" 'split',\n",
|
||
" 'command',\n",
|
||
" 'is',\n",
|
||
" 'used',\n",
|
||
" 'to',\n",
|
||
" 'split',\n",
|
||
" 'the',\n",
|
||
" 'final',\n",
|
||
" '.pairsam',\n",
|
||
" 'into',\n",
|
||
" 'two',\n",
|
||
" 'files',\n",
|
||
" ':',\n",
|
||
" '.sam',\n",
|
||
" '(',\n",
|
||
" 'or',\n",
|
||
" '.bam',\n",
|
||
" ')',\n",
|
||
" '\\n',\n",
|
||
" 'and',\n",
|
||
" '.pairs',\n",
|
||
" '(',\n",
|
||
" '.pairsam',\n",
|
||
" 'has',\n",
|
||
" 'two',\n",
|
||
" 'extra',\n",
|
||
" 'columns',\n",
|
||
" 'containing',\n",
|
||
" 'the',\n",
|
||
" 'alignments',\n",
|
||
" 'from',\n",
|
||
" 'which',\n",
|
||
" 'the',\n",
|
||
" 'Micro',\n",
|
||
" '-',\n",
|
||
" 'C',\n",
|
||
" '\\n',\n",
|
||
" 'pair',\n",
|
||
" 'was',\n",
|
||
" 'extracted',\n",
|
||
" ',',\n",
|
||
" 'these',\n",
|
||
" 'two',\n",
|
||
" 'columns',\n",
|
||
" 'are',\n",
|
||
" 'not',\n",
|
||
" 'included',\n",
|
||
" 'in',\n",
|
||
" '.pairs',\n",
|
||
" 'files',\n",
|
||
" ')',\n",
|
||
" '\\n\\n',\n",
|
||
" 'pairtools',\n",
|
||
" 'split',\n",
|
||
" 'options',\n",
|
||
" ':',\n",
|
||
" '\\n\\n',\n",
|
||
" 'Parameter',\n",
|
||
" 'Function',\n",
|
||
" '\\n\\n',\n",
|
||
" 'Output',\n",
|
||
" 'pairs',\n",
|
||
" 'file',\n",
|
||
" '.',\n",
|
||
" 'If',\n",
|
||
" 'the',\n",
|
||
" 'path',\n",
|
||
" 'ends',\n",
|
||
" 'with',\n",
|
||
" '.gz',\n",
|
||
" 'or',\n",
|
||
" '.Iz4',\n",
|
||
" 'the',\n",
|
||
" 'output',\n",
|
||
" 'is',\n",
|
||
" 'pbgzip-/lz4c-',\n",
|
||
" '\\n',\n",
|
||
" '-output',\n",
|
||
" '-',\n",
|
||
" 'pairs',\n",
|
||
" 'compressed',\n",
|
||
" '.',\n",
|
||
" 'If',\n",
|
||
" 'you',\n",
|
||
" 'wish',\n",
|
||
" 'to',\n",
|
||
" 'pipe',\n",
|
||
" 'the',\n",
|
||
" 'command',\n",
|
||
" 'and',\n",
|
||
" 'output',\n",
|
||
" 'the',\n",
|
||
" 'pairs',\n",
|
||
" 'fils',\n",
|
||
" 'to',\n",
|
||
" '\\n',\n",
|
||
" 'stdout',\n",
|
||
" 'use',\n",
|
||
" '-',\n",
|
||
" 'instead',\n",
|
||
" 'of',\n",
|
||
" 'file',\n",
|
||
" 'name',\n",
|
||
" '\\n\\n',\n",
|
||
" 'Output',\n",
|
||
" 'sam',\n",
|
||
" 'file',\n",
|
||
" '.',\n",
|
||
" 'If',\n",
|
||
" 'the',\n",
|
||
" 'file',\n",
|
||
" 'name',\n",
|
||
" 'extension',\n",
|
||
" 'is',\n",
|
||
" '.bam',\n",
|
||
" ',',\n",
|
||
" 'the',\n",
|
||
" 'output',\n",
|
||
" 'will',\n",
|
||
" 'be',\n",
|
||
" 'written',\n",
|
||
" '\\n',\n",
|
||
" 'in',\n",
|
||
" 'bam',\n",
|
||
" 'format',\n",
|
||
" '.',\n",
|
||
" 'If',\n",
|
||
" 'you',\n",
|
||
" 'wish',\n",
|
||
" 'to',\n",
|
||
" 'pipe',\n",
|
||
" 'the',\n",
|
||
" 'command',\n",
|
||
" ',',\n",
|
||
" 'use',\n",
|
||
" '-',\n",
|
||
" 'instead',\n",
|
||
" 'of',\n",
|
||
" 'a',\n",
|
||
" 'file',\n",
|
||
" 'name',\n",
|
||
" '.',\n",
|
||
" '\\n\\n',\n",
|
||
" '-output',\n",
|
||
" '-',\n",
|
||
" 'sam',\n",
|
||
" 'please',\n",
|
||
" 'note',\n",
|
||
" 'that',\n",
|
||
" 'in',\n",
|
||
" 'this',\n",
|
||
" 'case',\n",
|
||
" 'the',\n",
|
||
" 'sam',\n",
|
||
" 'format',\n",
|
||
" 'will',\n",
|
||
" 'be',\n",
|
||
" 'used',\n",
|
||
" '(',\n",
|
||
" 'and',\n",
|
||
" 'can',\n",
|
||
" 'be',\n",
|
||
" 'later',\n",
|
||
" '\\n',\n",
|
||
" 'converted',\n",
|
||
" 'to',\n",
|
||
" 'bam',\n",
|
||
" 'file',\n",
|
||
" 'e.g.',\n",
|
||
" 'with',\n",
|
||
" 'the',\n",
|
||
" 'command',\n",
|
||
" 'samtools',\n",
|
||
" 'view',\n",
|
||
" '-bS',\n",
|
||
" '-@16',\n",
|
||
" '-o',\n",
|
||
" '\\n',\n",
|
||
" 'temp.bam',\n",
|
||
" '\\n\\n',\n",
|
||
" 'Command',\n",
|
||
" ':',\n",
|
||
" '\\n\\n',\n",
|
||
" 'pairtools',\n",
|
||
" 'split',\n",
|
||
" '—',\n",
|
||
" '-nproc',\n",
|
||
" '-',\n",
|
||
" 'in',\n",
|
||
" '<',\n",
|
||
" 'cores',\n",
|
||
" '>',\n",
|
||
" '—',\n",
|
||
" '-nproc',\n",
|
||
" '-',\n",
|
||
" 'out',\n",
|
||
" '<',\n",
|
||
" 'cores',\n",
|
||
" '>',\n",
|
||
" '—',\n",
|
||
" '-output',\n",
|
||
" '-',\n",
|
||
" 'pairs',\n",
|
||
" '<',\n",
|
||
" 'mapped.pairs',\n",
|
||
" '>',\n",
|
||
" '\\\\',\n",
|
||
" '\\n',\n",
|
||
" '--output',\n",
|
||
" '-',\n",
|
||
" 'sam',\n",
|
||
" '<',\n",
|
||
" 'unsorted.bam',\n",
|
||
" '>',\n",
|
||
" '<',\n",
|
||
" 'dedup.pairsam',\n",
|
||
" '>',\n",
|
||
" '\\n\\n',\n",
|
||
" 'Example',\n",
|
||
" ':',\n",
|
||
" '\\n\\n',\n",
|
||
" 'pairtools',\n",
|
||
" 'split',\n",
|
||
" '--nproc',\n",
|
||
" '-',\n",
|
||
" 'in',\n",
|
||
" '8',\n",
|
||
" '—',\n",
|
||
" '-nproc',\n",
|
||
" '-',\n",
|
||
" 'out',\n",
|
||
" '8',\n",
|
||
" '--output',\n",
|
||
" '-',\n",
|
||
" 'pairs',\n",
|
||
" 'mapped.pairs',\n",
|
||
" '-—-output',\n",
|
||
" '-',\n",
|
||
" 'sam',\n",
|
||
" 'unsorted',\n",
|
||
" '.',\n",
|
||
" 'bz',\n",
|
||
" '\\n\\n',\n",
|
||
" 'The',\n",
|
||
" '.pairs',\n",
|
||
" 'file',\n",
|
||
" 'can',\n",
|
||
" 'be',\n",
|
||
" 'used',\n",
|
||
" 'for',\n",
|
||
" 'generating',\n",
|
||
" 'contact',\n",
|
||
" 'matrix',\n",
|
||
" '\\n']"
|
||
]
|
||
},
|
||
"execution_count": 32,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"first_img_token = nlp(text_scr[0])\n",
|
||
"# type(first_img_txt)\n",
|
||
"[token.text for token in first_img_token]"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 37,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
".\n",
|
||
"\n",
|
||
"...\n",
|
||
"@ Vivaldi File Edit View...\n",
|
||
"Help CS @ s}...\n",
|
||
"[ Pannzer2 fa ekhidna2.biocenter.helsi li...\n",
|
||
"Y Imported From......\n",
|
||
"Online Bewerbung QGIS API Docume...\n",
|
||
"New Script - Earth......\n",
|
||
"OnePlus 12R revie......\n",
|
||
"Whois “Indian”in.. v...\n",
|
||
"A »\n",
|
||
"\n",
|
||
"Job status...\n",
|
||
"5204\n",
|
||
"Database: uniprot...\n",
|
||
"Oct2024 consisting of 88511531046 letters...\n",
|
||
"Processed: 5204\n",
|
||
"Finished...\n",
|
||
"Sun Nov 10 19:19:54 EET...\n",
|
||
"helsinki.o34if@passinbox.com\n",
|
||
"\n",
|
||
"Results\n",
|
||
"\n",
|
||
"¢...\n",
|
||
"© Queries 1 to 1000...\n",
|
||
"STDOUT\n",
|
||
"o STDERR\n",
|
||
"\n",
|
||
"...\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"second_img_sents = list(nlp(text_scr[1]).sents)\n",
|
||
"# type(first_img_txt)\n",
|
||
"# len(first_img_sents)\n",
|
||
"for sentence in second_img_sents:\n",
|
||
" print(f\"{sentence[:5]}...\")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 39,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stderr",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"/Users/aman/lib/python3.13/site-packages/spacy/displacy/__init__.py:108: UserWarning: [W011] It looks like you're calling displacy.serve from within a Jupyter notebook or a similar environment. This likely means you're already running a local web server, so there's no need to make displaCy start another one. Instead, you should be able to replace displacy.serve with displacy.render to show the visualization.\n",
|
||
" warnings.warn(Warnings.W011)\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
"text/html": [
|
||
"<span class=\"tex2jax_ignore\"><!DOCTYPE html>\n",
|
||
"<html lang=\"en\">\n",
|
||
" <head>\n",
|
||
" <title>displaCy</title>\n",
|
||
" </head>\n",
|
||
"\n",
|
||
" <body style=\"font-size: 16px; font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', Helvetica, Arial, sans-serif, 'Apple Color Emoji', 'Segoe UI Emoji', 'Segoe UI Symbol'; padding: 4rem 2rem; direction: ltr\">\n",
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||
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|
||
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||
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||
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||
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||
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||
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|
||
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||
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||
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|
||
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|
||
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|
||
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||
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|
||
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|
||
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|
||
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|
||
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||
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||
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||
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||
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||
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||
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|
||
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||
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||
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||
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||
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||
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||
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||
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||
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||
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||
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||
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||
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||
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||
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||
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||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
"<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"834.5\">\n",
|
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" <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"2150\">s}</tspan>\n",
|
||
" <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"2150\">PROPN</tspan>\n",
|
||
"</text>\n",
|
||
"\n",
|
||
"<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"834.5\">\n",
|
||
" <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"2325\">-¥</tspan>\n",
|
||
" <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"2325\">PUNCT</tspan>\n",
|
||
"</text>\n",
|
||
"\n",
|
||
"<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"834.5\">\n",
|
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" <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"2500\">3}</tspan>\n",
|
||
" <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"2500\">X</tspan>\n",
|
||
"</text>\n",
|
||
"\n",
|
||
"<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"834.5\">\n",
|
||
" <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"2675\">©</tspan>\n",
|
||
" <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"2675\">NOUN</tspan>\n",
|
||
"</text>\n",
|
||
"\n",
|
||
"<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"834.5\">\n",
|
||
" <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"2850\">€& @))</tspan>\n",
|
||
" <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"2850\">PROPN</tspan>\n",
|
||
"</text>\n",
|
||
"\n",
|
||
"<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"834.5\">\n",
|
||
" <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"3025\">Q &</tspan>\n",
|
||
" <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"3025\">PROPN</tspan>\n",
|
||
"</text>\n",
|
||
"\n",
|
||
"<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"834.5\">\n",
|
||
" <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"3200\">SunNov</tspan>\n",
|
||
" <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"3200\">PROPN</tspan>\n",
|
||
"</text>\n",
|
||
"\n",
|
||
"<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"834.5\">\n",
|
||
" <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"3375\">10</tspan>\n",
|
||
" <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"3375\">NUM</tspan>\n",
|
||
"</text>\n",
|
||
"\n",
|
||
"<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"834.5\">\n",
|
||
" <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"3550\">18:04</tspan>\n",
|
||
" <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"3550\">NUM</tspan>\n",
|
||
"</text>\n",
|
||
"\n",
|
||
"<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"834.5\">\n",
|
||
" <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"3725\">\n",
|
||
"</tspan>\n",
|
||
" <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"3725\">SPACE</tspan>\n",
|
||
"</text>\n",
|
||
"\n",
|
||
"<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"834.5\">\n",
|
||
" <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"3900\">©</tspan>\n",
|
||
" <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"3900\">PROPN</tspan>\n",
|
||
"</text>\n",
|
||
"\n",
|
||
"<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"834.5\">\n",
|
||
" <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"4075\">taltech</tspan>\n",
|
||
" <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"4075\">NOUN</tspan>\n",
|
||
"</text>\n",
|
||
"\n",
|
||
"<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"834.5\">\n",
|
||
" <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"4250\">moodle @</tspan>\n",
|
||
" <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"4250\">NOUN</tspan>\n",
|
||
"</text>\n",
|
||
"\n",
|
||
"<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"834.5\">\n",
|
||
" <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"4425\">alternative</tspan>\n",
|
||
" <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"4425\">ADJ</tspan>\n",
|
||
"</text>\n",
|
||
"\n",
|
||
"<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"834.5\">\n",
|
||
" <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"4600\">to</tspan>\n",
|
||
" <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"4600\">PART</tspan>\n",
|
||
"</text>\n",
|
||
"\n",
|
||
"<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"834.5\">\n",
|
||
" <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"4775\">igv</tspan>\n",
|
||
" <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"4775\">AUX</tspan>\n",
|
||
"</text>\n",
|
||
"\n",
|
||
"<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"834.5\">\n",
|
||
" <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"4950\">browse</tspan>\n",
|
||
" <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"4950\">NOUN</tspan>\n",
|
||
"</text>\n",
|
||
"\n",
|
||
"<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"834.5\">\n",
|
||
" <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"5125\">fi</tspan>\n",
|
||
" <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"5125\">NOUN</tspan>\n",
|
||
"</text>\n",
|
||
"\n",
|
||
"<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"834.5\">\n",
|
||
" <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"5300\">pannzer2 [</tspan>\n",
|
||
" <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"5300\">ADJ</tspan>\n",
|
||
"</text>\n",
|
||
"\n",
|
||
"<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"834.5\">\n",
|
||
" <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"5475\">Pannzer2</tspan>\n",
|
||
" <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"5475\">PROPN</tspan>\n",
|
||
"</text>\n",
|
||
"\n",
|
||
"<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"834.5\">\n",
|
||
" <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"5650\">fa</tspan>\n",
|
||
" <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"5650\">NOUN</tspan>\n",
|
||
"</text>\n",
|
||
"\n",
|
||
"<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"834.5\">\n",
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||
" <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"5825\">ekhidna2.biocenter.helsi</tspan>\n",
|
||
" <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"5825\">PROPN</tspan>\n",
|
||
"</text>\n",
|
||
"\n",
|
||
"<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"834.5\">\n",
|
||
" <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"6000\">li</tspan>\n",
|
||
" <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"6000\">PROPN</tspan>\n",
|
||
"</text>\n",
|
||
"\n",
|
||
"<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"834.5\">\n",
|
||
" <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"6175\">ekhidna2.biocenter.helsi</tspan>\n",
|
||
" <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"6175\">PROPN</tspan>\n",
|
||
"</text>\n",
|
||
"\n",
|
||
"<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"834.5\">\n",
|
||
" <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"6350\">li</tspan>\n",
|
||
" <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"6350\">PROPN</tspan>\n",
|
||
"</text>\n",
|
||
"\n",
|
||
"<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"834.5\">\n",
|
||
" <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"6525\">ekhidna2.biocenter.helsi</tspan>\n",
|
||
" <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"6525\">PROPN</tspan>\n",
|
||
"</text>\n",
|
||
"\n",
|
||
"<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"834.5\">\n",
|
||
" <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"6700\">\n",
|
||
"\n",
|
||
"(</tspan>\n",
|
||
" <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"6700\">SPACE</tspan>\n",
|
||
"</text>\n",
|
||
"\n",
|
||
"<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"834.5\">\n",
|
||
" <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"6875\">a) —_</tspan>\n",
|
||
" <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"6875\">X</tspan>\n",
|
||
"</text>\n",
|
||
"\n",
|
||
"<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"834.5\">\n",
|
||
" <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"7050\">></tspan>\n",
|
||
" <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"7050\">PUNCT</tspan>\n",
|
||
"</text>\n",
|
||
"\n",
|
||
"<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"834.5\">\n",
|
||
" <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"7225\">YD</tspan>\n",
|
||
" <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"7225\">PROPN</tspan>\n",
|
||
"</text>\n",
|
||
"\n",
|
||
"<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"834.5\">\n",
|
||
" <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"7400\">VY</tspan>\n",
|
||
" <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"7400\">PROPN</tspan>\n",
|
||
"</text>\n",
|
||
"\n",
|
||
"<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"834.5\">\n",
|
||
" <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"7575\">@NotSecure</tspan>\n",
|
||
" <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"7575\">PROPN</tspan>\n",
|
||
"</text>\n",
|
||
"\n",
|
||
"<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"834.5\">\n",
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||
" <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"7750\">ekhidna2.biocenter.helsinki.fi/barcosel/tmp//S2Z0dlJVpH4/index.html</tspan>\n",
|
||
" <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"7750\">ADJ</tspan>\n",
|
||
"</text>\n",
|
||
"\n",
|
||
"<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"834.5\">\n",
|
||
" <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"7925\">|_a</tspan>\n",
|
||
" <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"7925\">PROPN</tspan>\n",
|
||
"</text>\n",
|
||
"\n",
|
||
"<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"834.5\">\n",
|
||
" <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"8100\">></tspan>\n",
|
||
" <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"8100\">PUNCT</tspan>\n",
|
||
"</text>\n",
|
||
"\n",
|
||
"<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"834.5\">\n",
|
||
" <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"8275\">wo</tspan>\n",
|
||
" <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"8275\">AUX</tspan>\n",
|
||
"</text>\n",
|
||
"\n",
|
||
"<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"834.5\">\n",
|
||
" <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"8450\">Se</tspan>\n",
|
||
" <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"8450\">PROPN</tspan>\n",
|
||
"</text>\n",
|
||
"\n",
|
||
"<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"834.5\">\n",
|
||
" <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"8625\">ly</tspan>\n",
|
||
" <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"8625\">VERB</tspan>\n",
|
||
"</text>\n",
|
||
"\n",
|
||
"<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"834.5\">\n",
|
||
" <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"8800\">©</tspan>\n",
|
||
" <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"8800\">PROPN</tspan>\n",
|
||
"</text>\n",
|
||
"\n",
|
||
"<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"834.5\">\n",
|
||
" <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"8975\">F @ @</tspan>\n",
|
||
" <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"8975\">PROPN</tspan>\n",
|
||
"</text>\n",
|
||
"\n",
|
||
"<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"834.5\">\n",
|
||
" <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"9150\">\n",
|
||
"\n",
|
||
"</tspan>\n",
|
||
" <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"9150\">SPACE</tspan>\n",
|
||
"</text>\n",
|
||
"\n",
|
||
"<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"834.5\">\n",
|
||
" <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"9325\">Y</tspan>\n",
|
||
" <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"9325\">PROPN</tspan>\n",
|
||
"</text>\n",
|
||
"\n",
|
||
"<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"834.5\">\n",
|
||
" <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"9500\">Speed</tspan>\n",
|
||
" <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"9500\">PROPN</tspan>\n",
|
||
"</text>\n",
|
||
"\n",
|
||
"<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"834.5\">\n",
|
||
" <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"9675\">Dial</tspan>\n",
|
||
" <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"9675\">PROPN</tspan>\n",
|
||
"</text>\n",
|
||
"\n",
|
||
"<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"834.5\">\n",
|
||
" <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"9850\">¥</tspan>\n",
|
||
" <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"9850\">NOUN</tspan>\n",
|
||
"</text>\n",
|
||
"\n",
|
||
"<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"834.5\">\n",
|
||
" <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"10025\">Y</tspan>\n",
|
||
" <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"10025\">PROPN</tspan>\n",
|
||
"</text>\n",
|
||
"\n",
|
||
"<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"834.5\">\n",
|
||
" <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"10200\">Imported</tspan>\n",
|
||
" <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"10200\">VERB</tspan>\n",
|
||
"</text>\n",
|
||
"\n",
|
||
"<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"834.5\">\n",
|
||
" <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"10375\">From...</tspan>\n",
|
||
" <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"10375\">PROPN</tspan>\n",
|
||
"</text>\n",
|
||
"\n",
|
||
"<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"834.5\">\n",
|
||
" <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"10550\">Y</tspan>\n",
|
||
" <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"10550\">PROPN</tspan>\n",
|
||
"</text>\n",
|
||
"\n",
|
||
"<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"834.5\">\n",
|
||
" <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"10725\">Imported</tspan>\n",
|
||
" <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"10725\">VERB</tspan>\n",
|
||
"</text>\n",
|
||
"\n",
|
||
"<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"834.5\">\n",
|
||
" <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"10900\">From...</tspan>\n",
|
||
" <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"10900\">PROPN</tspan>\n",
|
||
"</text>\n",
|
||
"\n",
|
||
"<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"834.5\">\n",
|
||
" <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"11075\">Online</tspan>\n",
|
||
" <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"11075\">PROPN</tspan>\n",
|
||
"</text>\n",
|
||
"\n",
|
||
"<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"834.5\">\n",
|
||
" <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"11250\">Bewerbung</tspan>\n",
|
||
" <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"11250\">PROPN</tspan>\n",
|
||
"</text>\n",
|
||
"\n",
|
||
"<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"834.5\">\n",
|
||
" <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"11425\">QGIS</tspan>\n",
|
||
" <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"11425\">PROPN</tspan>\n",
|
||
"</text>\n",
|
||
"\n",
|
||
"<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"834.5\">\n",
|
||
" <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"11600\">API</tspan>\n",
|
||
" <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"11600\">PROPN</tspan>\n",
|
||
"</text>\n",
|
||
"\n",
|
||
"<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"834.5\">\n",
|
||
" <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"11775\">Docume...</tspan>\n",
|
||
" <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"11775\">PROPN</tspan>\n",
|
||
"</text>\n",
|
||
"\n",
|
||
"<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"834.5\">\n",
|
||
" <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"11950\">qgis -</tspan>\n",
|
||
" <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"11950\">NOUN</tspan>\n",
|
||
"</text>\n",
|
||
"\n",
|
||
"<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"834.5\">\n",
|
||
" <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"12125\">Trying</tspan>\n",
|
||
" <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"12125\">VERB</tspan>\n",
|
||
"</text>\n",
|
||
"\n",
|
||
"<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"834.5\">\n",
|
||
" <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"12300\">to</tspan>\n",
|
||
" <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"12300\">PART</tspan>\n",
|
||
"</text>\n",
|
||
"\n",
|
||
"<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"834.5\">\n",
|
||
" <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"12475\">pe...</tspan>\n",
|
||
" <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"12475\">VERB</tspan>\n",
|
||
"</text>\n",
|
||
"\n",
|
||
"<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"834.5\">\n",
|
||
" <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"12650\">New</tspan>\n",
|
||
" <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"12650\">PROPN</tspan>\n",
|
||
"</text>\n",
|
||
"\n",
|
||
"<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"834.5\">\n",
|
||
" <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"12825\">Script -</tspan>\n",
|
||
" <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"12825\">PROPN</tspan>\n",
|
||
"</text>\n",
|
||
"\n",
|
||
"<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"834.5\">\n",
|
||
" <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"13000\">Earth...</tspan>\n",
|
||
" <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"13000\">PROPN</tspan>\n",
|
||
"</text>\n",
|
||
"\n",
|
||
"<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"834.5\">\n",
|
||
" <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"13175\">Pastebin.com - #</tspan>\n",
|
||
" <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"13175\">NOUN</tspan>\n",
|
||
"</text>\n",
|
||
"\n",
|
||
"<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"834.5\">\n",
|
||
" <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"13350\">1...</tspan>\n",
|
||
" <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"13350\">NUM</tspan>\n",
|
||
"</text>\n",
|
||
"\n",
|
||
"<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"834.5\">\n",
|
||
" <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"13525\">TargetP</tspan>\n",
|
||
" <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"13525\">PROPN</tspan>\n",
|
||
"</text>\n",
|
||
"\n",
|
||
"<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"834.5\">\n",
|
||
" <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"13700\">2.0-</tspan>\n",
|
||
" <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"13700\">NUM</tspan>\n",
|
||
"</text>\n",
|
||
"\n",
|
||
"<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"834.5\">\n",
|
||
" <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"13875\">DTU...</tspan>\n",
|
||
" <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"13875\">PROPN</tspan>\n",
|
||
"</text>\n",
|
||
"\n",
|
||
"<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"834.5\">\n",
|
||
" <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"14050\">https://www.mood...</tspan>\n",
|
||
" <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"14050\">NOUN</tspan>\n",
|
||
"</text>\n",
|
||
"\n",
|
||
"<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"834.5\">\n",
|
||
" <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"14225\">OnePlus</tspan>\n",
|
||
" <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"14225\">NOUN</tspan>\n",
|
||
"</text>\n",
|
||
"\n",
|
||
"<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"834.5\">\n",
|
||
" <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"14400\">12R</tspan>\n",
|
||
" <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"14400\">NUM</tspan>\n",
|
||
"</text>\n",
|
||
"\n",
|
||
"<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"834.5\">\n",
|
||
" <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"14575\">revie...</tspan>\n",
|
||
" <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"14575\">NOUN</tspan>\n",
|
||
"</text>\n",
|
||
"\n",
|
||
"<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"834.5\">\n",
|
||
" <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"14750\">Whois “</tspan>\n",
|
||
" <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"14750\">PROPN</tspan>\n",
|
||
"</text>\n",
|
||
"\n",
|
||
"<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"834.5\">\n",
|
||
" <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"14925\">Indian”in..</tspan>\n",
|
||
" <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"14925\">PROPN</tspan>\n",
|
||
"</text>\n",
|
||
"\n",
|
||
"<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"834.5\">\n",
|
||
" <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"15100\">v</tspan>\n",
|
||
" <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"15100\">X</tspan>\n",
|
||
"</text>\n",
|
||
"\n",
|
||
"<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"834.5\">\n",
|
||
" <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"15275\">A »</tspan>\n",
|
||
" <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"15275\">DET</tspan>\n",
|
||
"</text>\n",
|
||
"\n",
|
||
"<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"834.5\">\n",
|
||
" <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"15450\">\n",
|
||
"\n",
|
||
"</tspan>\n",
|
||
" <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"15450\">SPACE</tspan>\n",
|
||
"</text>\n",
|
||
"\n",
|
||
"<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"834.5\">\n",
|
||
" <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"15625\">Job</tspan>\n",
|
||
" <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"15625\">PROPN</tspan>\n",
|
||
"</text>\n",
|
||
"\n",
|
||
"<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"834.5\">\n",
|
||
" <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"15800\">status:</tspan>\n",
|
||
" <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"15800\">NOUN</tspan>\n",
|
||
"</text>\n",
|
||
"\n",
|
||
"<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"834.5\">\n",
|
||
" <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"15975\">Finished</tspan>\n",
|
||
" <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"15975\">VERB</tspan>\n",
|
||
"</text>\n",
|
||
"\n",
|
||
"<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"834.5\">\n",
|
||
" <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"16150\">\n",
|
||
"\n",
|
||
"</tspan>\n",
|
||
" <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"16150\">SPACE</tspan>\n",
|
||
"</text>\n",
|
||
"\n",
|
||
"<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"834.5\">\n",
|
||
" <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"16325\">Title:</tspan>\n",
|
||
" <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"16325\">NOUN</tspan>\n",
|
||
"</text>\n",
|
||
"\n",
|
||
"<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"834.5\">\n",
|
||
" <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"16500\"> </tspan>\n",
|
||
" <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"16500\">SPACE</tspan>\n",
|
||
"</text>\n",
|
||
"\n",
|
||
"<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"834.5\">\n",
|
||
" <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"16675\">ecoli_nano</tspan>\n",
|
||
" <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"16675\">PROPN</tspan>\n",
|
||
"</text>\n",
|
||
"\n",
|
||
"<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"834.5\">\n",
|
||
" <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"16850\">\n",
|
||
"</tspan>\n",
|
||
" <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"16850\">SPACE</tspan>\n",
|
||
"</text>\n",
|
||
"\n",
|
||
"<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"834.5\">\n",
|
||
" <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"17025\">Proteins:</tspan>\n",
|
||
" <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"17025\">NOUN</tspan>\n",
|
||
"</text>\n",
|
||
"\n",
|
||
"<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"834.5\">\n",
|
||
" <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"17200\">5204</tspan>\n",
|
||
" <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"17200\">NUM</tspan>\n",
|
||
"</text>\n",
|
||
"\n",
|
||
"<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"834.5\">\n",
|
||
" <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"17375\">\n",
|
||
"</tspan>\n",
|
||
" <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"17375\">SPACE</tspan>\n",
|
||
"</text>\n",
|
||
"\n",
|
||
"<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"834.5\">\n",
|
||
" <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"17550\">Database:</tspan>\n",
|
||
" <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"17550\">NOUN</tspan>\n",
|
||
"</text>\n",
|
||
"\n",
|
||
"<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"834.5\">\n",
|
||
" <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"17725\">uniprot.</tspan>\n",
|
||
" <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"17725\">NOUN</tspan>\n",
|
||
"</text>\n",
|
||
"\n",
|
||
"<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"834.5\">\n",
|
||
" <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"17900\">Oct2024</tspan>\n",
|
||
" <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"17900\">PROPN</tspan>\n",
|
||
"</text>\n",
|
||
"\n",
|
||
"<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"834.5\">\n",
|
||
" <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"18075\">consisting</tspan>\n",
|
||
" <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"18075\">VERB</tspan>\n",
|
||
"</text>\n",
|
||
"\n",
|
||
"<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"834.5\">\n",
|
||
" <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"18250\">of</tspan>\n",
|
||
" <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"18250\">ADP</tspan>\n",
|
||
"</text>\n",
|
||
"\n",
|
||
"<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"834.5\">\n",
|
||
" <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"18425\">88511531046</tspan>\n",
|
||
" <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"18425\">NUM</tspan>\n",
|
||
"</text>\n",
|
||
"\n",
|
||
"<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"834.5\">\n",
|
||
" <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"18600\">letters</tspan>\n",
|
||
" <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"18600\">NOUN</tspan>\n",
|
||
"</text>\n",
|
||
"\n",
|
||
"<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"834.5\">\n",
|
||
" <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"18775\">and</tspan>\n",
|
||
" <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"18775\">CCONJ</tspan>\n",
|
||
"</text>\n",
|
||
"\n",
|
||
"<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"834.5\">\n",
|
||
" <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"18950\">248838886</tspan>\n",
|
||
" <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"18950\">NUM</tspan>\n",
|
||
"</text>\n",
|
||
"\n",
|
||
"<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"834.5\">\n",
|
||
" <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"19125\">sequences</tspan>\n",
|
||
" <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"19125\">NOUN</tspan>\n",
|
||
"</text>\n",
|
||
"\n",
|
||
"<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"834.5\">\n",
|
||
" <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"19300\">\n",
|
||
"</tspan>\n",
|
||
" <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"19300\">SPACE</tspan>\n",
|
||
"</text>\n",
|
||
"\n",
|
||
"<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"834.5\">\n",
|
||
" <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"19475\">URL: _</tspan>\n",
|
||
" <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"19475\">PROPN</tspan>\n",
|
||
"</text>\n",
|
||
"\n",
|
||
"<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"834.5\">\n",
|
||
" <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"19650\">http://ekhidna2.biocenter.helsinki</tspan>\n",
|
||
" <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"19650\">PROPN</tspan>\n",
|
||
"</text>\n",
|
||
"\n",
|
||
"<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"834.5\">\n",
|
||
" <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"19825\">.fi/</tspan>\n",
|
||
" <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"19825\">PROPN</tspan>\n",
|
||
"</text>\n",
|
||
"\n",
|
||
"<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"834.5\">\n",
|
||
" <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"20000\">barcosel/</tspan>\n",
|
||
" <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"20000\">NOUN</tspan>\n",
|
||
"</text>\n",
|
||
"\n",
|
||
"<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"834.5\">\n",
|
||
" <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"20175\">tmp//S2Z0d1J</tspan>\n",
|
||
" <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"20175\">PROPN</tspan>\n",
|
||
"</text>\n",
|
||
"\n",
|
||
"<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"834.5\">\n",
|
||
" <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"20350\">V</tspan>\n",
|
||
" <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"20350\">PROPN</tspan>\n",
|
||
"</text>\n",
|
||
"\n",
|
||
"<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"834.5\">\n",
|
||
" <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"20525\">pH4</tspan>\n",
|
||
" <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"20525\">PROPN</tspan>\n",
|
||
"</text>\n",
|
||
"\n",
|
||
"<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"834.5\">\n",
|
||
" <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"20700\">\n",
|
||
"</tspan>\n",
|
||
" <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"20700\">SPACE</tspan>\n",
|
||
"</text>\n",
|
||
"\n",
|
||
"<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"834.5\">\n",
|
||
" <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"20875\">Checksum:</tspan>\n",
|
||
" <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"20875\">NOUN</tspan>\n",
|
||
"</text>\n",
|
||
"\n",
|
||
"<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"834.5\">\n",
|
||
" <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"21050\">3c404bc04b8e94f66e48ce69ea3b988bd4b7b699bbededd9fc52bSaf</tspan>\n",
|
||
" <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"21050\">NUM</tspan>\n",
|
||
"</text>\n",
|
||
"\n",
|
||
"<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"834.5\">\n",
|
||
" <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"21225\">\n",
|
||
"</tspan>\n",
|
||
" <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"21225\">SPACE</tspan>\n",
|
||
"</text>\n",
|
||
"\n",
|
||
"<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"834.5\">\n",
|
||
" <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"21400\">Submitted:</tspan>\n",
|
||
" <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"21400\">NOUN</tspan>\n",
|
||
"</text>\n",
|
||
"\n",
|
||
"<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"834.5\">\n",
|
||
" <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"21575\">Sun</tspan>\n",
|
||
" <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"21575\">PROPN</tspan>\n",
|
||
"</text>\n",
|
||
"\n",
|
||
"<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"834.5\">\n",
|
||
" <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"21750\">Nov</tspan>\n",
|
||
" <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"21750\">PROPN</tspan>\n",
|
||
"</text>\n",
|
||
"\n",
|
||
"<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"834.5\">\n",
|
||
" <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"21925\">10</tspan>\n",
|
||
" <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"21925\">NUM</tspan>\n",
|
||
"</text>\n",
|
||
"\n",
|
||
"<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"834.5\">\n",
|
||
" <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"22100\">19:07:35</tspan>\n",
|
||
" <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"22100\">NUM</tspan>\n",
|
||
"</text>\n",
|
||
"\n",
|
||
"<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"834.5\">\n",
|
||
" <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"22275\">EET</tspan>\n",
|
||
" <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"22275\">PROPN</tspan>\n",
|
||
"</text>\n",
|
||
"\n",
|
||
"<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"834.5\">\n",
|
||
" <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"22450\">2024</tspan>\n",
|
||
" <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"22450\">NUM</tspan>\n",
|
||
"</text>\n",
|
||
"\n",
|
||
"<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"834.5\">\n",
|
||
" <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"22625\">\n",
|
||
"</tspan>\n",
|
||
" <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"22625\">SPACE</tspan>\n",
|
||
"</text>\n",
|
||
"\n",
|
||
"<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"834.5\">\n",
|
||
" <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"22800\">Started:</tspan>\n",
|
||
" <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"22800\">VERB</tspan>\n",
|
||
"</text>\n",
|
||
"\n",
|
||
"<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"834.5\">\n",
|
||
" <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"22975\">Sun</tspan>\n",
|
||
" <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"22975\">PROPN</tspan>\n",
|
||
"</text>\n",
|
||
"\n",
|
||
"<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"834.5\">\n",
|
||
" <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"23150\">Nov</tspan>\n",
|
||
" <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"23150\">PROPN</tspan>\n",
|
||
"</text>\n",
|
||
"\n",
|
||
"<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"834.5\">\n",
|
||
" <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"23325\">10</tspan>\n",
|
||
" <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"23325\">NUM</tspan>\n",
|
||
"</text>\n",
|
||
"\n",
|
||
"<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"834.5\">\n",
|
||
" <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"23500\">19:07:55</tspan>\n",
|
||
" <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"23500\">NUM</tspan>\n",
|
||
"</text>\n",
|
||
"\n",
|
||
"<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"834.5\">\n",
|
||
" <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"23675\">EET</tspan>\n",
|
||
" <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"23675\">PROPN</tspan>\n",
|
||
"</text>\n",
|
||
"\n",
|
||
"<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"834.5\">\n",
|
||
" <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"23850\">2024</tspan>\n",
|
||
" <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"23850\">NUM</tspan>\n",
|
||
"</text>\n",
|
||
"\n",
|
||
"<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"834.5\">\n",
|
||
" <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"24025\">\n",
|
||
"</tspan>\n",
|
||
" <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"24025\">SPACE</tspan>\n",
|
||
"</text>\n",
|
||
"\n",
|
||
"<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"834.5\">\n",
|
||
" <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"24200\">Processed:</tspan>\n",
|
||
" <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"24200\">VERB</tspan>\n",
|
||
"</text>\n",
|
||
"\n",
|
||
"<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"834.5\">\n",
|
||
" <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"24375\">5204</tspan>\n",
|
||
" <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"24375\">NUM</tspan>\n",
|
||
"</text>\n",
|
||
"\n",
|
||
"<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"834.5\">\n",
|
||
" <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"24550\">\n",
|
||
"</tspan>\n",
|
||
" <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"24550\">SPACE</tspan>\n",
|
||
"</text>\n",
|
||
"\n",
|
||
"<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"834.5\">\n",
|
||
" <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"24725\">Finished:</tspan>\n",
|
||
" <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"24725\">VERB</tspan>\n",
|
||
"</text>\n",
|
||
"\n",
|
||
"<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"834.5\">\n",
|
||
" <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"24900\">Sun</tspan>\n",
|
||
" <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"24900\">PROPN</tspan>\n",
|
||
"</text>\n",
|
||
"\n",
|
||
"<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"834.5\">\n",
|
||
" <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"25075\">Nov</tspan>\n",
|
||
" <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"25075\">PROPN</tspan>\n",
|
||
"</text>\n",
|
||
"\n",
|
||
"<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"834.5\">\n",
|
||
" <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"25250\">10</tspan>\n",
|
||
" <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"25250\">NUM</tspan>\n",
|
||
"</text>\n",
|
||
"\n",
|
||
"<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"834.5\">\n",
|
||
" <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"25425\">19:19:54</tspan>\n",
|
||
" <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"25425\">NUM</tspan>\n",
|
||
"</text>\n",
|
||
"\n",
|
||
"<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"834.5\">\n",
|
||
" <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"25600\">EET</tspan>\n",
|
||
" <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"25600\">PROPN</tspan>\n",
|
||
"</text>\n",
|
||
"\n",
|
||
"<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"834.5\">\n",
|
||
" <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"25775\">2024</tspan>\n",
|
||
" <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"25775\">NUM</tspan>\n",
|
||
"</text>\n",
|
||
"\n",
|
||
"<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"834.5\">\n",
|
||
" <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"25950\">\n",
|
||
"</tspan>\n",
|
||
" <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"25950\">SPACE</tspan>\n",
|
||
"</text>\n",
|
||
"\n",
|
||
"<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"834.5\">\n",
|
||
" <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"26125\">E-</tspan>\n",
|
||
" <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"26125\">NOUN</tspan>\n",
|
||
"</text>\n",
|
||
"\n",
|
||
"<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"834.5\">\n",
|
||
" <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"26300\">mail: _</tspan>\n",
|
||
" <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"26300\">NOUN</tspan>\n",
|
||
"</text>\n",
|
||
"\n",
|
||
"<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"834.5\">\n",
|
||
" <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"26475\">helsinki.o34if@passinbox.com</tspan>\n",
|
||
" <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"26475\">PROPN</tspan>\n",
|
||
"</text>\n",
|
||
"\n",
|
||
"<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"834.5\">\n",
|
||
" <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"26650\">\n",
|
||
"\n",
|
||
"</tspan>\n",
|
||
" <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"26650\">SPACE</tspan>\n",
|
||
"</text>\n",
|
||
"\n",
|
||
"<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"834.5\">\n",
|
||
" <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"26825\">Results</tspan>\n",
|
||
" <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"26825\">NOUN</tspan>\n",
|
||
"</text>\n",
|
||
"\n",
|
||
"<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"834.5\">\n",
|
||
" <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"27000\">\n",
|
||
"\n",
|
||
"</tspan>\n",
|
||
" <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"27000\">SPACE</tspan>\n",
|
||
"</text>\n",
|
||
"\n",
|
||
"<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"834.5\">\n",
|
||
" <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"27175\">¢</tspan>\n",
|
||
" <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"27175\">PROPN</tspan>\n",
|
||
"</text>\n",
|
||
"\n",
|
||
"<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"834.5\">\n",
|
||
" <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"27350\">HTML</tspan>\n",
|
||
" <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"27350\">PROPN</tspan>\n",
|
||
"</text>\n",
|
||
"\n",
|
||
"<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"834.5\">\n",
|
||
" <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"27525\">summary:</tspan>\n",
|
||
" <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"27525\">NOUN</tspan>\n",
|
||
"</text>\n",
|
||
"\n",
|
||
"<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"834.5\">\n",
|
||
" <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"27700\">\n",
|
||
"</tspan>\n",
|
||
" <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"27700\">SPACE</tspan>\n",
|
||
"</text>\n",
|
||
"\n",
|
||
"<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"834.5\">\n",
|
||
" <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"27875\">©</tspan>\n",
|
||
" <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"27875\">PROPN</tspan>\n",
|
||
"</text>\n",
|
||
"\n",
|
||
"<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"834.5\">\n",
|
||
" <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"28050\">Queries</tspan>\n",
|
||
" <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"28050\">NOUN</tspan>\n",
|
||
"</text>\n",
|
||
"\n",
|
||
"<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"834.5\">\n",
|
||
" <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"28225\">1</tspan>\n",
|
||
" <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"28225\">NUM</tspan>\n",
|
||
"</text>\n",
|
||
"\n",
|
||
"<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"834.5\">\n",
|
||
" <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"28400\">to</tspan>\n",
|
||
" <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"28400\">ADP</tspan>\n",
|
||
"</text>\n",
|
||
"\n",
|
||
"<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"834.5\">\n",
|
||
" <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"28575\">1000</tspan>\n",
|
||
" <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"28575\">NUM</tspan>\n",
|
||
"</text>\n",
|
||
"\n",
|
||
"<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"834.5\">\n",
|
||
" <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"28750\">\n",
|
||
"</tspan>\n",
|
||
" <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"28750\">SPACE</tspan>\n",
|
||
"</text>\n",
|
||
"\n",
|
||
"<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"834.5\">\n",
|
||
" <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"28925\">Queries</tspan>\n",
|
||
" <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"28925\">NOUN</tspan>\n",
|
||
"</text>\n",
|
||
"\n",
|
||
"<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"834.5\">\n",
|
||
" <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"29100\">1001</tspan>\n",
|
||
" <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"29100\">NUM</tspan>\n",
|
||
"</text>\n",
|
||
"\n",
|
||
"<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"834.5\">\n",
|
||
" <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"29275\">to</tspan>\n",
|
||
" <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"29275\">ADP</tspan>\n",
|
||
"</text>\n",
|
||
"\n",
|
||
"<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"834.5\">\n",
|
||
" <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"29450\">2000</tspan>\n",
|
||
" <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"29450\">NUM</tspan>\n",
|
||
"</text>\n",
|
||
"\n",
|
||
"<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"834.5\">\n",
|
||
" <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"29625\">\n",
|
||
"</tspan>\n",
|
||
" <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"29625\">SPACE</tspan>\n",
|
||
"</text>\n",
|
||
"\n",
|
||
"<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"834.5\">\n",
|
||
" <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"29800\">Queries</tspan>\n",
|
||
" <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"29800\">NOUN</tspan>\n",
|
||
"</text>\n",
|
||
"\n",
|
||
"<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"834.5\">\n",
|
||
" <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"29975\">2001</tspan>\n",
|
||
" <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"29975\">NUM</tspan>\n",
|
||
"</text>\n",
|
||
"\n",
|
||
"<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"834.5\">\n",
|
||
" <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"30150\">to</tspan>\n",
|
||
" <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"30150\">ADP</tspan>\n",
|
||
"</text>\n",
|
||
"\n",
|
||
"<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"834.5\">\n",
|
||
" <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"30325\">3000</tspan>\n",
|
||
" <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"30325\">NUM</tspan>\n",
|
||
"</text>\n",
|
||
"\n",
|
||
"<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"834.5\">\n",
|
||
" <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"30500\">\n",
|
||
"</tspan>\n",
|
||
" <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"30500\">SPACE</tspan>\n",
|
||
"</text>\n",
|
||
"\n",
|
||
"<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"834.5\">\n",
|
||
" <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"30675\">Queries</tspan>\n",
|
||
" <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"30675\">NOUN</tspan>\n",
|
||
"</text>\n",
|
||
"\n",
|
||
"<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"834.5\">\n",
|
||
" <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"30850\">3001</tspan>\n",
|
||
" <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"30850\">NUM</tspan>\n",
|
||
"</text>\n",
|
||
"\n",
|
||
"<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"834.5\">\n",
|
||
" <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"31025\">to</tspan>\n",
|
||
" <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"31025\">ADP</tspan>\n",
|
||
"</text>\n",
|
||
"\n",
|
||
"<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"834.5\">\n",
|
||
" <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"31200\">4000</tspan>\n",
|
||
" <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"31200\">NUM</tspan>\n",
|
||
"</text>\n",
|
||
"\n",
|
||
"<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"834.5\">\n",
|
||
" <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"31375\">\n",
|
||
"</tspan>\n",
|
||
" <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"31375\">SPACE</tspan>\n",
|
||
"</text>\n",
|
||
"\n",
|
||
"<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"834.5\">\n",
|
||
" <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"31550\">Queries</tspan>\n",
|
||
" <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"31550\">PROPN</tspan>\n",
|
||
"</text>\n",
|
||
"\n",
|
||
"<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"834.5\">\n",
|
||
" <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"31725\">4001</tspan>\n",
|
||
" <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"31725\">NUM</tspan>\n",
|
||
"</text>\n",
|
||
"\n",
|
||
"<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"834.5\">\n",
|
||
" <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"31900\">to</tspan>\n",
|
||
" <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"31900\">ADP</tspan>\n",
|
||
"</text>\n",
|
||
"\n",
|
||
"<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"834.5\">\n",
|
||
" <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"32075\">5000</tspan>\n",
|
||
" <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"32075\">NUM</tspan>\n",
|
||
"</text>\n",
|
||
"\n",
|
||
"<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"834.5\">\n",
|
||
" <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"32250\">\n",
|
||
"</tspan>\n",
|
||
" <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"32250\">SPACE</tspan>\n",
|
||
"</text>\n",
|
||
"\n",
|
||
"<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"834.5\">\n",
|
||
" <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"32425\">Queries</tspan>\n",
|
||
" <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"32425\">PROPN</tspan>\n",
|
||
"</text>\n",
|
||
"\n",
|
||
"<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"834.5\">\n",
|
||
" <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"32600\">5001</tspan>\n",
|
||
" <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"32600\">NUM</tspan>\n",
|
||
"</text>\n",
|
||
"\n",
|
||
"<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"834.5\">\n",
|
||
" <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"32775\">to</tspan>\n",
|
||
" <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"32775\">ADP</tspan>\n",
|
||
"</text>\n",
|
||
"\n",
|
||
"<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"834.5\">\n",
|
||
" <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"32950\">5204</tspan>\n",
|
||
" <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"32950\">NUM</tspan>\n",
|
||
"</text>\n",
|
||
"\n",
|
||
"<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"834.5\">\n",
|
||
" <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"33125\">\n",
|
||
"</tspan>\n",
|
||
" <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"33125\">SPACE</tspan>\n",
|
||
"</text>\n",
|
||
"\n",
|
||
"<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"834.5\">\n",
|
||
" <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"33300\">¢</tspan>\n",
|
||
" <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"33300\">PROPN</tspan>\n",
|
||
"</text>\n",
|
||
"\n",
|
||
"<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"834.5\">\n",
|
||
" <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"33475\">Download:</tspan>\n",
|
||
" <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"33475\">PROPN</tspan>\n",
|
||
"</text>\n",
|
||
"\n",
|
||
"<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"834.5\">\n",
|
||
" <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"33650\">\n",
|
||
"</tspan>\n",
|
||
" <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"33650\">SPACE</tspan>\n",
|
||
"</text>\n",
|
||
"\n",
|
||
"<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"834.5\">\n",
|
||
" <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"33825\">o</tspan>\n",
|
||
" <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"33825\">PROPN</tspan>\n",
|
||
"</text>\n",
|
||
"\n",
|
||
"<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"834.5\">\n",
|
||
" <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"34000\">Annotations (</tspan>\n",
|
||
" <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"34000\">PROPN</tspan>\n",
|
||
"</text>\n",
|
||
"\n",
|
||
"<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"834.5\">\n",
|
||
" <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"34175\">parseable)</tspan>\n",
|
||
" <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"34175\">ADJ</tspan>\n",
|
||
"</text>\n",
|
||
"\n",
|
||
"<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"834.5\">\n",
|
||
" <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"34350\">\n",
|
||
"</tspan>\n",
|
||
" <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"34350\">SPACE</tspan>\n",
|
||
"</text>\n",
|
||
"\n",
|
||
"<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"834.5\">\n",
|
||
" <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"34525\">o</tspan>\n",
|
||
" <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"34525\">NOUN</tspan>\n",
|
||
"</text>\n",
|
||
"\n",
|
||
"<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"834.5\">\n",
|
||
" <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"34700\">DE</tspan>\n",
|
||
" <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"34700\">PROPN</tspan>\n",
|
||
"</text>\n",
|
||
"\n",
|
||
"<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"834.5\">\n",
|
||
" <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"34875\">prediction</tspan>\n",
|
||
" <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"34875\">NOUN</tspan>\n",
|
||
"</text>\n",
|
||
"\n",
|
||
"<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"834.5\">\n",
|
||
" <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"35050\">details</tspan>\n",
|
||
" <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"35050\">NOUN</tspan>\n",
|
||
"</text>\n",
|
||
"\n",
|
||
"<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"834.5\">\n",
|
||
" <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"35225\">\n",
|
||
"</tspan>\n",
|
||
" <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"35225\">SPACE</tspan>\n",
|
||
"</text>\n",
|
||
"\n",
|
||
"<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"834.5\">\n",
|
||
" <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"35400\">o</tspan>\n",
|
||
" <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"35400\">NOUN</tspan>\n",
|
||
"</text>\n",
|
||
"\n",
|
||
"<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"834.5\">\n",
|
||
" <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"35575\">GO</tspan>\n",
|
||
" <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"35575\">PROPN</tspan>\n",
|
||
"</text>\n",
|
||
"\n",
|
||
"<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"834.5\">\n",
|
||
" <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"35750\">prediction</tspan>\n",
|
||
" <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"35750\">NOUN</tspan>\n",
|
||
"</text>\n",
|
||
"\n",
|
||
"<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"834.5\">\n",
|
||
" <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"35925\">details</tspan>\n",
|
||
" <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"35925\">NOUN</tspan>\n",
|
||
"</text>\n",
|
||
"\n",
|
||
"<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"834.5\">\n",
|
||
" <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"36100\">\n",
|
||
"</tspan>\n",
|
||
" <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"36100\">SPACE</tspan>\n",
|
||
"</text>\n",
|
||
"\n",
|
||
"<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"834.5\">\n",
|
||
" <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"36275\">¢</tspan>\n",
|
||
" <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"36275\">PROPN</tspan>\n",
|
||
"</text>\n",
|
||
"\n",
|
||
"<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"834.5\">\n",
|
||
" <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"36450\">Logs:</tspan>\n",
|
||
" <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"36450\">PROPN</tspan>\n",
|
||
"</text>\n",
|
||
"\n",
|
||
"<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"834.5\">\n",
|
||
" <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"36625\">\n",
|
||
"</tspan>\n",
|
||
" <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"36625\">SPACE</tspan>\n",
|
||
"</text>\n",
|
||
"\n",
|
||
"<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"834.5\">\n",
|
||
" <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"36800\">o</tspan>\n",
|
||
" <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"36800\">NOUN</tspan>\n",
|
||
"</text>\n",
|
||
"\n",
|
||
"<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"834.5\">\n",
|
||
" <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"36975\">Uploaded</tspan>\n",
|
||
" <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"36975\">ADJ</tspan>\n",
|
||
"</text>\n",
|
||
"\n",
|
||
"<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"834.5\">\n",
|
||
" <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"37150\">sequences</tspan>\n",
|
||
" <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"37150\">NOUN</tspan>\n",
|
||
"</text>\n",
|
||
"\n",
|
||
"<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"834.5\">\n",
|
||
" <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"37325\">\n",
|
||
"</tspan>\n",
|
||
" <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"37325\">SPACE</tspan>\n",
|
||
"</text>\n",
|
||
"\n",
|
||
"<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"834.5\">\n",
|
||
" <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"37500\">o</tspan>\n",
|
||
" <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"37500\">NOUN</tspan>\n",
|
||
"</text>\n",
|
||
"\n",
|
||
"<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"834.5\">\n",
|
||
" <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"37675\">STDOUT</tspan>\n",
|
||
" <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"37675\">PROPN</tspan>\n",
|
||
"</text>\n",
|
||
"\n",
|
||
"<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"834.5\">\n",
|
||
" <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"37850\">\n",
|
||
"</tspan>\n",
|
||
" <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"37850\">SPACE</tspan>\n",
|
||
"</text>\n",
|
||
"\n",
|
||
"<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"834.5\">\n",
|
||
" <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"38025\">o</tspan>\n",
|
||
" <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"38025\">NOUN</tspan>\n",
|
||
"</text>\n",
|
||
"\n",
|
||
"<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"834.5\">\n",
|
||
" <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"38200\">STDERR</tspan>\n",
|
||
" <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"38200\">NOUN</tspan>\n",
|
||
"</text>\n",
|
||
"\n",
|
||
"<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"834.5\">\n",
|
||
" <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"38375\">\n",
|
||
"\n",
|
||
"</tspan>\n",
|
||
" <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"38375\">SPACE</tspan>\n",
|
||
"</text>\n",
|
||
"\n",
|
||
"<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"834.5\">\n",
|
||
" <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"38550\">oO</tspan>\n",
|
||
" <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"38550\">NOUN</tspan>\n",
|
||
"</text>\n",
|
||
"\n",
|
||
"<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"834.5\">\n",
|
||
" <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"38725\">00</tspan>\n",
|
||
" <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"38725\">NUM</tspan>\n",
|
||
"</text>\n",
|
||
"\n",
|
||
"<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"834.5\">\n",
|
||
" <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"38900\">0</tspan>\n",
|
||
" <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"38900\">NUM</tspan>\n",
|
||
"</text>\n",
|
||
"\n",
|
||
"<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"834.5\">\n",
|
||
" <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"39075\">0</tspan>\n",
|
||
" <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"39075\">NUM</tspan>\n",
|
||
"</text>\n",
|
||
"\n",
|
||
"<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"834.5\">\n",
|
||
" <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"39250\">\n",
|
||
"\n",
|
||
"</tspan>\n",
|
||
" <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"39250\">SPACE</tspan>\n",
|
||
"</text>\n",
|
||
"\n",
|
||
"<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"834.5\">\n",
|
||
" <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"39425\">CO</tspan>\n",
|
||
" <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"39425\">PROPN</tspan>\n",
|
||
"</text>\n",
|
||
"\n",
|
||
"<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"834.5\">\n",
|
||
" <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"39600\">en</tspan>\n",
|
||
" <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"39600\">ADP</tspan>\n",
|
||
"</text>\n",
|
||
"\n",
|
||
"<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"834.5\">\n",
|
||
" <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"39775\">100%</tspan>\n",
|
||
" <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"39775\">NOUN</tspan>\n",
|
||
"</text>\n",
|
||
"\n",
|
||
"<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"834.5\">\n",
|
||
" <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"39950\">18:04</tspan>\n",
|
||
" <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"39950\">NUM</tspan>\n",
|
||
"</text>\n",
|
||
"\n",
|
||
"<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"834.5\">\n",
|
||
" <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"40125\">\n",
|
||
"</tspan>\n",
|
||
" <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"40125\">SPACE</tspan>\n",
|
||
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|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Shutting down server on port 5003.\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"from spacy import displacy\n",
|
||
"s_sc = nlp(text_scr[1])\n",
|
||
"displacy.serve(s_sc, style=\"dep\", port=5003)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 41,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"['vivaldi',\n",
|
||
" 'file',\n",
|
||
" 'edit',\n",
|
||
" 'view',\n",
|
||
" 'bookmarks',\n",
|
||
" 'mail',\n",
|
||
" 'tools',\n",
|
||
" 'window',\n",
|
||
" 'help',\n",
|
||
" 'cs',\n",
|
||
" 's',\n",
|
||
" '-¥',\n",
|
||
" '3',\n",
|
||
" '©',\n",
|
||
" '€',\n",
|
||
" 'q',\n",
|
||
" 'sunnov',\n",
|
||
" '10',\n",
|
||
" '18:04',\n",
|
||
" '©',\n",
|
||
" 'taltech',\n",
|
||
" 'moodle',\n",
|
||
" 'alternative',\n",
|
||
" 'igv',\n",
|
||
" 'browse',\n",
|
||
" 'fi',\n",
|
||
" 'pannzer2',\n",
|
||
" 'pannzer2',\n",
|
||
" 'fa',\n",
|
||
" 'ekhidna2.biocenter.helsi',\n",
|
||
" 'li',\n",
|
||
" 'ekhidna2.biocenter.helsi',\n",
|
||
" 'li',\n",
|
||
" 'ekhidna2.biocenter.helsi',\n",
|
||
" '>',\n",
|
||
" 'yd',\n",
|
||
" 'vy',\n",
|
||
" '@notsecure',\n",
|
||
" 'ekhidna2.biocenter.helsinki.fi/barcosel/tmp//s2z0dljvph4/index.html',\n",
|
||
" '|_a',\n",
|
||
" '>',\n",
|
||
" 'will',\n",
|
||
" 'se',\n",
|
||
" 'ly',\n",
|
||
" '©',\n",
|
||
" 'f',\n",
|
||
" 'y',\n",
|
||
" 'speed',\n",
|
||
" 'dial',\n",
|
||
" '¥',\n",
|
||
" 'y',\n",
|
||
" 'import',\n",
|
||
" 'y',\n",
|
||
" 'import',\n",
|
||
" 'online',\n",
|
||
" 'bewerbung',\n",
|
||
" 'qgis',\n",
|
||
" 'api',\n",
|
||
" 'docume',\n",
|
||
" 'qgis',\n",
|
||
" 'try',\n",
|
||
" 'pe',\n",
|
||
" 'new',\n",
|
||
" 'script',\n",
|
||
" 'earth',\n",
|
||
" 'pastebin.com',\n",
|
||
" '1',\n",
|
||
" 'targetp',\n",
|
||
" '2.0-',\n",
|
||
" 'dtu',\n",
|
||
" 'https://www.mood',\n",
|
||
" 'oneplus',\n",
|
||
" '12r',\n",
|
||
" 'revie',\n",
|
||
" 'whois',\n",
|
||
" 'indian”in',\n",
|
||
" 'v',\n",
|
||
" 'job',\n",
|
||
" 'status',\n",
|
||
" 'finish',\n",
|
||
" 'title',\n",
|
||
" 'ecoli_nano',\n",
|
||
" 'protein',\n",
|
||
" '5204',\n",
|
||
" 'database',\n",
|
||
" 'uniprot',\n",
|
||
" 'oct2024',\n",
|
||
" 'consist',\n",
|
||
" '88511531046',\n",
|
||
" 'letter',\n",
|
||
" '248838886',\n",
|
||
" 'sequence',\n",
|
||
" 'url',\n",
|
||
" 'http://ekhidna2.biocenter.helsinki',\n",
|
||
" '.fi',\n",
|
||
" 'barcosel',\n",
|
||
" 'tmp//s2z0d1j',\n",
|
||
" 'v',\n",
|
||
" 'ph4',\n",
|
||
" 'checksum',\n",
|
||
" '3c404bc04b8e94f66e48ce69ea3b988bd4b7b699bbededd9fc52bsaf',\n",
|
||
" 'submitted',\n",
|
||
" 'sun',\n",
|
||
" 'nov',\n",
|
||
" '10',\n",
|
||
" '19:07:35',\n",
|
||
" 'eet',\n",
|
||
" '2024',\n",
|
||
" 'start',\n",
|
||
" 'sun',\n",
|
||
" 'nov',\n",
|
||
" '10',\n",
|
||
" '19:07:55',\n",
|
||
" 'eet',\n",
|
||
" '2024',\n",
|
||
" 'process',\n",
|
||
" '5204',\n",
|
||
" 'finish',\n",
|
||
" 'sun',\n",
|
||
" 'nov',\n",
|
||
" '10',\n",
|
||
" '19:19:54',\n",
|
||
" 'eet',\n",
|
||
" '2024',\n",
|
||
" 'e',\n",
|
||
" 'mail',\n",
|
||
" 'helsinki.o34if@passinbox.com',\n",
|
||
" 'result',\n",
|
||
" '¢',\n",
|
||
" 'html',\n",
|
||
" 'summary',\n",
|
||
" '©',\n",
|
||
" 'query',\n",
|
||
" '1',\n",
|
||
" '1000',\n",
|
||
" 'query',\n",
|
||
" '1001',\n",
|
||
" '2000',\n",
|
||
" 'query',\n",
|
||
" '2001',\n",
|
||
" '3000',\n",
|
||
" 'query',\n",
|
||
" '3001',\n",
|
||
" '4000',\n",
|
||
" 'queries',\n",
|
||
" '4001',\n",
|
||
" '5000',\n",
|
||
" 'queries',\n",
|
||
" '5001',\n",
|
||
" '5204',\n",
|
||
" '¢',\n",
|
||
" 'download',\n",
|
||
" 'o',\n",
|
||
" 'annotations',\n",
|
||
" 'parseable',\n",
|
||
" 'o',\n",
|
||
" 'de',\n",
|
||
" 'prediction',\n",
|
||
" 'detail',\n",
|
||
" 'o',\n",
|
||
" 'prediction',\n",
|
||
" 'detail',\n",
|
||
" '¢',\n",
|
||
" 'logs',\n",
|
||
" 'o',\n",
|
||
" 'uploaded',\n",
|
||
" 'sequence',\n",
|
||
" 'o',\n",
|
||
" 'stdout',\n",
|
||
" 'o',\n",
|
||
" 'stderr',\n",
|
||
" 'oo',\n",
|
||
" '00',\n",
|
||
" '0',\n",
|
||
" '0',\n",
|
||
" 'co',\n",
|
||
" 'en',\n",
|
||
" '100',\n",
|
||
" '18:04']"
|
||
]
|
||
},
|
||
"execution_count": 41,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"s_sc = nlp(text_scr[1])\n",
|
||
"def is_token_allowed(token):\n",
|
||
" return bool(\n",
|
||
" token\n",
|
||
" and str(token).strip()\n",
|
||
" and not token.is_stop\n",
|
||
" and not token.is_punct\n",
|
||
" )\n",
|
||
"\n",
|
||
"def preprocess_token(token):\n",
|
||
" return token.lemma_.strip().lower()\n",
|
||
"\n",
|
||
"complete_filtered_tokens = [\n",
|
||
"preprocess_token(token)\n",
|
||
"for token in s_sc\n",
|
||
"if is_token_allowed(token)\n",
|
||
"]\n",
|
||
"\n",
|
||
"complete_filtered_tokens"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"Custom Rule-Based Extraction (Matcher / PhraseMatcher)\\n \n",
|
||
"Dependency-based pattern extraction\\n \n",
|
||
"Pipeline custom components\\n \n",
|
||
"Text classification\\n \n",
|
||
"Phrase extraction (noun chunks)\\n \n",
|
||
"Token-level features\\n \n",
|
||
"Entity Linking\\n "
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"version: \"3\"\n",
|
||
"networks:\n",
|
||
" gitea:\n",
|
||
" external: false\n",
|
||
"services:\n",
|
||
" server:\n",
|
||
" image: docker.gitea.com/gitea:1.25.2\n",
|
||
" container_name: gitea\n",
|
||
" environment:\n",
|
||
" - USER_UID=1000\n",
|
||
" - USER_GID=1000\n",
|
||
" - GITEA__database__DB_TYPE=postgres\n",
|
||
" - GITEA__database__HOST=db:5432\n",
|
||
" - GITEA__database__NAME=gitea\n",
|
||
" - GITEA__database__USER=gitea\n",
|
||
" - GITEA__database__PASSWD=seoul123\n",
|
||
" restart: always\n",
|
||
" networks:\n",
|
||
" - gitea\n",
|
||
" volumes:\n",
|
||
" - ./gitea:/data\n",
|
||
" - /etc/timezone:/etc/timezone:ro\n",
|
||
" - /etc/localtime:/etc/localtime:ro\n",
|
||
" ports:\n",
|
||
" - \"3000:3000\"\n",
|
||
" - \"222:22\"\n",
|
||
" depends_on:\n",
|
||
" - db\n",
|
||
" db:\n",
|
||
" image: docker.io/library/postgres:18\n",
|
||
" restart: always\n",
|
||
" environment:\n",
|
||
" - POSTGRES_USER=gitea\n",
|
||
" - POSTGRES_PASSWORD=seoul123\n",
|
||
" - POSTGRES_DB=gitea\n",
|
||
" networks:\n",
|
||
" - gitea\n",
|
||
" volumes:\n",
|
||
" - ./postgres:/var/lib/postgresql/data\n",
|
||
" healthcheck:\n",
|
||
" test: [\"CMD-SHELL\", \"pg_isready -U gitea -d gitea\"]\n",
|
||
" interval: 10s\n",
|
||
" timeout: 5s\n",
|
||
" retries: 5"
|
||
]
|
||
}
|
||
],
|
||
"metadata": {
|
||
"kernelspec": {
|
||
"display_name": "3.12.0",
|
||
"language": "python",
|
||
"name": "python3"
|
||
},
|
||
"language_info": {
|
||
"codemirror_mode": {
|
||
"name": "ipython",
|
||
"version": 3
|
||
},
|
||
"file_extension": ".py",
|
||
"mimetype": "text/x-python",
|
||
"name": "python",
|
||
"nbconvert_exporter": "python",
|
||
"pygments_lexer": "ipython3",
|
||
"version": "3.13.9"
|
||
}
|
||
},
|
||
"nbformat": 4,
|
||
"nbformat_minor": 2
|
||
}
|