cff-version: 1.2.0
message: >-
  If you use this software in your research, please cite it using the
  following metadata.
type: software
title: >-
  chromatin-gnn: Variational Graph Autoencoder for learning latent
  representations of chromatin topology from Hi-C data
authors:
  - family-names: Okada
    given-names: Toru
    alias: ToruOkadaOi
    # orcid: "https://orcid.org/XXXX-XXXX-XXXX-XXXX"   # add your ORCID
version: "1.0.0"
date-released: "2024-01-01"   # update to actual release date
doi: "10.5281/zenodo.XXXXXXX" # replace with actual Zenodo DOI after deposit
repository-code: "https://github.com/ToruOkadaOi/chromatin-gnn"
url: "https://github.com/ToruOkadaOi/chromatin-gnn"
license: MIT
abstract: >-
  A Variational Graph Autoencoder (VGAE) applied to Hi-C chromatin contact
  data to learn unsupervised latent representations of chromatin topology.
  Genomic bins are modelled as graph nodes with ChIP-seq features (CTCF,
  H3K27me3); normalised contact frequencies define weighted edges.
  The model is trained on GM12878 lymphoblastoid cells and evaluated on
  both link-prediction (AUROC/AP) and the biological interpretability of
  the latent space against known A/B compartments.
keywords:
  - chromatin
  - Hi-C
  - graph neural network
  - variational autoencoder
  - VGAE
  - A/B compartments
  - topologically associating domains
  - TAD
  - epigenomics
  - 3D genome organisation
references:
  - type: article
    title: >-
      A 3D Map of the Human Genome at Kilobase Resolution Reveals
      Principles of Chromatin Looping
    authors:
      - family-names: Rao
        given-names: "Suhas S. P."
      - family-names: Huntley
        given-names: "Miriam H."
    year: 2014
    journal: Cell
    doi: 10.1016/j.cell.2014.11.021
  - type: article
    title: "Variational Graph Auto-Encoders"
    authors:
      - family-names: Kipf
        given-names: "Thomas N."
      - family-names: Welling
        given-names: Max
    year: 2016
    url: "https://arxiv.org/abs/1611.07308"