{
  "abstract": "Genome regulation unfolds through dynamic and stochastic single-molecule events. Deep learning models that read gene regulatory activities from DNA sequences have markedly advanced our ability to predict cell-population-level averages. However, they cannot capture the full spectrum of heterogeneity and dynamics at the single-molecule level, which directly reflect the underlying mechanisms of genome regulation. Here, we introduce a sequence-to-distribution (S2d) framework that directly models the entire probabilistic distribution of single-molecule chromatin states from DNA sequence alone. We used this framework to train Mola, a model of single-molecule chromatin accessibility. We harness Mola to investigate sequence-based regulatory code and mechanisms for multiple aspects of chromatin regulation at the single-molecule level. By quantifying sequence-driven nucleosome regulation, we reveal distinct modes of transcription factor engagement with nucleosomes, including a novel nucleosome-trapping factor, ZNF146. We characterized the sequence determinants governing the +1 nucleosome after transcription start sites and identified YY1 as the strongest +1 nucleosome-positioning factor. Leveraging long-context single-molecule modeling, we further discovered a shared sequence basis for the co-accessibility of regulatory elements and the co-footprinting of transcription factors. Joint prediction of DNA methylation and accessibility reveals a sequence-driven bimodal state mediated by methylation-sensitive transcription factors. Beyond static single-molecule snapshots, the S2d framework can generate pseudo-dynamic trajectories of chromatin states and propose a roadmap toward genome-wide chromatin dynamics. Moreover, we showed that Mola can predict and interpret the effects of genetic variants at the single-molecule level, revealing how sequence changes reshape distal regulatory coordination. Together, this sequence-to-distribution framework lays the foundation for understanding how genomic sequence encodes stochastic and dynamic genome regulation, and how genetic variation changes it, one molecule at a time.",
  "abstract_shown": true,
  "affiliations": [
    "University Of Chicago"
  ],
  "article_type": "New results",
  "arxiv": null,
  "author_total": 4,
  "authors": [
    {
      "name": "R. Wang"
    },
    {
      "name": "J. Jin"
    },
    {
      "name": "S. Pott"
    },
    {
      "affs": [
        0
      ],
      "name": "J. Zhou"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/d9hrrhd9jzg87.bib",
  "cited_by": 0,
  "doi": "10.64898/2026.10.29.762992",
  "fulltext_url": "https://www.biorxiv.org/content/10.64898/2026.10.29.762992v1.full",
  "id": "d9hrrhd9jzg87",
  "issns": [],
  "landing_url": "https://www.biorxiv.org/content/10.64898/2026.10.29.762992v1",
  "language": null,
  "licence": "CC-BY-NC-ND",
  "licence_label": "CC BY-NC-ND",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by-nc-nd/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "biorxiv",
      "genomics"
    ]
  ],
  "openalex": null,
  "pdf_url": "https://www.biorxiv.org/content/10.64898/2026.10.29.762992v1.full.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-29",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/d9hrrhd9jzg87.ris",
  "sources": [
    {
      "first_seen": "2026-11-05T00:21:43Z",
      "native_id": "10.64898/2026.10.29.762992",
      "source": "biorxiv",
      "url": "https://www.biorxiv.org/content/10.64898/2026.10.29.762992v1"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "biorxiv:genomics",
      "subfield": "genetics-genomics"
    }
  ],
  "title": "Predicting single-molecule chromatin distribution from DNA sequence",
  "title_text": "Predicting single-molecule chromatin distribution from DNA sequence",
  "type": "preprint",
  "updated_at": "2026-11-06T00:21:32Z",
  "url": "https://research.draconitic.com/paper/d9hrrhd9jzg87",
  "venue": "bioRxiv",
  "versions": []
}