{
  "abstract": "Super-enhancers (SEs) drive cell-identity gene expression, yet their identification relies on epigenomic profiling (e.g., H3K27ac ChIP-seq), precluding discovery in unprofiled cellular states and leaving their underlying sequence grammar unknown. Here, we present Borzoi-SE, a deep learning framework built upon the genomic transformer Borzoi that formulates SE discovery as hierarchical, cell-type-resolved genomic object detection directly from long-range sequence across 90 human lineages. Base-resolution attribution recovers lineage-defining master transcription factors across cell types. Within SEs shared across lineages, it reveals lineage-dependent motif usage, suggesting that the same regulatory regions can support distinct cell-type-specific programs. Using in silico ablation, Borzoi-SE decoded the functional hierarchy of the {beta}-globin super-enhancer, prioritizing hypersensitive site 2 (HS2) as the dominant constituent, which we experimentally verified via CRISPR interference. Trained without variant-effect labels, Borzoi-SE identified gain-of-super-enhancer variants in different tissues. At the CHRM2 locus, a single-nucleotide variant created a de novo glucocorticoid receptor (GR) motif that recruited activated GR and potentiated adjacent MEF2 regulatory syntax, nucleating a cardiac super-enhancer. Orthogonal multi-omics and population-genetic evidence linked this variant to increased CHRM2 expression and variation in resting heart rate and PR-interval duration. Borzoi-SE establishes a general framework for decoding SE regulatory grammar and prioritizing causal non-coding variants directly from sequence.",
  "abstract_shown": true,
  "affiliations": [
    "Liangzhu Laboratory, Zhejiang University School of Medicine"
  ],
  "article_type": "New results",
  "arxiv": null,
  "author_total": 5,
  "authors": [
    {
      "name": "Y. Liang"
    },
    {
      "name": "X. Xu"
    },
    {
      "name": "Y. Mao"
    },
    {
      "name": "Z. Zheng"
    },
    {
      "affs": [
        0
      ],
      "name": "N. Liu"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/7v9kr2vm9wh2m.bib",
  "cited_by": 0,
  "doi": "10.64898/2026.10.11.756618",
  "fulltext_url": "https://www.biorxiv.org/content/10.64898/2026.10.11.756618v1.full",
  "id": "7v9kr2vm9wh2m",
  "issns": [],
  "landing_url": "https://www.biorxiv.org/content/10.64898/2026.10.11.756618v1",
  "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",
      "1312|Genomics and Chromatin Dynamics"
    ]
  ],
  "openalex": "W7221755805",
  "pdf_url": "https://www.biorxiv.org/content/10.64898/2026.10.11.756618v1.full.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-11",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/7v9kr2vm9wh2m.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:27:42Z",
      "native_id": "10.64898/2026.10.11.756618",
      "source": "biorxiv",
      "url": "https://www.biorxiv.org/content/10.64898/2026.10.11.756618v1"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "biorxiv:genomics",
      "subfield": "genetics-genomics"
    }
  ],
  "title": "Decoding super-enhancer regulatory grammar and pathogenic gain-of-function variants directly from DNA sequence",
  "title_text": "Decoding super-enhancer regulatory grammar and pathogenic gain-of-function variants directly from DNA sequence",
  "type": "preprint",
  "updated_at": "2026-10-30T12:27:42Z",
  "url": "https://research.draconitic.com/paper/7v9kr2vm9wh2m",
  "venue": "bioRxiv",
  "versions": []
}