{
  "abstract": "Sequence-to-function models predict genomic tracks from DNA sequence. AlphaGenome has been shown to outperform previous models, including Borzoi, on most track-prediction and variant-effect benchmarks. However, like its predecessors, it predicts tracks only for predefined, mostly bulk-derived cell types and tissues. While Scooby has extended Borzoi to single-cell resolution, a single-cell adaptation of AlphaGenome is still lacking. We present Scooby-2, which combines AlphaGenome with a cell-specific decoder to predict single-cell RNA-seq coverage and chromatin-accessibility genomic tracks from DNA sequence. The package provides the implementation, the parameter-efficient fine-tuning setup, the training pipeline and inference scripts. When evaluated on the NeurIPS bone marrow single-cell dataset, Scooby-2 achieved a Pearson correlation of 0.56 when predicting cell-type-specific gene expression differences, compared with 0.53 for Scooby. When using the distilled AlphaGenome backbone, variant effect prediction concordance of fine-mapped eQTLs from the OneK1K cohort improved from 0.37 to 0.47 Spearman's rank correlation across all associations, an improvement consistent with that of AlphaGenome over Borzoi itself.",
  "abstract_shown": true,
  "affiliations": [
    "Technical University of Munich"
  ],
  "article_type": "New results",
  "arxiv": null,
  "author_total": 2,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "J. C. Hingerl"
    },
    {
      "name": "J. Gagneur"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/4j0da8h2fys09.bib",
  "cited_by": 0,
  "doi": "10.64898/2026.10.28.762238",
  "fulltext_url": "https://www.biorxiv.org/content/10.64898/2026.10.28.762238v1.full",
  "id": "4j0da8h2fys09",
  "issns": [],
  "landing_url": "https://www.biorxiv.org/content/10.64898/2026.10.28.762238v1",
  "language": null,
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "biorxiv",
      "bioinformatics"
    ]
  ],
  "openalex": null,
  "pdf_url": "https://www.biorxiv.org/content/10.64898/2026.10.28.762238v1.full.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-28",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/4j0da8h2fys09.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:30:26Z",
      "native_id": "10.64898/2026.10.28.762238",
      "source": "biorxiv",
      "url": "https://www.biorxiv.org/content/10.64898/2026.10.28.762238v1"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "biorxiv:bioinformatics",
      "subfield": "bioinformatics"
    }
  ],
  "title": "Scooby-2: fine-tuning AlphaGenome for single-cell genomic track prediction",
  "title_text": "Scooby-2: fine-tuning AlphaGenome for single-cell genomic track prediction",
  "type": "preprint",
  "updated_at": "2026-10-31T00:20:46Z",
  "url": "https://research.draconitic.com/paper/4j0da8h2fys09",
  "venue": "bioRxiv",
  "versions": []
}