Scholarlink

<p>Sequence-to-function models that predict gene expression from genomic DNA sequence have proven valuable for many biological tasks, including understanding <i>cis</i>-regulatory syntax and interpreting noncoding genetic variation. However, current state-of-the-art models are trained largely on bulk expression profiles from healthy tissues or cell lines and have not learned the properties of precise cell types and states that are captured in large-scale single-cell transcriptomic datasets. Thus, they cannot perform these tasks at the resolution of specific cell types or states. Here we present Decima, a model that predicts the cell type- and condition-specific expression of a gene from its surrounding DNA sequence. Decima is trained on single-cell or single-nucleus RNA sequencing data from over 22 million cells and successfully predicts the cell-type-specific expression of unseen genes. We demonstrate Decima’s ability to reveal <i>cis</i>-regulatory mechanisms driving cell-type-specific gene expression and its changes in disease, predict noncoding-variant effects at cell type resolution and design context-specific regulatory DNA elements.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Decoding sequence determinants of gene expression in diverse cellular and disease states

  • 期刊论文

  • 发表日期：

    2026年5月25日

查看全文
  • Avantika Lal,
  • Alexander Karollus,
  • Laura Gunsalus,
  • David Garfield,
  • Surag Nair,
  • Alex M. Tseng,
  • M. Grace Gordon,
  • John Blischak,
  • Bryce Van De Geijn,
  • Tushar Bhangale,
  • Jenna L. Collier,
  • Nathaniel Diamant,
  • Tommaso Biancalani,
  • Hector Corrada Bravo,
  • Gabriele Scalia,
  • Gokcen Eraslan

摘要

Sequence-to-function models that predict gene expression from genomic DNA sequence have proven valuable for many biological tasks, including understanding cis-regulatory syntax and interpreting noncoding genetic variation. However, current state-of-the-art models are trained largely on bulk expression profiles from healthy tissues or cell lines and have not learned the properties of precise cell types and states that are captured in large-scale single-cell transcriptomic datasets. Thus, they cannot perform these tasks at the resolution of specific cell types or states. Here we present Decima, a model that predicts the cell type- and condition-specific expression of a gene from its surrounding DNA sequence. Decima is trained on single-cell or single-nucleus RNA sequencing data from over 22 million cells and successfully predicts the cell-type-specific expression of unseen genes. We demonstrate Decima’s ability to reveal cis-regulatory mechanisms driving cell-type-specific gene expression and its changes in disease, predict noncoding-variant effects at cell type resolution and design context-specific regulatory DNA elements.