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<p>Deciphering how DNA sequence specifies cell-type-specific regulatory activity is a central challenge in gene regulation. We present Bag-of-Motifs (BOM), a computational framework that represents distal cis-regulatory elements as unordered counts of transcription factor (TF) motifs. This minimalist representation, combined with gradient-boosted trees, enables the accurate prediction of cell-type-specific enhancers across mouse, human, zebrafish, and <i>Arabidopsis</i> datasets. Despite its simplicity, BOM outperforms more complex deep-learning models while using fewer parameters. We validate BOM’s predictions experimentally by constructing synthetic enhancers from the most predictive motifs, demonstrating that these motif sets drive cell-type-specific expression. By providing direct interpretability and broad applicability, BOM reveals a highly predictive sequence code at distal regulatory regions and offers a scalable framework for dissecting cis-regulatory grammar across diverse species and conditions.</p>

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Motif-based models accurately predict cell type-specific distal regulatory elements

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年11月24日

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  • Paola Cornejo-Páramo,
  • Xuan Zhang,
  • Lithin Louis,
  • Zelun Li,
  • Yihua Yang,
  • Emily S. Wong

摘要

Deciphering how DNA sequence specifies cell-type-specific regulatory activity is a central challenge in gene regulation. We present Bag-of-Motifs (BOM), a computational framework that represents distal cis-regulatory elements as unordered counts of transcription factor (TF) motifs. This minimalist representation, combined with gradient-boosted trees, enables the accurate prediction of cell-type-specific enhancers across mouse, human, zebrafish, and Arabidopsis datasets. Despite its simplicity, BOM outperforms more complex deep-learning models while using fewer parameters. We validate BOM’s predictions experimentally by constructing synthetic enhancers from the most predictive motifs, demonstrating that these motif sets drive cell-type-specific expression. By providing direct interpretability and broad applicability, BOM reveals a highly predictive sequence code at distal regulatory regions and offers a scalable framework for dissecting cis-regulatory grammar across diverse species and conditions.