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

Paola Cornejo-Páramo, Xuan Zhang, Lithin Louis, Zelun Li, Yihua Yang and Emily S. Wong ()
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Paola Cornejo-Páramo: Victor Chang Cardiac Research Institute
Xuan Zhang: Victor Chang Cardiac Research Institute
Lithin Louis: Victor Chang Cardiac Research Institute
Zelun Li: Victor Chang Cardiac Research Institute
Yihua Yang: Victor Chang Cardiac Research Institute
Emily S. Wong: Victor Chang Cardiac Research Institute

Nature Communications, 2025, vol. 16, issue 1, 1-15

Abstract: Abstract 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.

Date: 2025
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DOI: 10.1038/s41467-025-65362-2

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