Base-resolution models of transcription-factor binding reveal soft motif syntax
Avsec Z, Weilert M, Shrikumar A, Krueger S, Alexandari A, Dalal K, Fropf R, Mcanany C, Gagneur J, Kundaje A, Zeitlinger J (2021)
Publication Type: Journal article
Publication year: 2021
Journal
Book Volume: 53
Pages Range: 354-366
Journal Issue: 3
DOI: 10.1038/s41588-021-00782-6
Abstract
The arrangement (syntax) of transcription factor (TF) binding motifs is an important part of the cis-regulatory code, yet remains elusive. We introduce a deep learning model, BPNet, that uses DNA sequence to predict base-resolution chromatin immunoprecipitation (ChIP)–nexus binding profiles of pluripotency TFs. We develop interpretation tools to learn predictive motif representations and identify soft syntax rules for cooperative TF binding interactions. Strikingly, Nanog preferentially binds with helical periodicity, and TFs often cooperate in a directional manner, which we validate using clustered regularly interspaced short palindromic repeat (CRISPR)-induced point mutations. Our model represents a powerful general approach to uncover the motifs and syntax of cis-regulatory sequences in genomics data.
Involved external institutions
Technische Universität München (TUM)
Germany (DE)
Stowers Institute for Medical Research
United States (USA) (US)
Stanford University
United States (USA) (US)
Germany (DE)
Stowers Institute for Medical Research
United States (USA) (US)
Stanford University
United States (USA) (US)
How to cite
APA:
Avsec, Z., Weilert, M., Shrikumar, A., Krueger, S., Alexandari, A., Dalal, K.,... Zeitlinger, J. (2021). Base-resolution models of transcription-factor binding reveal soft motif syntax. Nature Genetics, 53(3), 354-366. https://doi.org/10.1038/s41588-021-00782-6
MLA:
Avsec, Ziga, et al. "Base-resolution models of transcription-factor binding reveal soft motif syntax." Nature Genetics 53.3 (2021): 354-366.
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