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Base-resolution models of transcription-factor binding reveal soft motif syntax
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Journal Article
Base-resolution models of transcription-factor binding reveal soft motif syntax
2021
Overview
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.
BPNet is an interpretable deep learning tool that predicts transcription-factor binding profiles from DNA sequence at base-pair resolution, enabling the identification of motifs and the regulatory syntax underlying transcription-factor binding.
Publisher
Nature Publishing Group US,Nature Publishing Group
Subject
/ 45/15
/ 45/23
/ 45/70
/ 631/114
/ Animal Genetics and Genomics
/ Animals
/ Binding
/ Biomedical and Life Sciences
/ Chromatin Immunoprecipitation
/ Clustered Regularly Interspaced Short Palindromic Repeats
/ Computational Biology - methods
/ CRISPR
/ DNA
/ Genomes
/ Genomics
/ Mice
/ Mouse Embryonic Stem Cells - physiology
/ Mutation
/ Nanog Homeobox Protein - metabolism
/ Octamer Transcription Factor-3 - metabolism
/ Proteins
/ SOXB1 Transcription Factors - metabolism
/ Syntax
