Base-resolution models of transcription factor binding reveal soft motif syntax

Abstract

Summary The arrangement of transcription factor (TF) binding motifs (syntax) 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 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 CRISPR-induced point mutations. Our model represents a powerful general approach to uncover the motifs and syntax of cis-regulatory sequences in genomics data. Highlights The neural network BPNet accurately predicts TF binding data at base-resolution. Model interpretation discovers TF motifs and TF interactions dependent on soft syntax. Motifs for Nanog and partners are preferentially spaced at ∼10.5 bp periodicity. Directional cooperativity is validated: Sox2 enhances Nanog binding, but not vice versa.