Deep neural networks identify sequence context features predictive of transcription factor binding.

ABSTRACT: Transcription factors (TFs) bind DNA by recognizing specific sequence motifs, typically of length 6-12bp. A motif can occur many thousands of times in the human genome, but only a subset of those sites are actually bound. Here we present a machine learning framework leveraging existing convolutional neural network architectures and model interpretation techniques to identify and interpret sequence context features most important for predicting whether a particular motif instance will be bound. We apply our framework to predict binding at motifs for 38 TFs in a lymphoblastoid cell line, score the importance of context sequences at base-pair resolution, and characterize context features most predictive of binding. We find that the choice of training data heavily influences classification acc

SUBMITTER: Zheng A 

PROVIDER: S-EPMC8009085 | biostudies-literature | 2021 Feb

REPOSITORIES: biostudies-literature