Discovery of a non-canonical GRHL1 binding site using deep convolutional and recurrent neural networks.

ABSTRACT:

Background

Transcription factors regulate gene expression by binding to transcription factor binding sites (TFBSs). Most models for predicting TFBSs are based on position weight matrices (PWMs), which require a specific motif to be present in the DNA sequence and do not consider interdependencies of nucleotides. Novel approaches such as Transcription Factor Flexible Models or recurrent neural networks consequently provide higher accuracies. However, it is unclear whether such approaches can uncover novel non-canonical, hitherto unexpected TFBSs relevant to human transcriptional regulation.

Results

In this study, we trained a convolutional recurrent neural network with HT-SELEX data for GRHL1 binding and applied it to a set of GRHL1 binding sites obtained from ChIP-Seq experi

SUBMITTER: Proft S 

PROVIDER: S-EPMC10696883 | biostudies-literature | 2023 Dec

REPOSITORIES: biostudies-literature