SeqGL Identifies Context-Dependent Binding Signals in Genome-Wide Regulatory Element Maps

Manu Setty, Christina S. Leslie

Abstract: Author Summary Transcriptional regulation is the cell’s primary mode of controlling gene expression. Transcription factors (TFs) are proteins that recognize and bind specific DNA sequence signals to regulate the expression of target genes. Recent years have seen the rapid development of genome-wide assays to profile the binding locations of a single TF or, more generally, regions of open chromatin that are occupied by a complex repertoire of DNA binding factors. New methods are therefore needed to detect and represent DNA sequence signals in these genome-wide regulatory element maps. Here we present a novel tool called SeqGL to extract multiple TF binding signals from genome-wide maps. SeqGL employs a machine learning framework to identify features that best discriminate the peaks, where we expect DNA sequence signals to occur, from the flank regions that should not contain these signals. Our tool performed significantly better than widely used motif discovery methods in discriminative accuracy and achieved higher sensitivity in detecting the numerous sequence signals underlying regulatory element maps.