TRACE: transcription factor footprinting using chromatin accessibility data and DNA sequence

Ningxin Ouyang, Alan P Boyle
2020 Genome Research  
Transcription is tightly regulated by cis-regulatory DNA elements where transcription factors can bind. Thus, identification of transcription factor binding sites (TFBSs) is key to understanding gene expression and whole regulatory networks within a cell. The standard approaches used for TFBS prediction, such as position weight matrices (PWMs) and chromatin immunoprecipitation followed by sequencing (ChIP-seq), are widely used, but have their drawbacks including high false positive rates and
more » ... ited antibody availability, respectively. Several computational footprinting algorithms have been developed to detect TFBSs by investigating chromatin accessibility patterns, however these also have limitations. We have developed a footprinting method to predict Transcription factor footpRints in Active Chromatin Elements (TRACE) to improve the prediction of TFBS footprints. TRACE incorporates DNase-seq data and PWMs within a multivariate Hidden Markov Model (HMM) to detect footprint-like regions with matching motifs. TRACE is an unsupervised method that accurately annotates binding sites for specific TFs automatically with no requirement for pre-generated candidate binding sites or ChIP-seq training data. Compared to published footprinting algorithms, TRACE has the best overall performance with the distinct advantage of targeting multiple motifs in a single model.
doi:10.1101/gr.258228.119 pmid:32660981 pmcid:PMC7397869 fatcat:duzc7zeefbewris7ndyokcaz3q