@article{Ouyang01072020,
author = {Ouyang, Ningxin and Boyle, Alan P.}, 
title = {TRACE: transcription factor footprinting using chromatin accessibility data and DNA sequence},
volume = {30}, 
number = {7}, 
pages = {1040-1046}, 
year = {2020}, 
doi = {10.1101/gr.258228.119}, 
abstract ={Transcription is tightly regulated by cis-regulatory DNA elements where transcription factors (TFs) can bind. Thus, identification of TF 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 limited 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 TF 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 pregenerated candidate binding sites or ChIP-seq training data. Compared with published footprinting algorithms, TRACE has the best overall performance with the distinct advantage of targeting multiple motifs in a single model.}, 
URL = {http://genome.cshlp.org/content/30/7/1040.abstract}, 
eprint = {http://genome.cshlp.org/content/30/7/1040.full.pdf+html}, 
journal = {Genome Research} 
}