TY  - JOUR
A1  - Ouyang, Ningxin
A1  - Boyle, Alan P.
T1  - TRACE: transcription factor footprinting using chromatin accessibility data and DNA sequence
Y1  - 2020/07/01 
JF  - Genome Research 
JO  - Genome Research 
SP  - 1040 
EP  - 1046 
DO  - 10.1101/gr.258228.119 
VL  - 30 
IS  - 7 
UR  - http://genome.cshlp.org/content/30/7/1040.abstract 
N2  - 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. 
ER  -