Journal article Open Access

Lisa: inferring transcriptional regulators through integrative modeling of public chromatin accessibility and ChIP-seq data

Qian Qin; Jingyu Fan; Rongbin Zheng; Changxin Wan; Shenglin Mei; Qiu Wu; Hanfei Sun; Jing Zhang; Myles Brown; Clifford A. Meyer; X. Shirley Liu

We develop Lisa (epigenetic Landscape In silico Sequence Analysis) to predict transcriptional regulators (TRs) of differentially expressed or co-expressed gene sets. Based on input gene sets, Lisa first uses histone mark ChIP-seq and chromatin accessibility profiles to construct a chromatin model related to the regulation of these genes. Using TR ChIP-seq peaks or imputed TR binding sites, Lisa probes the chromatin models using in silico deletion to find the most relevant TRs. Applied to gene sets derived from targeted TF perturbation experiments, Lisa boosts the performance of imputed TR cistromes, and outperforms alternative methods in identifying the perturbed TRs.

This work was supported by grants from the NIH (U24 HG009446 to XLS, U24 CA237617 to XSL and CAM), National Natural Science Foundation of China (31801110 to S.M.) and the Shanghai Sailing Program (18YF1402500 to QQ).
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