Published December 19, 2019 | Version 1.0
Journal article Open

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

Creators

  • 1. Shanghai Key Laboratory of Tuberculosis, Clinical Translational Research Center, Shanghai Pulmonary Hospital, School of Life Sciences and Technology, Tongji University; Children's hospital of Fudan University, Center of Molecular Medicine
  • 2. Shanghai Key Laboratory of Tuberculosis, Clinical Translational Research Center, Shanghai Pulmonary Hospital, School of Life Sciences and Technology, Tongji University
  • 3. School of Life Science and Technology, Tongji University; Stem Cell Translational Research Center, Tongji Hospital, School of Life Science and Technology, Tongji University
  • 4. Center for Functional Cancer Epigenetics, Dana-Farber Cancer Institute; Department of Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical School
  • 5. Center for Functional Cancer Epigenetics, Dana-Farber Cancer Institute; Department of Data Sciences, Dana-Farber Cancer Institute and Harvard T.H. Chan School of Public Health

Description

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.

Notes

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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Preprint: 10.1101/846139 (Handle)