Model-based Analysis of Regulation of Gene Expression (MARGE) is a framework for interpreting the relationship between the H3K27ac chromatin environment and differentially expressed gene sets. The framework has three main functions: MARGE-potential, MARGE-express, and MARGE-cistrome. MARGE-potential defines a regulatory potential (RP) for each gene as the sum of H3K27ac ChIP-seq signals weighted by a function of genomic distance from the transcription start site. The MARGE framework includes a compendium of RPs derived from 365 human and 267 mouse H3K27ac ChIP-seq datasets. Relative RPs, scaled using this compendium, are superior to super-enhancers in predicting BET (bromodomain and extra-terminal domain) -inhibitor repressed genes. MARGE-express, which uses logistic regression to retrieve relevant H3K27ac profiles from the compendium to accurately model a query set of differentially expressed genes, was tested on 671 diverse gene sets from MSigDB. MARGE-cistrome adopts a novel semi-supervised learning approach to identify cis-regulatory elements regulating a gene set. MARGE-cistrome exploits information from H3K27ac signal at DNase I hypersensitive sites identified from published human and mouse DNase-seq data derived from diverse cell types. We tested the framework on newly generated RNA-seq and H3K27ac ChIP-seq profiles upon siRNA silencing of multiple transcriptional and epigenetic regulators in a prostate cancer cell line, LNCaP-abl. MARGE-cistrome can predict the binding sites of silenced transcription factors without matched H3K27ac ChIP-seq data. Even when the matching H3K27ac ChIP-seq profiles are available, MARGE leverages public H3K27ac profiles to enhance this data. This study demonstrates the advantage of integrating a large compendium of historical epigenetic data for genomic studies of transcriptional regulation.
Genome research. 2016 Jul 27 [Epub ahead of print]
Su Wang, Chongzhi Zang, Tengfei Xiao, Jingyu Fan, Shenglin Mei, Qian Qin, Qiu Wu, Xujuan Li Li, Kexin Xu, Housheng Hansen He, Myles Brown, Clifford A Meyer, Xiaole Shirley Liu
Tongji University;, Harvard School of Public Health and Dana-Farber Cancer Institute;, Harvard School of Public Health and Dana-Farber Cancer Institute;, Tongji University;, Tongji University;, Tongji University;, Tongji University;, Tongji University;, The University of Texas Health Science Center at San Antonio Texas;, University of Toronto;, Dana-Farber Cancer Institute., Harvard School of Public Health and Dana-Farber Cancer Institute; ., Harvard School of Public Health and Dana-Farber Cancer Institute;