title: Modeling cis-regulation with a compendium of genome-wide histone H3K27ac profiles.
abstract: 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 data sets. Relative RPs, scaled using this compendium, are superior to superenhancers in predicting BET (bromodomain and extraterminal 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 semisupervised 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. 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 these data. This study demonstrates the advantage of integrating a large compendium of historical epigenetic data for genomic studies of transcriptional regulation.
authors.affiliation: Shanghai Key Laboratory of Tuberculosis, Shanghai Pulmonary Hospital, Shanghai, 200433, China; Department of Bioinformatics, School of Life Science and Technology, Tongji University, Shanghai, 200092, China.
authors.forename: Su
authors.initials: S
authors.lastname: Wang
authors.affiliation: Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute and Harvard T.H. Chan School of Public Health, Boston, Massachusetts 02215, USA; Center for Functional Cancer Epigenetics, Dana-Farber Cancer Institute, Boston, Massachusetts 02215, USA.
authors.forename: Chongzhi
authors.initials: C
authors.lastname: Zang
authors.affiliation: Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute and Harvard T.H. Chan School of Public Health, Boston, Massachusetts 02215, USA; Center for Functional Cancer Epigenetics, Dana-Farber Cancer Institute, Boston, Massachusetts 02215, USA; Department of Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, Massachusetts 02215, USA.
authors.forename: Tengfei
authors.initials: T
authors.lastname: Xiao
authors.affiliation: Department of Bioinformatics, School of Life Science and Technology, Tongji University, Shanghai, 200092, China.
authors.forename: Jingyu
authors.initials: J
authors.lastname: Fan
authors.affiliation: Department of Bioinformatics, School of Life Science and Technology, Tongji University, Shanghai, 200092, China.
authors.forename: Shenglin
authors.initials: S
authors.lastname: Mei
authors.affiliation: Department of Bioinformatics, School of Life Science and Technology, Tongji University, Shanghai, 200092, China.
authors.forename: Qian
authors.initials: Q
authors.lastname: Qin
authors.affiliation: Department of Bioinformatics, School of Life Science and Technology, Tongji University, Shanghai, 200092, China.
authors.forename: Qiu
authors.initials: Q
authors.lastname: Wu
authors.affiliation: Department of Bioinformatics, School of Life Science and Technology, Tongji University, Shanghai, 200092, China.
authors.forename: Xujuan
authors.initials: X
authors.lastname: Li
authors.affiliation: Department of Molecular Medicine/Institute of Biotechnology, The University of Texas Health Science Center at San Antonio, San Antonio, Texas 78229-3900, USA.
authors.forename: Kexin
authors.initials: K
authors.lastname: Xu
authors.affiliation: Department of Medical Biophysics, University of Toronto, Toronto, Ontario, M5G 1L7, Canada.
authors.forename: Housheng Hansen
authors.initials: HH
authors.lastname: He
authors.affiliation: Center for Functional Cancer Epigenetics, Dana-Farber Cancer Institute, Boston, Massachusetts 02215, USA; Department of Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, Massachusetts 02215, USA.
authors.forename: Myles
authors.initials: M
authors.lastname: Brown
authors.affiliation: Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute and Harvard T.H. Chan School of Public Health, Boston, Massachusetts 02215, USA; Center for Functional Cancer Epigenetics, Dana-Farber Cancer Institute, Boston, Massachusetts 02215, USA.
authors.forename: Clifford A
authors.initials: CA
authors.lastname: Meyer
authors.affiliation: Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute and Harvard T.H. Chan School of Public Health, Boston, Massachusetts 02215, USA; Center for Functional Cancer Epigenetics, Dana-Farber Cancer Institute, Boston, Massachusetts 02215, USA.
authors.forename: X Shirley
authors.initials: XS
authors.lastname: Liu
mesh_terms: D000107:Acetylation; D000818:Animals; D045744:Cell Line, Tumor; D044127:Epigenesis, Genetic; D015894:Genome, Human; D042421:Histone Code; D006657:Histones / Q000235:genetics / Q000378:metabolism*; D006801:Humans; D051379:Mice; D008957:Models, Genetic
chemical_list: D006657:Histones