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.

Enhancers and super-enhancers have an equivalent regulatory role in embryonic stem cells through regulation of single or multiple genes

“…Algorithm-based enhancer prediction models such as ChromHMM, ROSE, MARGE, and super-enhancer predictions claim to identify enhancers controlling cell identity (Ernst and Kellis 2012;Loven et al 2013;Whyte et al 2013;Wang et al 2016); however, the vast majority of these predictions remain functionally unexplored. This enhancer deletion study in ES cells revealed that, in some cases, transcriptional regulation of a single gene (Sall1, Tet1, Lifr, Jarid2) or gene cluster (Dppa5a/Ooep, Ifitm, and Six) was almost entirely dependent on the EC or isolated enhancer.…”

Inference of cell type specific regulatory networks on mammalian lineages

“…In all cases, a linear or logistic regression model learns feature weights, which can identify important cis -regulatory elements for cell type specific expression. When profiling multiple cell types using a single active mark (e.g., H3K27ac), one can use the MARGE framework [108] to identify a set of cis -regulatory elements associated with differentially expressed genes. However, these approaches do not infer regulatory connections for individual genes.…”

An introduction to computational tools for differential binding analysis with ChIP‐seq data

“…In particular, it usually gives more reliable results to perform ChIP-seq data analysis in a quantitative manner, especially for cross-condition comparisons [8,9,16]. For example, several recent studies suggested to quantitatively combine the ChIP-seq signal intensity of a peak, sometimes called peak height, as well as the distance from this peak to a candidate target gene to represent its regulatory potential to the gene [16][17][18]. It has been shown that such a quantitative measure can integrate with other observations to better infer the functional impact of the protein's chromatin binding being studied.…”