AffiliationUniv Arizona, Dept Cellular & Mol Med
MetadataShow full item record
PublisherOXFORD UNIV PRESS
CitationVerena M Link, Casey E Romanoski, Dirk Metzler, Christopher K Glass; MMARGE: Motif Mutation Analysis for Regulatory Genomic Elements, Nucleic Acids Research, Volume 46, Issue 14, 21 August 2018, Pages 7006–7021, https://doi.org/10.1093/nar/gky491
JournalNUCLEIC ACIDS RESEARCH
Rights© The Author(s) 2018. Published by Oxford University Press on behalf of Nucleic Acids Research. This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License.
Collection InformationThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at firstname.lastname@example.org.
AbstractCell-specific patterns of gene expression are determined by combinatorial actions of sequence specific transcription factors at cis-regulatory elements. Studies indicate that relatively simple combinations of lineage-determining transcription factors (LDTFs) play dominant roles in the selection of enhancers that establish cell identities and functions. LDTFs require collaborative interactions with additional transcription factors to mediate enhancer function, but the identities of these factors are often unknown. We have shown that natural genetic variation between individuals has great utility for discovering collaborative transcription factors. Here, we introduce MMARGE (Motif Mutation Analysis of Regulatory Genomic Elements), the first publicly available suite of software tools that integrates genome-wide genetic variation with epigenetic data to identify collaborative transcription factor pairs. MMARGE is optimized to work with chromatin accessibility assays (such as ATAC-seq or DNase I hypersensitivity), as well as transcription factor binding data collected by ChIP-seq. Herein, we provide investigators with rationale for each step in the MMARGE pipeline and key differences for analysis of datasets with different experimental designs. We demonstrate the utility of MMARGE using mouse peritoneal macrophages, liver cells, and human lymphoblastoid cells. MMARGE provides a powerful tool to identify combinations of cell type-specific transcription factors while simultaneously interpreting functional effects of non-coding genetic variation.
NoteOpen access journal.
VersionFinal published version
SponsorsNIH [CA173903, GM085764, DK091183]; NIH-NHLBI [R00123485]