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Nucleic Acids Res. 2018 Aug 21;46(14):7006-7021. doi: 10.1093/nar/gky491.

MMARGE: Motif Mutation Analysis for Regulatory Genomic Elements.

Author information

1
Department of Cellular and Molecular Medicine, University of California, San Diego, San Diego, USA.
2
Division of Evolutionary Biology, Faculty of Biology, Ludwig-Maximilian Universität München, Planegg-Martinsried, Germany.
3
Department of Cellular and Molecular Medicine, University of Arizona, Tucson, USA.
4
Department of Medicine, University of California, San Diego, San Diego, USA.

Abstract

Cell-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.

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