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Using SCOPE to Identify Potential Regulatory Motifs in Coregulated Genes
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MMARGE: Motif Mutation Analysis for Regulatory Genomic Elements.

Verena M Link1,2, Casey E Romanoski3, Dirk Metzler2

  • 1Department of Cellular and Molecular Medicine, University of California, San Diego, San Diego, USA.

Nucleic Acids Research
|June 13, 2018
PubMed
Summary
This summary is machine-generated.

MMARGE software identifies collaborative transcription factor pairs by integrating genetic variation with epigenetic data. This tool aids in understanding cell-specific gene expression and the functional impact of non-coding genetic variations.

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Area of Science:

  • Genomics
  • Molecular Biology
  • Bioinformatics

Background:

  • Cell-specific gene expression relies on transcription factors binding to cis-regulatory elements.
  • Lineage-determining transcription factors (LDTFs) are crucial for cell identity, often requiring collaboration with other factors.
  • Identifying these collaborative transcription factors is essential but challenging.

Purpose of the Study:

  • To introduce MMARGE (Motif Mutation Analysis of Regulatory Genomic Elements), a novel software suite.
  • To enable the identification of collaborative transcription factor pairs using genetic variation and epigenetic data.
  • To provide a tool for interpreting the functional effects of non-coding genetic variation.

Main Methods:

  • Integration of genome-wide genetic variation data with epigenetic data (e.g., ATAC-seq, DNase I, ChIP-seq).
  • Development of the MMARGE software pipeline for analyzing regulatory genomic elements.
  • Application of MMARGE to mouse peritoneal macrophages, liver cells, and human lymphoblastoid cells.

Main Results:

  • MMARGE successfully identifies collaborative transcription factor pairs.
  • The software demonstrates utility across different cell types and experimental designs.
  • MMARGE facilitates the interpretation of how genetic variation impacts gene regulation.

Conclusions:

  • MMARGE is a powerful, publicly available tool for discovering cell type-specific transcription factor combinations.
  • The software aids in understanding the functional consequences of non-coding genetic variation on gene expression.
  • MMARGE advances the study of gene regulation by linking genetic variation to transcription factor interactions.