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scMEGA: single-cell multi-omic enhancer-based gene regulatory network inference.

Zhijian Li1, James S Nagai1, Christoph Kuppe2,3

  • 1Institute for Computational Genomics, Joint Research Center for Computational Biomedicine, RWTH Aachen University Medical School, Aachen 52062, Germany.

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|January 26, 2023
PubMed
Summary
This summary is machine-generated.

scMEGA enables end-to-end analysis of single-cell multi-omics data for gene regulatory network inference. This tool aids in understanding complex gene regulation in dynamic biological processes like cellular differentiation and disease.

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Area of Science:

  • Genomics
  • Computational Biology
  • Systems Biology

Background:

  • Single-cell multi-omics data is increasingly available for studying gene regulation.
  • Characterizing gene regulatory networks is crucial for understanding dynamic biological processes.

Purpose of the Study:

  • To introduce scMEGA (Single-cell Multiomic Enhancer-based Gene Regulatory Network Inference), a tool for end-to-end analysis of multi-omics data.
  • To enable quantitative characterization of gene regulation and inference of gene regulatory networks.

Main Methods:

  • scMEGA integrates multi-omics data, performs trajectory analysis, and predicts enhancer-to-promoter associations.
  • It includes network analysis and visualization capabilities.
  • The tool is implemented in R.

Main Results:

  • scMEGA facilitates the study of complex gene regulatory mechanisms in dynamic biological processes.
  • A case study demonstrates its application to gene regulatory networks in human myocardial infarction.

Conclusions:

  • scMEGA provides a comprehensive framework for multi-omics data analysis in gene regulatory network inference.
  • It is a valuable resource for studying cellular differentiation, disease-driven remodeling, and specific biological contexts like myocardial infarction.