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CMGRN: a web server for constructing multilevel gene regulatory networks using ChIP-seq and gene expression data.

Daogang Guan1, Jiaofang Shao1, Youping Deng1

  • 1Department of Biology, Hong Kong Baptist University, Hong Kong, China, Department of Internal Medicine and Biochemistry, Rush University Medical Center, Chicago, IL, USA, Department of Biochemistry and HKU-SIRI, Centre for Genomic Sciences, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong, China and Key Laboratory of Acupuncture and Medicine Research of Minister of Education , Nanjing University of Chinese Medicine, Nanjing, Jiangsu 210046, China.

Bioinformatics (Oxford, England)
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Summary

CMGRN constructs multilevel gene regulatory networks by integrating ChIP-seq and gene expression data. This web server reveals hierarchical interactions between regulators and genes, aiding in understanding complex biological functions.

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Area of Science:

  • Genomics
  • Bioinformatics
  • Systems Biology

Background:

  • ChIP-seq technology accurately identifies DNA-binding sites for transcription factors and epigenetic modifiers.
  • Understanding the cross-interactions among multilevel regulators, genes, and biological functions requires integrating diverse high-throughput data.
  • Existing methods may not fully capture the hierarchical nature of gene regulatory networks.

Purpose of the Study:

  • To develop an integrative web server, CMGRN (constructing multilevel gene regulatory networks), for unraveling hierarchical gene regulatory networks.
  • To provide a user-friendly platform for dissecting complex interactions at different regulatory levels.
  • To enable the integration of ChIP-seq and gene expression data for comprehensive network reconstruction.

Main Methods:

  • Utilized Bayesian network modeling to infer causal relationships among transcription factors and epigenetic modifications using ChIP-seq data.
  • Employed a Bayesian hierarchical model with Gibbs sampling to integrate regulator binding signals and gene expression profiles.
  • Developed an integrative web server (CMGRN) accessible online.

Main Results:

  • Successfully reconstructed hierarchical gene regulatory networks by integrating heterogeneous high-throughput data.
  • Demonstrated the capability of CMGRN to reveal the 'regulome' and associated gene expression programs.
  • Provided effective web-based framework for analyzing complex regulatory interactions.

Conclusions:

  • CMGRN offers an effective computational framework for constructing multilevel gene regulatory networks.
  • The integration of ChIP-seq and gene expression data through CMGRN enhances the understanding of gene regulation.
  • CMGRN facilitates the discovery of hierarchical 'regulomes' and their functional implications.