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Promoter Capture Hi-C: High-resolution, Genome-wide Profiling of Promoter Interactions
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Capturing context-specific regulation in molecular interaction networks.

Stephen T A Rush1, Dirk Repsilber2

  • 1School of Medical Sciences, Örebro University, Södra Grev Rosengatan, Örebro, Sweden.

BMC Bioinformatics
|December 23, 2018
PubMed
Summary
This summary is machine-generated.

Coherent Network Expression (CoNE) identifies functional modules by analyzing gene expression changes within molecular networks. This approach enhances the specificity of detecting real biological findings.

Keywords:
Activated subnetworkCoherent differential expressionDifferential regulationError controlFunctional moduleMolecular network

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Area of Science:

  • Systems biology
  • Bioinformatics
  • Genomics

Background:

  • Gene expression profiles dynamically change with biological perturbations.
  • Coordinated gene expression changes form functional modules governed by regulatory interactions.
  • Differential expression analysis can be refined by integrating prior knowledge of regulatory networks.

Purpose of the Study:

  • To introduce Coherent Network Expression (CoNE), a method for identifying differentially activated functional modules.
  • To enhance the precision and mechanistic understanding of differential regulatory signals.
  • To provide a robust computational tool for network-based differential expression analysis.

Main Methods:

  • Developed Coherent Network Expression (CoNE) algorithm.
  • Applied CoNE to simulated data for performance benchmarking against alternative methods.
  • Utilized CoNE on a transcription regulatory network associated with particle-induced pulmonary inflammation.

Main Results:

  • CoNE effectively identifies differentially activated functional modules in molecular networks.
  • The method demonstrates superior performance compared to existing approaches on simulated data.
  • Analysis of pulmonary inflammation data recapitulated known results and identified novel candidate regulatory interactions.

Conclusions:

  • Integrating coherent interactions with differential gene expression error control significantly improves inference specificity.
  • CoNE ensures that identified functional modules represent genuine biological findings.
  • The CoNE R-package offers a practical tool for researchers in systems biology and bioinformatics.