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RelExplain-integrating data and networks to explain biological processes.

Evi Berchtold1, Gergely Csaba1, Ralf Zimmer1

  • 1Department of Informatics, Institute of Bioinformatics, Ludwig-Maximilians-Universität München, Amalienstraße 17, München, Germany.

Bioinformatics (Oxford, England)
|February 7, 2017
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Summary

RelExplain provides a novel method to analyze biological processes by constructing gene interaction networks. This approach enhances understanding of differential gene functions and interactions within specific biological contexts.

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Area of Science:

  • Genomics
  • Systems Biology
  • Bioinformatics

Background:

  • Genome-wide experiments aim to elucidate condition-specific changes.
  • Current methods identify differential genes (DG) and biological processes (BP) but lack detailed interaction insights.
  • Existing network methods identify gene subnetworks but do not integrate process knowledge for focused analysis.

Purpose of the Study:

  • To develop a method, RelExplain, for detailed analysis of biological processes using differential genes and interaction networks.
  • To compute network explanations that connect differential genes within a biological process, incorporating non-differential genes.

Main Methods:

  • RelExplain computes subnetworks explaining a biological process (bp) using measured differential genes (DG) and a gene interaction network.
  • The method considers functional annotations and edge consistency of experimental data.
  • An interactive tool allows computation and inspection of optimal and sub-optimal explanations.

Main Results:

  • RelExplain generates compact networks highlighting relevant genes and their interactions for a biological process.
  • Evaluations demonstrate RelExplain's superior ability to retrieve known biological subnetworks compared to other algorithms.
  • The tool facilitates the discovery of potentially important genes beyond the initial differential gene set.

Conclusions:

  • RelExplain offers a powerful approach to dissect biological processes by integrating differential gene expression data with network information.
  • The method provides deeper insights into gene interactions and functional relationships within specific biological contexts.
  • The interactive tool supports detailed exploration of biological pathways and regulatory mechanisms.