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Computational detection of cis -regulatory modules.

Stein Aerts1, Peter Van Loo, Gert Thijs

  • 1Department of Electrical Engineering ESAT-SCD, Katholieke Universiteit Leuven, Kasteelpark Arenberg 10, Leuven, Belgium. stein.aerts@esat.kuleuven.ac.be

Bioinformatics (Oxford, England)
|October 10, 2003
PubMed
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We developed novel algorithms, ModuleSearcher and ModuleScanner, to identify cis-regulatory modules (CRMs) in coexpressed genes. These tools predict gene regulatory switches by analyzing conserved DNA sequences, aiding in understanding gene regulation.

Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Gene transcription relies on transcription factors binding to cis-regulatory modules (CRMs).
  • CRMs integrate signals to control organism-specific gene expression spatially and temporally.

Purpose of the Study:

  • To develop a novel methodology for identifying CRMs in sets of coexpressed or coregulated genes.
  • To create algorithms that predict regulatory switches shared by genes with similar functions.

Main Methods:

  • Developed the ModuleSearcher algorithm using an A(*) tree search procedure to find optimal transcription factor binding site combinations within sequence windows.
  • Utilized conserved DNA regions between human and mouse orthologous genes to minimize noise and identify functional cis-regulatory information.

Related Experiment Videos

  • Developed the ModuleScanner for genomic searches using predicted or known CRMs to identify potential target genes.
  • Validated putative targets using Gene Ontology annotations.

    • Main Results:

    • Demonstrated the effectiveness, specificity, and sensitivity of ModuleSearcher and ModuleScanner on semi-artificial data.
    • Successfully applied the algorithms to identify a regulatory module within human cell cycle gene expression profiles.

    Conclusions:

    • The developed algorithms provide a powerful approach for discovering cis-regulatory modules and understanding gene regulatory networks.
    • This methodology aids in identifying genes with shared regulatory mechanisms, advancing the study of transcriptional regulation.