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Related Experiment Videos

A search engine to identify pathway genes from expression data on multiple organisms.

Chunnuan Chen1, Matthew T Weirauch, Corey C Powell

  • 1Department of Biomolecular Engineering, University of California, Santa Cruz, California 95064, USA. cchen@soe.ucsc.edu

BMC Systems Biology
|May 5, 2007
PubMed
Summary
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A new search engine, the Multiple-Species Gene Recommender (MSGR), identifies genes in genetic pathways by analyzing gene expression data across multiple organisms. This tool helps infer functions for uncharacterized genes, aiding in biological discovery.

Area of Science:

  • Bioinformatics
  • Genomics
  • Systems Biology

Background:

  • Genome projects have identified numerous genes, particularly in multicellular organisms, with unknown functions.
  • Abundant gene expression data exists, offering insights into gene activity under various experimental conditions.
  • Co-regulation patterns across conditions can suggest functional roles for uncharacterized genes.

Purpose of the Study:

  • To develop a computational tool for identifying genes involved in specific genetic pathways.
  • To leverage cross-species gene expression data for inferring gene function and pathway membership.
  • To enhance the discovery of novel pathway components.

Main Methods:

  • Development of the Multiple-Species Gene Recommender (MSGR) search engine.

Related Experiment Videos

  • Input of query gene lists from six model organisms (Homo sapiens, Drosophila melanogaster, Caenorhabditis elegans, Saccharomyces cerevisiae, Arabidopsis thaliana, Helicobacter pylori).
  • Utilizing a probabilistic method to merge cross-species gene expression dataset searches and identify significant co-regulation.

    • Main Results:

    • The MSGR successfully identifies genes participating in genetic pathways by scanning multi-organism gene expression datasets.
    • Combining searches across species improves accuracy for identifying human pathway members.
    • Specific examples demonstrate the identification of novel genes in neuromuscular signaling and cell-adhesion pathways.

    Conclusions:

    • The MSGR effectively scans large-scale gene expression data to find novel genes co-regulated with known pathways.
    • Integrating cross-species analyses enables the discovery of pathway members with conserved or newly evolved co-regulation.
    • This approach aids in characterizing gene function and understanding complex biological pathways.