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yMGV: helping biologists with yeast microarray data mining.

Stéphane Le Crom1, Frédéric Devaux, Claude Jacq

  • 1Laboratoire de Génétique Moléculaire, CNRS UMR8541, Ecole Normale Supérieure, 46 Rue d'Ulm, 75005 Paris, France.

Nucleic Acids Research
|December 26, 2001
PubMed
Summary

The yeast Microarray Global Viewer (yMGV) database offers a comprehensive resource for analyzing genome-wide yeast expression data. It enables biologists to visualize and compare gene expression profiles across multiple experiments, aiding in the discovery of condition-specific patterns and functional gene clusters.

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Area of Science:

  • Genomics
  • Bioinformatics
  • Molecular Biology

Background:

  • Genome-wide expression data from yeast microarrays is rapidly accumulating.
  • Analyzing this data requires integrated tools for visualization and comparison.
  • Existing resources may lack comprehensive integration of diverse experimental results.

Purpose of the Study:

  • To develop a centralized database and visualization tool for yeast microarray data.
  • To enable comparative analysis of gene expression profiles across different studies.
  • To facilitate the identification of condition-specific gene expression patterns and functional gene clusters.

Main Methods:

  • Compilation of published yeast microarray datasets spanning four years.
  • Development of customizable tools for rapid visualization of gene expression profiles.

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  • Implementation of comparative analysis features for cross-publication data assessment.

    • Main Results:

    • The yeast Microarray Global Viewer (yMGV) database integrates a significant portion of available yeast microarray data.
    • yMGV provides tools for visualizing gene expression profiles and comparing results across experiments.
    • Global analyses using yMGV successfully identified condition-specific gene expression profiles and located functional gene clusters.

    Conclusions:

    • yMGV serves as a valuable resource for biologists studying yeast gene expression.
    • The database facilitates the discovery of novel biological insights through data integration and comparative analysis.
    • Future expansion to include data from other organisms is planned.