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Related Experiment Videos

GEPAS, an experiment-oriented pipeline for the analysis of microarray gene expression data.

Juan M Vaquerizas1, Lucía Conde, Patricio Yankilevich

  • 1Bioinformatics Unit, Centro Nacional de Investigaciones Oncológicas (CNIO) Melchor Fernández Almagro 3, 28029 Madrid, Spain.

Nucleic Acids Research
|June 28, 2005
PubMed
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The Gene Expression Profile Analysis Suite (GEPAS) is a robust platform for analyzing gene expression microarray data. It offers tools for differential gene expression, predictor building, and functional annotation, serving the biomedical community.

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • The Gene Expression Profile Analysis Suite (GEPAS) has been operational for over three years.
  • It analyzes over 76,000 experiments annually, averaging nearly 300 analyses daily.
  • GEPAS is a well-established platform for gene expression microarray data analysis.

Purpose of the Study:

  • To provide a comprehensive web-based pipeline for analyzing gene expression microarray data.
  • To meet the demands of the biomedical community for advanced data analysis tools.
  • To develop and implement efficient methods for functional annotation and predictor building.

Main Methods:

  • GEPAS implements clustering methods for gene expression data analysis.
  • It focuses on identifying differentially expressed genes and genes correlated with clinical outcomes.

Related Experiment Videos

  • The platform includes tools for comparative genomic hybridization array (CGH-array) analysis (InSilicoCGH) and functional annotation (FatiGO suite).

    • Main Results:

    • GEPAS supports the analysis of entire series of experiments.
    • It offers methods for building predictive models from gene expression data.
    • Functional annotation tools provide information on transcription factor binding sites, chromosomal locations, and tissue-specific expression.

    Conclusions:

    • GEPAS is a widely used and reliable platform for gene expression and CGH-array data analysis.
    • The suite facilitates in-depth functional annotation within a robust statistical framework.
    • Its continuous development is driven by the needs of the biomedical research community.