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Related Experiment Videos

EST databases as multi-conditional gene expression datasets.

R M Ewing1, J M Claverie

  • 1Carnegie Institution of Washington, Department of Plant Biology, Stanford, California 94305, USA. ewing@genome.stanford.edu

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
|July 21, 2000
PubMed
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This study validates using expressed sequence tag (EST) digital expression profiles to cluster genes. This method effectively identifies genes in similar pathways and aids in predicting orthologs across species.

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Gene Expression Analysis

Background:

  • Large-scale gene expression data offers insights into gene function and regulation.
  • Clustering genes by expression profiles can reveal genes in shared pathways or regulatory networks.
  • Publicly available expressed sequence tag (EST) collections represent an underutilized data source.

Purpose of the Study:

  • To validate the use of digital expression profiles derived from ESTs for gene clustering.
  • To assess the utility of this approach in identifying functionally related genes.
  • To explore the potential for cross-species ortholog prediction using EST data.

Main Methods:

  • Generation of digital expression profiles by counting EST tag frequencies from various cDNA libraries.

Related Experiment Videos

  • Application of statistical tests to associate genes and cDNA libraries with similar expression profiles.
  • Validation using larger EST datasets from UniGene projects for human, mouse, and rat.

    • Main Results:

    • Genes clustered based on digital expression profiles often participate in similar biological pathways.
    • Clustering can group genes encoding subunits of the same multi-component enzyme complexes.
    • The approach demonstrated effectiveness across different species (human, mouse, rat).

    Conclusions:

    • Digital expression profiling of ESTs is a valid method for inferring gene function and relationships.
    • This approach facilitates the identification of co-regulated genes and pathway members.
    • Cross-species comparison of gene clusters can aid in ortholog confirmation and prediction.