: manipulating gene expression data for formation of hypotheses for plant signal transduction

  • Davina K. Button

    Student thesis: Doctoral Thesis


    DRASTIC (Database Resource for the Analysis of Signal Transduction in Cells) and the INSIGHTS (INference of cell Signalling HypoTheseS) web-based suite of tools bring together data on plant responses to pathogens, environmental stresses and chemicals from refereed journal publications. Presenting these data in a unified, searchable format allows the user to extract information beyond that obtained by the authors’ single genes, or clusters of similar expression patterns by browsing multiple treatments at once, identifying potential regulatory relationships between multiple treatments and genes. DRASTIC-INSIGHTS overcomes the limitations of other plant expression databases by allowing for updating of information from previous publications, by directly linking to publications and through the tracking of genes with unknown function that have the same accession or AGI (Arabidopsis genome initiative) number, which would otherwise be difficult to link between publications. Additionally, genomic, EST, Northern data and information derived from microarrays from multiple plant species are included, after human curation, to ensure accuracy and to standardize the nomenclature of data. The INSIGHTS tools encourage comparison of gene expression patterns, intelligent mining of information, testing and formulation of novel hypotheses on the complex signal transduction and response pathways used by plants. Identifying common elements in pathways affected by different treatments permits the formation of hypotheses previously opaque to the user.

    Genes for proteins involved in the same signal transduction pathway are likely to be coregulated and show the same response to a range of treatments. Thus, to find for example kinases, transcription factors and calcium-binding proteins that are in the same signal transduction pathway, expression patterns should be compared. Verification that identified genes are truly associated within signal transduction or metabolic pathways requires experimental confirmation, but the database and associated diagrams promote more targeted hypothesis formation. This type of analysis is useful in providing a framework for understanding signal transduction responses and to assist with identifying regulatory gene networks. It is also useful for finding genes associated with plant pathogen infection that are also affected by environmental stresses such as drought and cold in differing ways.
    Date of AwardNov 2009
    Original languageEnglish

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