Bioinformatics-based hypothesis generation with biological validation for plant stress biology

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The publication of the genomic sequence of the model organism Arabidopsis thaliana in 2000 was a significant event in plant biology, and has proved to be a valuable resource to investigators in many different areas of plant research. However, of the 25498 predicted gene products, only ~10% have had their function identified experimentally. Gene expression profiling for the identification of genes that are coregulated at the level of gene expression under a given condition or conditions is one method for functional characterization. The international AtGenExpress effort has provided a reference set of 1400 Affymetrix microarray datasets from both wild-type and mutant plant material harvested under diverse conditions and treatments to guide researchers towards potential gene functions for their genes of interest. The availability of at least 13 collections for Arabidopsis reverse genetics will allow rapid experimental testing of in silico hypotheses generated using AtGenExpress or other database resources. To fully take advantage of these plant biology resources and further their development for use in plant functional genomics, I am proposing a two-pronged bioinformatic and biology-based approach in this grant application. First, it is imperative that lab-based plant researchers, including ourselves, have the tools at their disposal to make use of the enormous amount of information that is being accumulated. User-friendly interfaces for querying the datasets and displaying the results are a must. My lab has made available several such tools at the Botany Array Resource, including an electronic or e-Northern tool, Expression Angler for identifying coregulated genes across all datasets, and Promomer for identifying cis-elements in coregulated genes. Other tools are in development. The other aspect of my proposed research is to focus on in planta testing of functional predictions. To this end, expression data sets from plants subject to abiotic and other stresses will be mined using machine learning algorithms. The function of stress-associated genes with annotations such as 'expressed protein' will be examined using a combination of reverse genetics and cell biological procedures.