Hypothesis generation for plant biology: enabling cyberinfrastructure and experimental validation

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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. Of the 27,411 predicted gene products, however, only ~60% have had their function predicted or identified experimentally to date. The international AtGenExpress effort has provided a reference set of thousands of gene expression data sets 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. Many other data sets have become available recently (metabolomic, proteomic, genetic variation, epigenetic etc.) and these can also be used for hypothesis generation/function prediction. Large collections for Arabidopsis reverse genetics allow rapid experimental testing of in silico-generated hypotheses. To fully take advantage of these plant biology resources and further their development for use in plant biology, I am proposing a two-pronged informatic and biology-based approach in this grant application. First, it is imperative that lab-based plant researchers, including my lab, 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 data sets and displaying the results are a must. My lab has already made available several such tools at its popular Bio-Array Resource, including an electronic Fluorescent Pictograph (eFP) Browser, Expression Angler for identifying coexpressed genes across all data sets, and Promomer for identifying cis-elements in coexpressed genes. Other cyberinfrastructure for hypothesis generation is in development including an ePlant. The other aspect of my proposed research will focus on experimental validation of our tools using a combination of reverse genetics, biochemistry, and molecular biological methods.