Virginia Tech Life Science Seminar Series
Dr. Nicholas Provart on "Raising the BAR for Hypothesis Generation in Plant Biology Using Large Data Sets"
Friday, February 20, 12:20pm, VBI Conference Center, 1015 Life Science Circle
Abstract: We have developed tools, available as part of the Bio-Analytic Resource at http://bar.utoronto.ca, for exploring large data sets from plants, to allow deeper insights into biological questions, and to help guide lab-based research. An emerging theme in plant biology is that interactions, be they regulatory or protein-protein, create networks. In the former instance, coexpression networks can provide more robust support for inferred biological involvement than simple coexpression analyses alone. Coexpression networks developed using publicly-available gene expression data sets from dormant and germinating seeds have provided high-quality candidates for genes involved in regulating these two important processes (joint work with George Bassel – Division of Plant & Crop Sciences, University of Birmingham; and Hui Lan and Anthony Bonner – Department of Computer Science, University of Toronto). In the latter instance, the complex cellular functions of an organism frequently rely on physical interactions between proteins. A map of all protein-protein interactions, an interactome, is thus an invaluable tool. Interactomes for Arabidopsis thaliana and rice predicted from interacting orthologs in 7 organisms will be presented (joint work with Matt Geisler and Jane Geisler-Lee – Southern Illinois University Carbondale). These predictions can aid researchers by extending known complexes and pathways with candidate proteins.Methods for integrating networks of coexpression, protein-protein interaction, and of other high-throughput data, can provide additional levels of support for novel function identification. An algorithm for doing so, called GeneMANIA, will be presented and discussed (joint work with Quaid Morris – CCBR, University of Toronto). Finally, a method for leveraging microarray- or RNA-Seq-based gene expression atlases across 8 plant species for identifying the most likely “expressologs” (homologs showing the most similar pattern of expression in equivalent tissues) between species will be presented.