Raising the BAR for Hypothesis Generation in Plant Biology Using Open Big Data.

Speakers: Nicholas J. Provart, University of Toronto 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. My lab’s three visual analytic tools for transcriptomic data (eFP Browser, ePlant, and eFP-Seq Browser) allow for rapid access to comprehensive gene expression compendia we have curated for identifying tissues, cell-types, or perturbations in which a gene is active or alternatively spliced. Interactions, be they protein-protein or regulatory, create networks. We have developed new tools for exploring such data, either from large collections of experimentally-supported protein-protein or protein-DNA interactions or from predicted interactions, including protein-protein interactions inferred from molecular docking studies. We are currently working on integrating large-scale phenotype data from field trials monitored by drone-based sensors into ePlants we have developed for several agronomically-important species to improve understanding of links between genotype and phenotype.