'Computable' Phenotypes Enable Comparative And Predictive Phenomics Among Plant Species And Across Domains Of Life

Scientists are adept at comparing genomic sequences. The collection of more such data promises to increase our ability to determine gene function, discover and describe biological processes, and prioritize causative variants of interest that underlie disease response. Yet the question remains: Can we compare phenotypes or traits of interest across disciplines in a manner similar to how we compare genomic sequences? Here we present examples of 'semantic reasoning' - computational methodologies that enable computation across organized formal phenotypic representations. These methods facilitate the analysis of phenotype information across species, domains of knowledge, people, and computers. We review representative examples of successful semantic reasoning to recover known biological phenomena in medical and agricultural applications. Necessary changes in how we collect, analyze, and share data to enable such computations are presented, and database and analytic tool suites for these sorts of analyses are described.