HIERARCHICAL COMPUTING FOR DYNAMIC EVOLUTIONARY INFERENCE OF COMPLEXITY

We propose to develop new tools grounded in emerging techniques for accommodating heterogeneity in genomic and trait datasets to enable statistical comparison of the formation and evolution of modules across many taxa that efficiently handle multivariate datasets consisting of multiple sources including morphology, genomes, biochemical data, and gene expression. These new methods will identify common patterns of evolutionary rate and mode across multiple genes and traits, allow for lineage specific heterogeneity, scale to the large dimensions common in modern datasets, and break the false dichotomy of genes vs traits. Given the size of the datasets, and the necessity to explore model complexity, these methods will require significant computation and a hierarchical computational approach: distributed computing for independent analyses (e.g., individual genes), multi-core parallel computing of individual analyses where likelihoods are calculated in parallel on shared memory resources, and GPU computing for more extensive model explorations that require extensive matrix calculations. The methodological developments will be implemented in, gophy, a package developed by the participants of the proposal.