Collaborative Research: Novel Methodologies for Genome-Scale Evolutionary Analysis of Multi-Locus Data

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Rice University, the University of Michigan, and the University of Texas at Austin are awarded collaborative grants to develop and implement algorithms and software tools for the analysis of gene genealogies and inference of species phylogenies from them. A gene genealogy, also known as gene tree, models how genes replicate and get transmitted from one generation to the next during evolution. A species phylogeny models how species arise and diverge. A species phylogeny is traditionally inferred by a three-step process: (1) a genomic region from the set of species under study is sequenced; (2) a "gene tree" is inferred for the genomic region; and, (3) the gene tree is declared to be the species tree. However, recent evolutionary genomic analyses of various groups of organisms have demonstrated that different genomic regions may have evolutionary histories that disagree with each other as well as with that of the species. Further, evolutionary processes such as horizontal gene transfer, result in network-like, rather than tree-like, species phylogenies. This joint project will develop accurate computational methods for determining the causes of gene tree discordance, and inferring species phylogenies (trees as well as networks) from gene trees despite their discordance. Special emphasis will be put on the efficiency of the methods so that they allow for analysis of genome-scale data sets. All methods will be implemented and extensively tested for performance. All methods developed will be made publicly available in software packages that we have been developing in the respective groups. The material will be integrated into courses that the PIs regularly teach at their respective institutions. Last but not least, the project will culminate with a two-day workshop, open to students and post-doctoral fellows from around the country, with presentations by the investigators on the methodologies developed, as well as hands-on tutorials on using the tools in analyzing data.