2019_11_18_Prof. Keith_A._Crandall

Prof. Dr. Keith A. Crandall; Computational Approaches for Characterizing Microbiome Diversity Abstract: Microbiome characterization has become an integral component to the study of a wide variety of disease and health for a diversity of organisms. Through the collection of metagenomic sequence data from DNA and/or RNA samples isolated from host individuals, effective microbiome characterization can identify pathogens, link diversity to disease state, characterize treatment effects, and identify drug resistant variants. I present a computational platform, PathoScope, for metagenomic sequence analysis to characterize microbiome diversity and test hypotheses about diversity associates with disease and diversity dynamics over time. I then describe a second software package, TeleScope, that characterizes transposable elements in genomic data (a part of the microbiome component often ignored), maps those elements back to reference genomes, and identifies active mobile elements and their potential phenotypic impact. I present results from both empirical studies and simulation studies characterizing the utility of our computational approaches with metagenomic data and compare our approach to other leading packages. I then demonstrate our computational tools with applications in endangered species conservation, agriculture, and a variety of aspects of human health. Specifically, I will demonstrate the use of microbiome characterization related to black rhino health, human health related to Konzo disease, and human fecal transplant diversity over time. Finally, I will demonstrate the characterization of human endogenous retroviral elements (HERVs) in relation to head and neck cancer and incorporate this information into risk assessment.