Mercedes Burns, Biological Sciences
Second- and third-generation sequencing technologies produce large datasets that can be variously parsed and mined for significant genomic features. These subsets are summarily passed to likelihood-based programs that model evolutionary processes or population statistics. The programs, particularly those utilizing Bayesian MCMC approaches, are computationally intensive and successful utilization necessitates a similarly high-throughput computing strategy. The UMBC HPCF will be utilized to efficiently handle genomic datasets for demultiplexing, variant calling, and species delimitation analyses.