Individual Genomes Reveal Deep Population Histories and Uncover the Evolutionary Roles of Non Coding DNA
High throughput DNA sequencing has transformed the landscape of genomic data and is expected to revolutionize our knowledge of evolution and genomic function. However, the abundant sequence data also poses serious computational challenges, and realizing its full potential requires developing efficient and reliable computational and statistical inference methods. This talk will cover work that I have done as part of my postdoctoral research, utilizing newly emerging genomic
data sets and population genetic models to examine several open questions in evolution. I will start by describing a study I conducted of ancient human population demography in Africa, focusing on one of the deepest population divergence events in human history, dating roughly 130 thousand years ago. I will then present work I did as part of a large-scale collaborative effort to study the early evolution of dogs using the complete genome sequences of two dogs and three gray wolves. I will show how we were able to settle several longstanding debates revolving around the origins of dogs using these genomes and an innovative computational approach I developed. Lastly, I will describe a parallel line of research I have been recently conducting, trying to understand the evolutionary roles of non coding regulatory elements in the human genome. The talk will describe the computational challenges involved in these three studies. I will outline the methods developed to address these challenges, and present the main findings and their significance. I will conclude with a short survey of my ongoing research, and a map of the opportunities and challenges we face in the study of evolution in a world of rapidly evolving genomic data sets.
Note: the talk does not require any prior biological knowledge.