This is a graduate course on phylogenetic methods covering theory, statistics and practice. We will study the evolutionary models used to construct phylogenies (evolutionary trees), and how these phylogenetic estimates may be used to understand evolutionary processes. We will focus on statistical and methodological foundations of these models and methods, and understand the nuts-and-bolts of parsimony, distance, as well as process-model based maximum likelihood and bayesian approaches to inferring phylogenetic trees. We will develop our computational skills to allow us to use High Performance Computing (HPC) resources to perform phylogenetic analyses. The course provides hands-on experience with several important phylogenetic software packages. By the end of the course we will be able to understand much of the primary literature on modern phylogenetic methods and know how to apply these methods to own data.
“Nothing in the biology makes sense except in the light of evolution”
This class will cover evolutionary theory in detail, providing the core conceptual framework to understand the patterns of life that we see around us today in terms of the dynamic interacting processes that structure and inform it.