Bayesian Phylodynamics

Abstract

How fast is the latest variant of COVID-19 spreading? How fast was Ebola spreading in West Africa? Where did these epidemics come from? How can we construct the phylogenetic tree of great apes, and did gene flow occur between different apes? At the end of the course, students will have designed, performed, presented, and discussed their own phylodynamic data analysis to answer such questions.

Objective

Attendees will extend their knowledge of Bayesian phylodynamics obtained in the “Computational Biology” class (636-0017-00L) and will learn how to apply this theory to real world data. The main theoretical concepts introduced are: * Bayesian statistics * Phylogenetic and phylodynamic models * Markov Chain Monte Carlo methods Attendees will apply these concepts to a number of applications yielding biological insight into: * Epidemiology * Pathogen evolution * Macroevolution of species

Content

In the first part of the block course, we will present the theoretical concepts of Bayesian phylodynamics. This will involve both lectures and tutorials, during which students will gain experience in using the software package BEAST 2 to apply these theoretical concepts to empirical data. We use previously published datasets on e.g. Ebola, Zika, Yellow Fever, Apes, and Penguins for analysis. Examples of these practical tutorials are available on https://taming-the-beast.org/ . In the second part of the block course, students will choose a set of real genetic sequence data and possibly some non-genetic metadata. They will then design and conduct a research project in which they perform Bayesian phylogenetic analyses of their chosen data. A final written report on the research project will be submitted after the block course for grading.

Resources