Timothy Vaughan: Catalogue data in Spring Semester 2021

Name Dr. Timothy Vaughan
Name variantsTim Vaughan
Timothy G. Vaughan
Address
Computational Evolution
ETH Zürich, BSS J 2.1
Klingelbergstrasse 48
4056 Basel
SWITZERLAND
E-mailtimothy.vaughan@bsse.ethz.ch
DepartmentBiosystems Science and Engineering
RelationshipLecturer

NumberTitleECTSHoursLecturers
262-0200-00LBayesian Phylodynamics – Taming the BEAST4 credits2G + 2AT. Stadler, T. Vaughan
AbstractHow fast is COVID-19 spreading at the moment? How fast was Ebola spreading in West Africa? Where and when did these epidemic outbreak start? 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.
ObjectiveAttendees 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
ContentDuring the first part of the block course, the theoretical concepts of Bayesian phylodynamics will be presented by us as well as leading international researchers in that area. The presentations will be followed by attendees using the software package BEAST v2 to apply these theoretical concepts to empirical data. We will use previously published datasets on e.g. COVID-19, 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 choose an empirical dataset of genetic sequencing data and possibly some non-genetic metadata. They then design and conduct a research project in which they perform Bayesian phylogenetic analyses of their dataset. A final written report on the research project has to be submitted after the block course for grading.
Lecture notesAll material will be available on https://taming-the-beast.org/.
LiteratureThe following books provide excellent background material:
• Drummond, A. & Bouckaert, R. 2015. Bayesian evolutionary analysis with BEAST.
• Yang, Z. 2014. Molecular Evolution: A Statistical Approach.
• Felsenstein, J. 2003. Inferring Phylogenies.
More detailed information is available on https://taming-the-beast.org/.
Prerequisites / NoticeThis class builds upon the content which we teach in the Computational Biology class (636-0017-00L). Attendees must have either taken the Computational Biology class or acquired the content elsewhere.