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Bayesian Phylodynamics – Taming the BEAST
Last Updated: 2026-02-05 15:55:00
Abstract
How 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.
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
During 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.
Resources
Lecture Notes
All material will be available onhttps://taming-the-beast.org/.
Literature
The 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/ .
General Information
- Language
- English
- Levels
- BSC , MSC
- Frequency
- Yearly recurring
Examination
- Type
- graded semester performance
Course Components
| Type | Title | Time & Place | Hours |
|---|---|---|---|
| lecture with exercise |
Bayesian Phylodynamics – Taming the BEAST
Block course in first week after the semester (June 7-11); all day.
The whole course can be followed virtually and – given the pandemic situation allows – will be held at D-BSSE in Basel.
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2 h weekly |
| independent project | Bayesian Phylodynamics – Taming the BEAST | No time listed | 2 h weekly |
Offered In
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Additional Electives from the Fields of Specialization (CSE Master) (recognition of 227-0662-00L and 227-0662-10L requires the successful completion of both course units)
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Electives (Electives may be taken at D-BSSE or at Uni Basel. The mentor may assign other courses to the electives category based on student`s formal request. Courses offered in the advanced courses category may also be taken as electives.)
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Application Area (Only necessary and eligible for the Master degree in Applied Mathematics. One of the application areas specified must be selected for the category Application Area for the Master degree in Applied Mathematics. At least 8 credits are required in the chosen application area.)
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Computational Biology and Bioinformatics Master (More informations at: )
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Advanced Courses (A total of 30 ECTS needs to be acquired in the Advanced Courses category. Thereof 18 ECTS in the Theory and 12 ECTS in the Biology category. Note that some of the lectures are being recorded: )
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Theory (At least 18 ECTS need to be acquired in this category.)
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