262-0200-00L Bayesian Phylodynamics
|Dozierende||T. Stadler, T. Vaughan|
|Periodizität||jährlich wiederkehrende Veranstaltung|
|Kurzbeschreibung||How fast was Ebola spreading in West Africa? Where and when did the epidemic outbreak start? How can we construct the phylogenetic tree of great apes, and did gene flow occur between different apes? Students will be able to perform their own phylodynamic analysis of genetic sequencing and independent data analysis to characterize future epidemic outbreaks or reconstruct parts of the tree of life.|
|Lernziel||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:
* Pathogen evolution
* Macroevolution of species
|Inhalt||In the first part of the semester, in each week, we will first present the theoretical concepts of Bayesian phylodynamics. The presentation will be followed by attendees using the software package BEAST v2 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 semester, the 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. The weekly class is intended to discuss and monitor progress and to address students’ questions very interactively. At the end of the semester, the students present their research project in an oral presentation. The content of the presentation, the style of the presentation, and the performance in answering the questions after the presentation will be marked.
|Skript||Lecture slides will be available on moodle.|
|Literatur||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.
The tutorials in this course are based on our Summer School “Taming the BEAST”: https://taming-the-beast.org/
|Voraussetzungen / Besonderes||This class builds upon the content which we taught in the Computational Biology class (636-0017-00L). Attendees must have either taken the Computational Biology class or acquired the content elsewhere.|