262-0200-00L  Bayesian Phylodynamics

SemesterSpring Semester 2020
LecturersT. Stadler, T. Vaughan
Periodicityyearly recurring course
Language of instructionEnglish



Courses

NumberTitleHoursLecturers
262-0200-00 GBayesian Phylodynamics
***ATTENTION: Starting with the lecture on March18, the Bayesian Phylodynamics lecture will be broadcasted using a Zoom videoconference. The lecturer will inform the students about the URL to participate in the online course***
2 hrs
Wed11:15-13:00BSB E 4 »
T. Stadler, T. Vaughan
262-0200-00 ABayesian Phylodynamics2 hrsT. Stadler, T. Vaughan

Catalogue data

AbstractHow 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? 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
ContentIn 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 Link.
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.
Lecture notesLecture slides will be available on moodle.
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.
The tutorials in this course are based on our Summer School “Taming the BEAST”: Link
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.

Performance assessment

Performance assessment information (valid until the course unit is held again)
Performance assessment as a semester course
ECTS credits4 credits
ExaminersT. Stadler, T. Vaughan
Typegraded semester performance
Language of examinationEnglish
RepetitionRepetition only possible after re-enrolling for the course unit.
Additional information on mode of examinationGraded oral presentation of research project which was conducted throughout the semester (10 min of presentation of research project, plus 5 min of questions on presentation and research project).

Learning materials

No public learning materials available.
Only public learning materials are listed.

Groups

No information on groups available.

Restrictions

There are no additional restrictions for the registration.

Offered in

ProgrammeSectionType
Biotechnology MasterElectivesWInformation
Computational Biology and Bioinformatics MasterTheoryWInformation
Data Science MasterInterdisciplinary ElectivesWInformation
Mathematics MasterBiologyWInformation
Computational Science and Engineering BachelorAdditional Electives from the Fields of Specialization (CSE Master)WInformation
Computational Science and Engineering MasterBiologyWInformation
Environmental Sciences MasterAdvanced ConceptsWInformation