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636-0017-00L 6 Credits BSC , DR , MSC , WBZ , NDS D-INFK , D-MATH , D-PHYS , D-BIOL , D-ITET , D-BSSE , D-HEST , D-USYS

Computational Biology

Lecturers: Prof. Dr. Tanja Stadler, Dr. Carsten Magnus, Dr. Timothy Vaughan
Examiners: Prof. Dr. Tanja Stadler, Dr. Timothy Vaughan
VVZ CR n/a

Last Updated: 2026-06-03 00:07:30

Abstract

The aim of the course is to provide up-to-date knowledge on how we can study biological processes using genetic sequencing data. Computational algorithms extracting biological information from genetic sequence data are discussed, and statistical tools to understand this information in detail are introduced.

Objective

Attendees will learn which information is contained in genetic sequencing data and how to extract information from this data using computational tools. The main concepts introduced are: * stochastic models in molecular evolution * phylogenetic & phylodynamic inference * maximum likelihood and Bayesian statistics Attendees will apply these concepts to a number of applications yielding biological insight into: * epidemiology * pathogen evolution * macroevolution of species

Content

The course consists of four parts. We first introduce modern genetic sequencing technology, and algorithms to obtain sequence alignments from the output of the sequencers. We then present methods for direct alignment analysis using approaches such as BLAST and GWAS. Second, we introduce mechanisms and concepts of molecular evolution, i.e. we discuss how genetic sequences change over time. Third, we employ evolutionary concepts to infer ancestral relationships between organisms based on their genetic sequences, i.e. we discuss methods to infer genealogies and phylogenies. Lastly, we introduce the field of phylodynamics, the aim of which is to understand and quantify population dynamic processes (such as transmission in epidemiology or speciation & extinction in macroevolution) based on a phylogeny. Throughout the class, the models and methods are illustrated on different datasets giving insight into the epidemiology and evolution of a range of infectious diseases (e.g. HIV, HCV, influenza, Ebola). Applications of the methods to the field of macroevolution provide insight into the evolution and ecology of different species clades. Students will be trained in the algorithms and their application both on paper and in silico as part of the exercises.

Resources

Lecture Notes

Lecture slides will be available on moodle.

Literature

The course is not based on any of the textbooks below, but they are excellent choices as accompanying material: * Yang, Z. 2006. Computational Molecular Evolution. * Felsenstein, J. 2004. Inferring Phylogenies. * Semple, C. & Steel, M. 2003. Phylogenetics. * Drummond, A. & Bouckaert, R. 2015. Bayesian evolutionary analysis with BEAST.

General Information

Language
English
Levels
BSC , DR , MSC , WBZ , NDS
Frequency
Yearly recurring

Examination

Type
session examination
Mode
written 90 minutes
Aids
None
Compulsory continuous performance assessment in form of homework project assignments amounts to 25% of the final grade. The project work has to be re-done in case of repetition.

Course Components

Type Title Time & Place Hours
lecture with exercise Computational Biology
The lecture will be held each Thursday (13h-15h) in Basel and will be transmitted via videoconference to Zurich. Tutorials will happen in both locations. Tutorials in Basel: Thursday 15h (BSS E 21) Tutorials in Zurich: Friday 11h (CHN G 42) Attention: the lecture and tutorials start in the second week of the semester.
No time listed 3 h weekly
independent project Computational Biology
Project Work (compulsory continuous performance assessments), no fixed presence required.
No time listed 2 h weekly

Offered In