636-0017-00L Computational Biology
|Semester||Autumn Semester 2019|
|Lecturers||T. Vaughan, T. Stadler|
|Periodicity||yearly recurring course|
|Language of instruction||English|
|636-0017-00 G||Computational Biology|
The lecture will be held each Monday (15-17 h) either in Zurich or Basel and will be transmitted via videoconference to the second location. Tutorials will happen in both locations.
Tutorials in Zürich: Monday 17-18h (HG D 16.2)
Tutorials in Basel: Thursday 12-13h (BSA E46)
ATTENTION: Lecture starts on Monday, Sept. 23, First Tutorial in Basel on Thursday Sept. 26
|T. Vaughan, T. Stadler|
|636-0017-00 A||Computational Biology|
Project Work (compulsory continuous performance assessments), no fixed presence required.
|2 hrs||T. Vaughan, T. Stadler|
|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:
* 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.|
|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.
|Prerequisites / Notice||Basic knowledge in linear algebra, analysis, and statistics will be helpful. Programming in R will be required for the project work (compulsory continuous performance assessments). We provide an R tutorial and help sessions during the first two weeks of class to learn the required skills. However, in case you do not have any previous experience with R, we strongly recommend to get familiar with R prior to the semester start. For the D-BSSE students, we highly recommend the voluntary course „Introduction to Programming“, which takes place at D-BSSE from Wednesday, September 12 to Friday, September 14, i.e. BEFORE the official semester starting date http://www.cbb.ethz.ch/news-events.html |
For the Zurich-based students without R experience, we recommend the R course Link, or working through the script provided as part of this R course.
|Performance assessment information (valid until the course unit is held again)|
|Performance assessment as a semester course|
|ECTS credits||6 credits|
|Examiners||T. Vaughan, T. Stadler|
|Language of examination||English|
|Repetition||The performance assessment is only offered in the session after the course unit. Repetition only possible after re-enrolling.|
|Mode of examination||written 90 minutes|
|Additional information on mode of examination||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.|
|This information can be updated until the beginning of the semester; information on the examination timetable is binding.|
|Main link||CB Materials|
|Only public learning materials are listed.|
|No information on groups available.|
|There are no additional restrictions for the registration.|