636-0017-00L  Computational Biology

SemesterHerbstsemester 2018
DozierendeT. Stadler, C. Magnus, T. Vaughan
Periodizitätjährlich wiederkehrende Veranstaltung
LehrspracheEnglisch



Katalogdaten

KurzbeschreibungThe 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.
LernzielAttendees 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
InhaltThe 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.
SkriptLecture slides will be available on moodle.
LiteraturThe 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.
Voraussetzungen / BesonderesBasic 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.

Leistungskontrolle

Information zur Leistungskontrolle (gültig bis die Lerneinheit neu gelesen wird)
Leistungskontrolle als Semesterkurs
ECTS Kreditpunkte6 KP
PrüfendeT. Stadler, C. Magnus, T. Vaughan
FormSessionsprüfung
PrüfungsspracheEnglisch
RepetitionDie Leistungskontrolle wird nur in der Session nach der Lerneinheit angeboten. Die Repetition ist nur nach erneuter Belegung möglich.
Prüfungsmodusschriftlich 90 Minuten
Zusatzinformation zum PrüfungsmodusCompulsory 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.
Hilfsmittel schriftlichKeine
Diese Angaben können noch zu Semesterbeginn aktualisiert werden; verbindlich sind die Angaben auf dem Prüfungsplan.

Lernmaterialien

 
HauptlinkCB Materials
Es werden nur die öffentlichen Lernmaterialien aufgeführt.

Lehrveranstaltungen

NummerTitelUmfangDozierende
636-0017-00 GComputational 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 13-14h (BSB E4)
First lecture takes place Monday, Sept. 24.
First tutorial in Zurich takes place Monday, Sept. 24.
First tutorial in Basel takes place Thursday, Sept. 27.
3 Std.
Mo15-17BSA E 46 »
15-17HG D 16.2 »
17-18HG D 16.2 »
Do13-14BSB E 4 »
T. Stadler, C. Magnus, T. Vaughan
636-0017-00 AComputational Biology
Project Work (compulsory continuous performance assessments), no fixed presence required.
2 Std.T. Stadler, C. Magnus, T. Vaughan

Gruppen

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Einschränkungen

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