Population genetics and genomics GMB-204. Basic skills in Linux command-line work.
The student knows key concepts of evolutionary genomic data analysis and Coalescent theory. Can apply computational tools to investigate resequencing data sets.
First year, Spring term, period III, every year
The course teaches
- efficient use of computational tools in the analysis of high-throughput resequencing data from model and non-model organisms, and
- the theoretical background of the key analyses: central concepts of population genetics (pairwise differences, segregating sites, site frequency spectrum) are derived from the Coalescent theory and applied to study the effective population size, population structure and demography and natural selection.
- Course follows lecture → exercise → feedback cycle.
- The contact lectures focus on theoretical concepts and background around the week's topic.
- The computational exercises are performed independently following written instructions; given assignments are returned in electronic format before the feedback session.
- Exercise works are reviewed in feedback sessions.
- Active participation is required to take the course exam.
- Lectures are in regular class rooms. Use of own laptop computer is strongly recommended.
- For some sessions learning will further be enhanced by initial discussion based on selected relevant scientific papers
- Past exercises and assignments are reviewed in feedback sessions.
Course book: Rasmus Nielsen and Montgomery Slatkin, An Introduction to Population Genetics: Theory and Applications, First Edition, Sinauer, 2013.
Supplementary reading: John Wakeley, Coalescent Theory: An Introduction, First Edition, W. H. Freeman, 2008. (very demanding)
Grading scale is 1...5.
The course exam is done in Moodle at the given time period (approx. one week). The exam consists of questions that test general knowledge on the course topics and practical work that utilises the methods learned on the course. Points gathered from the assignments will be added to the exam score.
One should obtain 30 points to pass the course with grade 1. Grade 5 is obtained with 50 points out of the maximum 60. Linear scale is applied for other grades.
The amounts of points that can be acquired via the exercises and the exam will be set at the start of the course.
Deviations from the scheme are possible depending on the difficulty of the exam.
Basic courses on programming (e.g. Python) and data analysis in R, Machine Learning in Molecular Biology.
Replaces the former course 529053 Evolutionary genomics 5 cr.