The course introduces key concepts in population genomics from generation of population genetic data sets to the most common population genetic analyses and association studies. The first part of the course focuses on generation of population genetic data sets. The second part introduces the most common population genetic analyses and their theoretical background. Here, topics include analysis of demography, population structure, recombination and selection. The last part of the course focus on applications of population genetic data sets for association studies in relation to human health. See the full course description and the GitHub page.
I have developed Python libraries to aid student leadning:
The course is available on github. Feel free to reuse.
The course introduces programming and its practical applications in bioinformatics. The course also outlines and discusses bioinformatics algorithms and the most common tools for bioinformatics analysis of sequence data are presented and demonstrated. During the first seven weeks the participant will aquire and train basic programming skills. The last seven weeks introduce key topics in bioinformatics with focus on application of bioinformatical software and aquired programming skills. Subjects for lectures and exercises include: bioinformatics databases, sequence alignment, genome annotation, sequence evolution, phylogenetic analysis. See the course website for more information.
I have developed my own lecture notes and a series of Python libraries to aid student leadning:
The course is available on github. Feel free to reuse.
Student projects include Project in Bioinformatics and Master project. Past student projects in the group:
[Template git repository]](https://github.com/kaspermunch/birc-project) and HOWTO for a data analysis project on the GenomeDK cluster