Teaching

Population Genomics

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.

Bioinformatics and Programming

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 full course description.

Student project at BiRC

Student projects include Project in Bioinformatics and Master project. Past student projects in the group:

  • Detection of Selection in the Tumour Suppressor Cluster 3p21.3
  • A composite of multiple signals reveals signatures of positive selection in the 3p21.31 region of East Asians
  • Differential circRNA expression
  • Explorative analysis of X chromosome diversity in Sub-Saharan Africa populations
  • Analysis of positive selection in great ape evolution
  • Differential circRNA expression
  • Profiling circRNA in spermatogenesis

[Template git repository]](https://github.com/kaspermunch/birc-project) and HOWTO for a data analysis project on the GenomeDK cluster