Biological sequences dominate the data basis for evolutionary analyses. The improvement of high-throughput DNA sequencing methodologies meanwhile result in the availability of comprehensive genetic and genomic sequence information for organisms even from the remotest corner of the tree of life. Full exploitation of the information content of this data and correct interpretations are inextricably linked to three questions: How does one process, organise and analyse data sets from high-throughput DNA sequencing? How do DNA sequences and the proteins encoded therein change over time, and what can be learned about their evolutionary history from comparing present-day sequences? And finally, what assumptions and (evolutionary) concepts underlie common bioinformatic sequence analysis algorithms, and how can these influence the outcome of an analysis?
This module will use the assembly and analysis of a small eukaryotic genome as an example to answer these three questions. Unfortunately, it is always a little difficult to estimate how close one's own reconstructions and conclusions come to the actual truth in bioinformatics. To get around this shortcoming and get a better 'feel' for the data and the analysis results, we will support our work on real sequence datasets with parallel analyses of simulated datasets. The practical work will be accompanied by lectures and a seminar in which the theoretical basics will be taught and deepened.
