Topics : In this class, we introduce fundamental bioinformatics technology for analysing sequencing data. Our focus will be on understanding fundamental computer science methods behind direct processing and important downstream analysis. Thus, the class takes about one third of its time to discuss graph algorithms for the assembly of genomes as well as index structure for the mapping of next and 3rd generation sequencing data. We will then introduce methods for annotation, alignment and motif finding as well as spend one unit on phylogenomics. As another focus area, we turn to the downstream analysis of RNA-seq based experiments, in order to reveal the structure and interactions of RNAs. Here, we will dive into combinatorics of RNA structure and interaction prediction, emphasizing their integration with experimental data. Moreover, we discuss applications of machine learning techniques in down-stream analysis, e.g. specialized kernel methods.
Goals : Students of this class will get an overview of fundamental bioinformatics methods in the analysis of sequencing data and sequencing-based experiments. They will get the opportunity to
gain insights into fundamental algorithmic concepts and see their relevance in modern bioinformatics analysis. In hands-on sessions, students will familiarize themselves with implementation
and other practical aspects of the methods, thereby deepening their grasp of the underlying theory.This class will be relevant and interesting out of diverse motivations. On the one hand, it will enable students to assess existing and future methods in this area or simply apply existing analysis tools more successfully due to understanding their internals. On the other hand, the class aims at providing solid foundations for future developments in sequencing analysis due to a detailed understanding of the principles, current limitations and the potential of key technology in the field.
- Teaching coordinator: Will Sebastian