M.Sc. Bioinformatics

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Fundamental study portion

Three basic modules, 6 credit points (CP) each, must be done within the first semester. All three modules consist of a lecture (2 SWS = hours per week) and an exercise/tutorial (2 SWS):

In addition, there is an introductory module provided (12 CP). This introductory module consists of a lecture series (2 SWS) and a seminar (2 SWS):

In this lecture we will introduce concepts and methods of advanced algorithmics relevant to current reseach in bioinformatics. We will discuss Methods for the development and analysis of deterministic and randomised algorithms and foundations of compact data structures. Finally, the lecture will encompass concepts for parallel and vectorized computing.

In more detail we will cover:

- Introduction into different kinds of algorithms and analysis methods
- Foundations of compact data structures 
- Graph theiry and graph algorithms 
- Analysis of randomized algorithms and data structures
- Introduction into parallel and vectorized computing
- Concepts, paradigms and frameworks for distributed computing

In this course we introduce advanced mathematical concepts and methods from optimization, numerics and statistics relevant to current reseach in bioinformatics and systems biology. Topics of interest include:

- Linear optimization (Simplex algorithm, duality)
- Integer linear optimization (branch-and-bound, cutting planes, branch-and-cut)
- Local search and metaheuristics
- Ordinary differential equations (modeling, analysis, sensitivity)
- Linear models and test theory
- Classification
- Bootstrap and model evaluation
- Markov models (EM, HMM, MCMC)

This series of seminars and tutorials gives an introduction into how information is stored in the genome, how this information replicated and passed on to daughter cells and within the germline, and how it is read out to give rise to the living organism. The origin and consequences of germline and somatic genomic variability on the cellular and phenotypic level will be discussed as well as the methods for genome analysis including aspects of data processing and safety. Using different human hereditary disorders as examples pathological changes in cellular compartments and in the 3D structure of the genome and the consequences on gene regulation will be explained. The aim is a broad overview over the use of genomic data in clinical medicine.

The module presents interdisciplinary exemplary problems and approaches from the three focus areas "Data Science for Bioinformatics", "Complex Systems in Bioinformatics" and "Advanced Algorithms in Bioinformatics". During a project, teams work together on concrete tasks on selected topics from these focus areas. They develop concrete proposals for solutions to practice-oriented problems, implement them and present the results.