The purpose of this course is to give an understanding of computational problems in modern biomedical research. We will start with concrete medical questions, develop a formal problem description, setup an algorithmic/statistical model, solve it and subsequently derive real-world answers from within the solved model. The course aims for giving a basic understanding of which problems arise in modern molecular biology and clinical research, and how these problems can be solved with appropriate computational tools. It is a class that needs regular attendance. Precondition for admittance to the exam will be the preparation of exercise sheets as well as the course project.
Expected learning outcome
- Explain and understand the central dogma of molecular biology, central aspects of gene regulation, the basic principle of epigenetic DNA modifications, and specialties w.r.t. bacteria & phage genetics
- Model ontologies for biomedical data dependencies
- Design of systems biology databases
- Explain and implement DNA & amino acid sequence analysis methods (HMMs, scoring matrices, and efficient statistics with them on data structures like suffix arrays)
- Explain and implement statistical learning methods on biological networks (network enrichment)
- Explain the specialties of bacterial genetics (the operon prediction trick)
- Explain and implement methods for suffix trees, suffix arrays, and the Burrows-Wheeler transformation
- Explain de novo sequence pattern screening with EM algorithm and entropy models.
- Explain and implement basic methods for supervised and unsupervised data mining, as well as their application to biomedical OMICS data sets
The following main topics are contained in the course:
- Central dogma of molecular genetics, epigenetics, and bacterial and phage genetics
- Design of online databases for molecular biology content (ontologies, and example databases: NCBI, CoryneRegNet, ONDEX)
- DNA and amino acid sequence pattern models (HMMS, scoring matrices, mixed models, efficient statistics with them on big data sets)
- Specialities in bacterial genetics (sequence models and functional models for operons prediction)
- De novo identification of transcription factor binding motifs (recursive expectation maximization, entropy-based models)
- Analysis of next-generation DNA sequencing data sets (memory-aware short sequence read mapping data with Burrows Wheeler transformation and suffix arrays, bi-modal peak calling)
- Visualization of biological networks (graph layouting: small but highly variable graphs vs. huge but rather static graphs)
- Systems biology and statistics on networks (network enrichment with CUSP, jActiveModules and KeyPathwayMiner)
- Basic supervised and unsupervised classification methods for OMICS data analysis
During the course the students have to complete exercise sheets and participate on one large project at the end of the semester. The project will be evaluated with pass/fail and needs to be passed in order to be eligible for the oral exam at the end of the semester.