Bioinformatics Algorithms

Subject description

– Sequence analysis, search for subsequences, motif search.

– Genome assembly, graph algorithms.

– Comparison of biological sequences, dynamic programming.

– Phylogeny algorithms.

– Hidden Markov Models and gene prediction.

– Gene expression analysis, clustering and supervised data mining, enrichment analysis.

– Gene network reconstruction and analysis.

– Data vizualization.

The subject is taught in programs

Objectives and competences

Students completing the course should be able to implement a variety of bioinformatics and systems biology algorithms, and learn which type of biological questions can be answered by means of computational approaches.

Teaching and learning methods

Workshops, homeworks, consultations, seminar. Solving problems on learning portals such as and

Expected study results

Students will become familiar with main classess of computational approaches and algorithms in bioinformatics. The algorithms that they will design in a class are those from sequence and graph analysis and analysis of data coming from experimental measurements in molecular biology. In practical cases of analysis of large data sets they will need to cope with problems of computational efficiency and limited data storage (computer memory). They will advance their knowledge of programming, and use thier previously developed skills in probability and statistics in practical problems from systems biology.

Basic sources and literature

– Durbin R, Eddy SR, Krogh A, Mitchison G (1998) Biological sequence analysis: probabilistic models of proteins and nucleic acids, Cambridge University Press.

– Jones NC, Pevzner PA (2004) An introduction to bioinformatics algorithms, The MIT Press.

– Pavel A. Pevzner, Phillip Compeau (2018) Bioinformatics Algorithms: An Active Learning Approach , Active Learning Publishers.

Ostalo: revijalni članki s področja, tekoča periodika in druga učna gradiva.

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