Teacher: dr. Pavle Goldstein, assistant professor
- To introduce main bioinformatics methods, primarily sequence analysis methods and series of associated techniques,
- The focus of the course will mainly be on algorithmic and computational aspects of the subject, while being aware of the statistical and mathematical background,
- It is intended that the course serves as a preparation for studying real-life problems in biology and medicine, especially proteomics and genomics.
Expected learning outcomes
After the completion of the course, it is expected that a student:
- will be familiar with the mathematical background in biological sequence analysis,
- will be familiar with techniques and modelling algorithms,
- will have a good understanding of underlying methods (dynamic programming),
- will be capable of interpreting results (statistically),
- will be familiar with classification techniques for biological sequences.
- biological sequences,
- pairwise alignment: mutation and substitution matrices, local and global alignment, dynamic programming; posterior probabilities, extreme value distributions,
- introduction to hidden Markov models: Viterbi, forward and backward algorithms, posterior decoding; model optimization, EM-algorithm, Viterbi training.