1. To introduce main bioinformatics methods, primarily sequence analysis methods and series of associated techniques,
2. 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,
3. It is intended that the course serves as a preparation for studying real-life problems in biology and medicine, especially proteomics and genomics.

After the completion of the course, it is expected that a student:

1. will be familiar with the mathematical background in biological sequence analysis,
2. will be familiar with techniques and modelling algorithms,
3. will have a good understanding of underlying methods (dynamic programming),
4. will be capable of interpreting results (statistically),
5. will be familiar with classification techniques for biological sequences.

1. biological sequences,
2. pairwise alignment: mutation and substitution matrices, local and global alignment, dynamic programming; posterior probabilities, extreme value distributions,
3. introduction to hidden Markov models: Viterbi, forward and backward algorithms, posterior decoding; model optimization, EM-algorithm, Viterbi training.