Introduction to computational biology 1000-2N03BO
1. Biological introduction: basic knowledge of molecular biology, structure of nucleic acids and proteins, proteins, transcription, translation, experimental techniques (2 lectures).
2. Molecular sequence analysis: algorithms for alignment of two sequences (global – Needleman-Wunsch algorithm and local – Smith-Waterman algorithm) (2 lectures).
3. Mathematical models of molecular evolution: Jukes-Cantor and Kimura models for DNA sequences, amino acid substitution tables for proteins: PAM, BLOSUM, statistical significance of alignment scores. (3 lectures).
4. Hidden Markov models and their applications: Viterbi and Baum-Welch algorithms (2 lectures).
5. Multiple alignment (dynamic programming, `star alignment', `tree alignment'), effective heuristics: CLUSTALW, T-Coffee, MAFFT (2 lectures).
6. Genome-scale alignment (1 lecture).
7. Introduction to phylogenetics (2 lectures).
The course will be given in Polish, if no non-Polish-speaking students register for it.
Type of course
Course coordinators
Learning outcomes
Knowledge:
1. Has a general knowledge of the problems of computational biology.
2. Has a basic knowledge of the mathematical tools used in modeling and analysis of molecular data.
Skills:
1. Can perform simple bioinformatics analyses for molecular sequences.
2. Can use advanced bioinformatics tools.
Competences:
1. Knows the limitations of their own knowledge and understands the need for further education (K_K01).
2. Is able to manage their time and make commitments and meet deadlines (K_K05)
3. Is able to use interdisciplinary literature.
Assessment criteria
Theory test, programming assignments, programming homework. Oral exam.
In the case of completing the course by a doctoral student, an additional element will be to read an original research article that is close to the current research front and discuss it with the lecturer.
Bibliography
1. A. Malcolm Campbell. Laurie J. Heyer, Discovering Genomics, Proteomics, and Bioinformatics, Pearson Education 2003.
2. R. Durbin, S. Eddy, A. Krogh, G. Mitchson, Biological Sequence Analysis, Cambridge Univ. Press, 1997.
3. P. Pevzner, Computational Molecular Biology, The MIT Press, 2000.
4. W. J. Ewens, G. Grant, Statistical Methods in Bioinformatics, Springer 2001.
Additional information
Information on level of this course, year of study and semester when the course unit is delivered, types and amount of class hours - can be found in course structure diagrams of apropriate study programmes. This course is related to the following study programmes:
- Bachelor's degree, first cycle programme, Computer Science
- Master's degree, second cycle programme, Bioinformatics and Systems Biology
- Master's degree, second cycle programme, Computer Science
- Master's degree, second cycle programme, Mathematics
Additional information (registration calendar, class conductors, localization and schedules of classes), might be available in the USOSweb system: