Introduction to computational biology

elective courses

Algorithms and data structures 1000-213bASD
Probability theory 1000-213bRP

The aim of this course is to introduce students with a computer science and mathematics background to the problems of modern computational biology. The topics are focused on analysis of protein and nucleic acid sequences. Fundamental mathematical models and computational methods used in the description of molecular sequences will be presented.

1. Biological introduction: basic knowledge of molecular biology, structure of nucleic acids and proteins, transcription and translation.

2. Molecular sequence analysis: sequencing by hybridization, algorithms for global and local alignment of two sequences.

3. Mathematical models of molecular evolution: Jukes-Cantor and Kimura models for DNA sequences, PAM and BLOSUM substitution matrices for proteins, statistical significance of alignment scores.

4. Multiple sequence alignment: dynamic programming, greedy algorithms, efficient heuristics (CLUSTALW, T-Coffee, MUSCLE).

5. Hidden Markov Models and their applications to molecular sequences: Viterbi and Baum-Welch algorithms.

6. Searching sequence databases: BLAST algorithm.

7. Finding motifs in DNA sequences, functional enrichment analysis of gene sets.

8. Introduction to phylogenetics: reconstructing phylogenetic trees of single genes and reconciling them.

9. Introduction to genomic data analysis: mapping reads to reference genome, genome assembly, metagenomics.

The course will be given in Polish, if no non-Polish-speaking students register for it.

1. Richard Durbin, Sean R. Eddy, Anders Krogh, Graeme Mitchison, Biological Sequence Analysis: Probabilistic Models of Proteins and Nucleic Acids, Cambridge University Press, 1998.

2. Pavel A. Pevzner, Computational Molecular Biology: An Algorithmic Approach, MIT Press, 2000.

3. Warren J. Ewens, Gregory R. Grant, Statistical Methods in Bioinformatics: An Introduction, Springer 2001.

4. A. Malcolm Campbell, Laurie J. Heyer, Discovering Genomics, Proteomics, and Bioinformatics, CSHL Press, 2007.

Knowledge:

1. Has a general knowledge of the problems of contemporary computational biology.

2. Has basic knowledge of mathematical models and computational methods used in the description of molecular sequences.

Skills:

1. Can implement fundamental bioinformatics analyses of molecular sequences.

2. Can use advanced bioinformatics tools to analyze experimental data.

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.

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.