The subject of the course is an advanced theoretical and practical introduction to bioinformatics with special regard to the analysis of DNA and protein sequences. The goal is to provide students a good, up to date understanding of the contemporary cross-cutting issues of bioinformatics as well as knowledge of the main techniques and algorithms. The topics covered will include:
- Theory of bioinformatics data, core data types,
- Core operations of bioinformatics: aggregation, comparison, annotation
- Concepts of similarity, different similarity searching techniques,
- Pairwise sequence alignment algorithms
- Multiple sequence alignment algorithms,
- BLAST algorithm, its evaluation, and practical applications
- Biological databases
- Fundamentals of phylogenetics analysis
- Next generation sequencing (NGS) techniques
- Genome assembly techniques and algorithms
- Genome annotation techniques and algorithms
- Fundamentals of metagenomics analysis and techniques
- Mutation analysis, variant search techniques
- Contemporary medical applications of NGS
- Modern data representations and frameworks (i.e., network analysis docker containers, working in HPC environment, SPARK).
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- Designing capability of complex pipelines.
- Modern machine learning algorithms and visualization in biology (CART, LASSO, T-SNE, PCA, etc.).
- Complex network representations and their applications
List of the most important 2–5 pieces of required and recommended literature
1. Arthur M. Lesk: Introduction to Bioinformatics, 4e, Oxford University Press, 2013, ISBN 9780199651566
2. David W. Mount: Bioinformatics: Sequence and Genome Analysis, 2e, Cold Spring Harbor, 2004, ISBN 0- 879-69712-1
3. Molecular Data Analysis Using R (Csaba Ortutay, Zsuzsanna Ortutay) ISBN: 978-1-119-16502-6
4. Next-Generation Sequencing Data Analysis (Xinkun Wang; CRC Press 2016) ISBN-10: 1482217880
