[1] Phillip Compeau, Pavel Pevzner, Bioinformatics Algorithms 3rd Edition, Active Learning Publishers, 2018
[2] Arthur Lesk, Introduction to Bioinformatics 5th Edition, Oxford Universitz Press, 2019
[3] Susan Holmes and Wolfgang Huber, Modern Statistics for Modern Biology, Cambridge University Press, 2019
[4] Vince Buffalo, Bioinformatics Data Skills, O’Reilly, 2015
Bioinformatics
| Language of instruction | angličtina, čeština |
|---|---|
| Code | 460-4175 |
| Abbreviation | BI |
| Course title | Bioinformatics |
| Coordinating department | Department of Computer Science |
| Course coordinator | Ing. Michal Vašinek, Ph.D. |
Summary
Upon completing the course, the student will be able to explain the key concepts of modern bioinformatics, select appropriate algorithms for sequence similarity measurement, indexing, and alignment, and apply them to real DNA/RNA data. They will be able to analyze data quality, interpret and compare outputs from different sequencing tools, distinguish among sequencing technologies and explain their limitations. They will know how to design and build a basic bioinformatics pipeline, experiment with parameters, evaluate results, and justify the chosen approach. In addition, they will be able to algorithmically identify structural variants, analyze gene expression, and integrate multi-omics data in order to determine subsequent steps of the analysis.
Literature
Advised literature
[1] Shai Ben-David, Artificial Intellgience for Medicine, Academic Press, 2024