| Course title |
Computational Genomics |
| Acronym |
13M111GI |
| Study programme |
Electrical Engineering and Computing |
| Module |
Applied Mathematics, Audio and Video Communications, Audio and Video Technologies, Biomedical and Environmental Engineering, Biomedical and Nuclear Engineering, Computer Engineering and Informatics, Electronics, Electronics and Digital Systems, Energy Efficiency, Information and Communication Technologies, Microwave Engineering, Nanoelectronics and Photonics, Power Systems - Networks and Systems, Power Systems - Renewable Energy Sources, Power Systems - Substations and Power Equipment, Signals and Systems, Software Engineering, System Engineering and Radio Communications |
| Type of study |
master academic studies |
| Lecturer (for classes) |
|
| Lecturer/Associate (for practice) |
|
| Lecturer/Associate (for OTC) |
|
| ESPB |
6.0 |
Status |
elective |
| Condition |
|
| The goal |
This course presents some of the basic computational methods that can infer biological information from genomic data, the strengths and weaknesses of related methods, and the important parameters embedded in these analyses. Theoretical, applied, and statistical issues will be addressed. |
| The outcome |
Students should be able to understand the principles of algorithm design for biological datasets, to analyze problems, and use described methods in order to locate genes, repeat families, similarities between sequences of different organisms and several other applications. |
- R. Durbin, S. Eddy, A. Krogh, G. Mitchison, "Biological Sequence Analysis", Cambridge University (Original title)
- N. Jones, P. Pevzner, "An Introduction to Bioinformatics Algorithms", MIT Press (Original title)
- D. Gusfield, "Algorithms on Strings, Trees and Sequences", Cambridge University Press (Original title)
- Najnoviji radovi po izboru predavača (Original title)
- Heumos, L., Schaar, A.C., Lance, C. et al. Best practices for single-cell analysis across modalities. Nat Rev Genet (2023) (Original title)
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