SylabUZ
| Course name | Elements of bioinformatics |
| Course ID | 13.1-WF-FizD-EB-S17 |
| Faculty | Faculty of Physics and Astronomy |
| Field of study | Physics |
| Education profile | academic |
| Level of studies | Second-cycle studies leading to MS degree |
| Beginning semester | winter term 2019/2020 |
| Semester | 3 |
| ECTS credits to win | 4 |
| Available in specialities | Medical Physics |
| Course type | obligatory |
| Teaching language | english |
| Author of syllabus |
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| The class form | Hours per semester (full-time) | Hours per week (full-time) | Hours per semester (part-time) | Hours per week (part-time) | Form of assignment |
| Lecture | 15 | 1 | - | - | Exam |
| Laboratory | 30 | 2 | - | - | Credit with grade |
To familiarize the student with the theoretical, computational and practical elements of bioinformatics. Preparation for work at a bioinformatics laboratory either in a medical healthcare center or a research facility.
Knowledge of the elements of probability theory, programming and mathematical methods of bioinformatics. The ability to programi in either Python or R
Fundamentals of genes and genomes.
Fundamentals of molecular evolution
Genomic technologies
Data, databases, formats, search and retrieval / genome browsers
Large volume data analysis in bioinformatics / big data in bioinformatics
Sequencie alignment and similarity search
Sequencing
Microarrays analysis
Protein structure
In the laboratory the students will carry out programming exercises covering the above topics in the Python or R programming languages.
Lectures on problems and discussions. Laboratory, programming asignments and projects.
| Outcome description | Outcome symbols | Methods of verification | The class form |
LECTURE: A course credit for the lectures is obtained by taking a final exam composed of tasks of varying degrees of difficulty.
Laboratory: During the laboratory the students will be given a series of open-ended projects covering the lectures.
Overall rating: 40% the result of the exam and 60% of the grades achieved for the laboratory projects.
[1] Supratim Choudhuri, Bioinformatics for Beginners: Genes, Genomes, Molecular Evolution, Databases and Analytical Tools,
[2] Phillip Compeau and Pavel Pevzner, Bioinformatics Algorithms: An Active Learning Approach, 2nd Ed
Modified by dr hab. Maria Przybylska, prof. UZ (last modification: 30-04-2020 22:38)
