PřF:C2144 Python for bioinformaticians - Course Information
C2144 Python for bioinformaticians
Faculty of ScienceSpring 2026
- Extent and Intensity
- 1/2/1. 6 credit(s). Type of Completion: zk (examination).
In-person direct teaching - Teacher(s)
- RNDr. Tomáš Raček, Ph.D. (lecturer)
- Guaranteed by
- RNDr. Tomáš Raček, Ph.D.
National Centre for Biomolecular Research – Faculty of Science
Supplier department: National Centre for Biomolecular Research – Faculty of Science - Prerequisites
- Elementary knowledge of Python and basic algorithms.
- Course Enrolment Limitations
- The course is offered to students of any study field.
- Course objectives
- Students will improve their skills in Python programming with emphasis on bioinformatics applications. In addition, they will learn to efficiently process and analyze biological data using Python and relevant libraries as part of smaller projects.
- Learning outcomes
- Upon completion of the course, the student will be able to:
Extend and optimize existing code in Python.
Apply Python to the analysis of biological data and build suitable algorithms.
Access bioinformatics databases and resources programmatically.
Create simple bioinformatics projects from design to implementation. - Syllabus
- Reviewing Python basics with bioinformatics examples
- Introduction to bioinformatics libraries in Python (e.g. BioPython)
- Biological data formats and reading/writing them in Python
- Analysis, filtering and validation of biological data using Python
- Design and implementation of simple projects (modules, packages)
- Using data visualization libraries in Python (matplotlib, seaborn, etc.)
- Use of platforms like Jupyter Notebook, Galaxy.
- Design and presentation of a bioinformatics project and discussion of the goals achieved.
- Literature
- recommended literature
- Mastering Python for Bioinformatics: How to Write Flexible, Documented, Tested Python Code for Research Computing 1st Edition, ISBN: 9781098100889
- McKinney, W. Python for data analysis : [agile tools for real world data]. 1st ed. Sebastopol, Calif.: O'Reilly, 2013. xiii, 452. ISBN 9781449319793
- Teaching methods
- Lectures and practical exercises, small team project.
- Assessment methods
- Compulsory attendance together with the defence of a small-scale project at the end of the semester.
- Language of instruction
- Czech
- Further Comments
- The course is taught annually.
The course is taught every week.
- Permalink: https://is.muni.cz/course/sci/spring2026/C2144