C2144 Python for bioinformaticians

Faculty of Science
Spring 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)
Ing. Daniel Kříž (assistant)
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
Timetable
Mon 16. 2. to Fri 22. 5. Fri 9:00–10:50 B11/305
  • Timetable of Seminar Groups:
C2144/01: Mon 16. 2. to Fri 22. 5. Fri 11:00–12:50 C04/118, D. Kříž
C2144/02: Mon 16. 2. to Fri 22. 5. Fri 14:00–15:50 C04/118, D. Kříž
Prerequisites
Elementary knowledge of Python and basic algorithms.
Course Enrolment Limitations
The course is offered to students of any study field.
Abstract
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.
Key topics
  • 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.
Study resources and 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
Approaches, practices, and methods used in teaching
Lectures and practical exercises, small team project.
Method of verifying learning outcomes and course completion requirements
Compulsory attendance together with the defence of a small-scale project at the end of the semester.
Language of instruction
Czech
Further Comments
Study Materials
The course is taught annually.
  • Enrolment Statistics (recent)
  • Permalink: https://is.muni.cz/course/sci/spring2026/C2144