Introduction to bioinformatics 2 1000-714BI2
The following topics will be covered in this class:
• Python programming basics, libraries (Pandas, Seaborn, Numpy, BioPython), visualization and processing of large amounts of data
• Homology, analogy and coevolution in the context of protein sequences and structures, concepts of protein families and domains and how to classify them
• Sequence and structure databases (AFDB, ECOD, UniProt), accessing databases from Python scripts
• Sequence methods (HHsuite, HMMER, MMseqs2, CCmpred), homology detection, function determination and protein classification
• Structural methods (AlphaFold, FoldSeek, PyMol), modelling of oligomers, protein-protein interactions and fibrous structures
• Protein language models, embeddings and tools based on them, such as pLM-BLAST. tSNE/UMAP visualization techniques
• Carry out your own experiment based on the knowledge gained. Design from a pool of available designs or self-invented
Course coordinators
Type of course
Learning outcomes
Advanced understanding and application of different bioinformatics methods and tools available as standalone programs suits or as a web services.
Assessment criteria
• Laboratories: Work in small groups to complete a project and present the results.
• Lecture: A passing grade in the laboratories allows you to take the exam (test), which covers theoretical knowledge (lecture) and practical skills (labs).
Bibliography
- Branden, C., & Tooze, J. (1999). Introduction to protein structure (2nd ed.). Garland Science.
- Cheng, H., Schaeffer, R. D., Liao, Y., Kinch, L. N., Pei, J., et al. (2014).
ECOD: An evolutionary classification of protein domains. PLOS Computational Biology, 10(12), e1003926.
Additional information
Information on level of this course, year of study and semester when the course unit is delivered, types and amount of class hours - can be found in course structure diagrams of apropriate study programmes. This course is related to the following study programmes: