Course syllabus


BBT045 BBT045 Applied bioinformatics lp3 VT24 (7.5 hp)

Course is offered by the department of Life Sciences

Contact details

  • Johan Bengtsson-Palme – examiner, lecturer
  • Aleksej Zelezniak – lecturer (Reproducible research)
  • Sandra Viknander – teaching assistant, lecturer (HMMs)
  • Marcus Wenne –  teaching assistant, lecturer (algorithmic thinking)
  • Vi Varga – teaching assistant, lecturer (phylogeny)

Course purpose

The course aims at providing applied knowledge behind bioinformatics methods used for biological sequence analysis, gives hands-on experience in practical analysis of next-generation sequencing (NGS) data. The student should gain a critical view on bioinformatics methods, be able to conduct reproducible research in computational biology projects and become familiar with sequence analysis.




Course design

The course will be mix of lectures, flipped classroom seminars ( Links to an external site.), practical exercises, individual assignments as home works and a team work project. During the lectures the instructors will teach you new theoretical concepts that will be practiced in class or through homework assignments. Everyone learns differently and has her/his own learning strategies, therefore during the flipped classroom, you will be following the provided material, including papers and videos, at your own pace, and at the following seminar the content will be summarised and discussed. The homework assignments will be mainly to practice your programming skills and introduce you to algorithmic thinking. The project work will provide a hands on experience in practical bioinformatics and will demonstrate the importance of reproducible science. 

All the communications and practical information, including slides and literature will be posted on here on Canvas. Homeworks, tutorials. exercises and study materials are posted on course webpage:

Learning objectives and syllabus

Learning objectives:

  • Apply and implement established bioinformatics methods used for biological sequence analysis, including pairwise sequence alignment, multiple sequence alignment and its evolutionary aspects.
  • Apply established techniques for mapping of NGS reads to reference genomes, understand and apply efficient sequence similarity searches.
  • Describe and apply methods for de novo sequence assembly of data generated by next generation DNA sequencing.
  • Apply and implement methods to predict genes and their functions and perform annotation of DNA sequences. Special emphasis will be given to Hidden Markov Models (HMM).
  • Discuss computational issues in analysing sequence data on small and large scale, including algorithmic limitations and the need for heuristic approaches.
  • Discuss and apply gene set enrichment analysis and how it can be used to biologically interpret results from omics data.
  • Discuss and apply different methods to combine omics data from multiple platforms.

Link to the syllabus on Studieportalen.

Study plan

Examination form

Written Exam, team project (compulsory) and individual homeworks (compulsory). 

Course summary:

Date Details Due