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Graduate courses

Departments' graduate courses for PhD-students.


Syllabus for

Academic year
BBT045 - Applied bioinformatics
Tillämpad bioinformatik
Syllabus adopted 2020-02-20 by Head of Programme (or corresponding)
Owner: MPBIO
7,5 Credits
Grading: TH - Pass with distinction (5), Pass with credit (4), Pass (3), Fail
Education cycle: Second-cycle
Major subject: Bioengineering

Teaching language: English
Application code: 08120
Open for exchange students: Yes
Maximum participants: 30

Module   Credit distribution   Examination dates
Sp1 Sp2 Sp3 Sp4 Summer course No Sp
0119 Examination 7,5 c Grading: TH   7,5 c   Contact examiner,  Contact examiner,  Contact examiner

In programs

MPBIO BIOTECHNOLOGY, MSC PROGR, Year 1 (compulsory elective)


Aleksej Zelezniak

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General entry requirements for Master's level (second cycle)
Applicants enrolled in a programme at Chalmers where the course is included in the study programme are exempted from fulfilling the requirements above.

Specific entry requirements

English 6 (or by other approved means with the equivalent proficiency level)
Applicants enrolled in a programme at Chalmers where the course is included in the study programme are exempted from fulfilling the requirements above.

Course specific prerequisites

Basic knowledge of biology and statistics. Programming skills would be beneficial. Students with other background must discuss this with the examiner.


The course aims at providing advanced knowledge behind bioinfomatics methods used for biological sequence analysis, gives hands-on experience in practical analysis of next-generation sequencing (NGS) data. The student should gain a comprehensive view on bioinformatics methods and become familiar with next-generation sequence analysis.

Learning outcomes (after completion of the course the student should be able to)

  • 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.


This is an advance course in bioinformatics, elective within the Biotechnology program. The course aims to provide the students with a practical knowledge and the methodologies used in bioinformatics. Advanced Bioinformatics has a focus on sequence analysis where the underlying algorithms will be studied in more details. Topics include sequence alignment and mapping, sequence assembly, gene prediction and genome annotation. The course also goes into integrative analysis and techniques to combine quantitative data from multiple forms of omics data. The course has several computer exercises which provide students with experience of working with sequence analysis and omics data interpretation. The computer exercises are based on the programming languages Python and R which are introduced to the students throughout the course.

This course will be project oriented with the students working on assignments in order to get hands-on practical experiences. The students will work in Unix environments and use Python and R for programming. Topics for the computer exercises include
  • A brief introductory exercise to get familiar with the Linux environment.
  • How to program in Python for many students providing the first steps into programming.
  • Implementation and application of sequence alignment/mapping algorithms
  • Assembly and annotation of genomes
  • Application of GSA/multi-omics approaches to big omics data. Examples from e.g. TCGA.


The course includes lectures, practical exercises and a project work. For the project, the students will form groups of 2-3 persons and select topic in consultation with the instructor. The results will be presented in the form of a written report as well as an oral presentation.


There is no textbook for this course. Students will be provided with relevant literature and be expected to use available reputable resources on the internet.

Examination including compulsory elements

The final grade will be based on homeworks, team project and written exam.

Page manager Published: Thu 04 Feb 2021.