NBIK20004U Advanced Bioinformatics for Next-Generation Sequencing

Volume 2025/2026
Education

MSc Programme in Bioinformatics

Content

The course covers advanced topics in Bioinformatics for analysis of data next-generation sequencing (NGS). The course is based mostly on scientific papers and texts. The topics include:

  • Mapping algorithms
  • Population genetic and medical genetic analysis based on NGS
  • RNA-seq
  • Cross-species comparative genomic analyses
  • Other topics relating to high-throughput DNA sequencing
Learning Outcome

Knowledge:

The student will know and understand the main methods used in the course. More specifically, the student will obtain knowledge of:

  • Analysis of high-throughput sequencing data
  • Genetic analyses
  • Phylogenetic construction
  • Genome evolutionary analyses


Skills:

The student will be able to:

  • Do the types of data analyses covered in the course
  • Interpret results of such analyses in a biological context
  • Explain the methods covered in the course
  • Select the appropriate methods and tools for a given problem covered in the course


Competences:

The student will obtain or improve these general competences:

  • Will be able to discuss and explain methods in bioinformatics with researchers in bioinformatics and related sciences
  • Will be able to contribute to interdisciplinary projects involving biological sequence analysis
  • Will be able to read, understand, and discuss scientific literature in bioinformatics

See Absalon.

Competencies corresponding to 3 out of 4 of the mandatory courses in the bioinformatics graduate programme or equivalent.
Students must be on their second year. The course builds on several components that are taught in the first year, listed below, and is thus recommended for second-year students.

- statistics
- mapping and alignment, incl. Burrows-Wheeler transformation
- R programming for visualization and data manipulation
- Working with expression data (RNAseq)
- experience with analysing short read NGS data
- population genetics, pop structure, admixture
- PCA
While it is of course possible to catch up by studying independently, the course may become rather challenging and time-consuming if all the expected qualifications are new to you.

Overall, unless you are fluent in Python, we recommend that you sign up for NDAB24000U Python Programming for Data Science.

If you have solid python skills and the expected math background, we recommend NDAK22000U Machine Learning A (MLA) or SHUA13043U Course in Bioinformatics and Systems Biology – note that there are detailed descriptions of the recommended qualifications, including a self-assessment.
Lectures and exercises (7 hours per week), and homework (4 in total).
The course is identical to NBIK15022U Advanced Topics in Bioinformatics.
It is not allowed to pass both courses.

The course is prepared for the bioinformatics graduate programme, but it is open to third year BSc students and MSc students in general.
  • Category
  • Hours
  • Lectures
  • 40
  • Preparation
  • 122
  • Practical exercises
  • 24
  • Exam
  • 20
  • Total
  • 206
Written
Individual
Credit
7,5 ECTS
Type of assessment
Continuous assessment
Type of assessment details
Mandatory homework assignments. Grade based on the average number of points obtained in the assignments.
Aid
All aids allowed
Marking scale
7-point grading scale
Censorship form
No external censorship
Several internal examiners
Re-exam

Renewed hand in of mandatory homework assignments.

Criteria for exam assesment

In order to obtain the grade 12 the student should convincingly and accurately demonstrate the knowledge, skills and competences described under Learning Outcome.