NBIA07023U Bioinformatics of High Throughput Analyses

Volume 2018/2019

MSc Programme in Molecular Biomedicine
MSc Programme in Bioinformatics
MSc Programme in Biology
MSc Programme in Biology w. minor subject


There are four major subject areas of the course:

  1. Usage of R in applied statistics and data handling: This will be used throughout the course
  2. Visualization, handling and analysis of genomic data using the genome browser, the galaxy tool and R
  3. Expression analysis using microarrays and DNA sequencer data (”tag data”) using R and public tools
  4. Analysis of proteomics data using R and public tools.
Learning Outcome

The student will achieve the following from attending the course:


After successfully completing the course, students will master the fundamentals of computational analysis of large biological datasets. This includes:
i) understanding the diverse laboratory techniques and biological processes generating the data
ii) understanding and mastering the statistical and informatics techniques used for visualization and analysis, including the selection of appropriate techniques for a given data and question
iii) interpreting analysis results in a biological context, and identify and apply follow-up analyses based on this.


The skill set taught in the course can be divided into:

  • Applied statistics, visualization and data handling within R and the Galaxy web tool
  • Knowledge of molecular biology techniques that generate genomics data - cDNA analysis, ChIP, RNA-seq, microarrays, mass spec and more, and their strengths and weaknesses
  • Visualization techniques for the data above: genome browsers and R
  • Techniques for data mining and data exploration

There is a special focus on hands-on exercises to develop analysis skills; both within lessons, group work and in the final evaluation. We also have one day with speakers from industry that use similar techniques.


  • To be able to analyze, visualize and interpret cutting edge biological data sets using biological and statistical toolsets combined.
  • To solve realistic problems in which finding the appropriate methods - and the specific programming syntax necessary - for attacking sub-questions question is an important part of the problem.

See Absalon.

Students should have a molecular biology background corresponding to those of students in Bioinformatics or Biomedicine master programs (for instance "Molecular biology for non-life students" in block 1 or a life-science oriented bachelor education). Moreover, skills in statistics and R corresponding to "Statistics for Molecular Biomedicine" in block 3 is necessary.
Hybrid between lectures and computer exercises.
  • Category
  • Hours
  • Colloquia
  • 3
  • Exam
  • 20
  • Lectures
  • 32
  • Practical exercises
  • 31
  • Preparation
  • 60
  • Project work
  • 60
  • Total
  • 206
7,5 ECTS
Type of assessment
Written assignment, 5 days
Oral examination, 30 minutes (no preparation time)
Exam registration requirements

In order to be allowed to the final exam, the student must have had three smaller written group projects approved.

All aids allowed
Marking scale
7-point grading scale
Censorship form
No external censorship
Several internal examiners/co-examiners

As ordinary exam.

The three smaller written group projects have to be approved not later than 2 weeks before the reexam.

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.