BB2491 Analysis of Data from High-Throughput Molecular Biology Experiments 7.0 credits

Analys av data från storskaliga molekylärbiologiska experiment

  • Education cycle

    Second cycle
  • Main field of study

  • Grading scale

    A, B, C, D, E, FX, F

Course offerings

Autumn 18 TMTLM for programme students

  • Periods

    Autumn 18 P2 (7.0 credits)

  • Application code


  • Start date


  • End date


  • Language of instruction


  • Campus


  • Tutoring time


  • Form of study


  • Number of places *

    Min. 7

    *) The Course date may be cancelled if number of admitted are less than minimum of places.

  • Schedule

    Schedule (new window)

  • Course responsible

    Olof Emanuelsson <>

  • Teacher

    Olof Emanuelsson <>

Intended learning outcomes

After passing the course, the student should be able to:

1. Describe widely used high-throughput experimental techniques employed to investigate the DNA, RNA and protein contents of a cell, tissue, or organism.

2. Explain the theory of state-of-the-art tools/algorithms for processing data from high-throughput molecular biology experiments.

3. Choose appropriate tools for processing data from high-throughput molecular biology experiments.

4. Apply tools for processing data from high-throughput molecular biology experiments.

5. Interpret the results of these analyses in a biologically or medically relevant context.

6. Reflect over the choice of methods and tools and how it influences the outcome of the analyses.

Course main content

The course contains the fundamental theory of bioinformatics analysis of large data sets from high-throughput genomics, transcriptomics, and proteomics experiments – in particular, massively parallel DNA sequencing and protein mass spectrometry: how this theory is implemented in state-of-the-art tools for analyzing the data; how these tools are applied on real high-throughput molecular biology data; and how the outcome of the analysis may be interpreted in a biologically or medically relevant context.


Admission requirements for programme students at KTH:
At least 150 credits from grades 1, 2 and 3 of which at least 100 credits from years 1 and 2, and bachelor's work must be completed.  The 150 credits should include a minimum of 20 credits within the fields of Mathematics, Numerical Analysis and Computer Sciences, 5 of these must be within the fields of Numerical Analysis and Computer Sciences, 20 credits of Chemistry, possibly including courses in Chemical Measuring Techniques and 20 credits of Biotechnology or Molecular Biology.

Admission requirements for independent students:
A total of 20 university credits (hp) in life science courses ( e.g. biochemistry, microbiology and gene technology/molecular biology). 10 university credits (hp) in mathematics and 3,5 university credits (hp) bioinformatics. Documented proficiency in English corresponding to English B.


Recommended prerequisites

The following courses, or equivalent, are recommended: Bioinformatics and basic probability theory corresponding to BB2440 Bioinformatics and Biostatistics, and probability theory corresponding to SF1901 Probability Theory and Statistics. Computer acquaintance equivalent to the course DD2404 Applied Bioinformatics.


Scientific articles and web resources as assigned during the course. Handouts from the lectures.


  • LAB1 - Computer Exercises, 1.0, grading scale: P, F
  • PRO2 - Project, 6.0, grading scale: A, B, C, D, E, FX, F

No aids are allowed other than those specified in the course PM.

Requirements for final grade

The final grade on the course is determined by the grade on the project (PRO2, grade scale A-F) and the computer exercises (LAB1, grade scale P-F). There are parts of the course that has compulsory attendance.

Offered by

CBH/Gene Technology


Lukas Käll <>


Course syllabus valid from: Autumn 2017.
Examination information valid from: Spring 2019.