BB2491 Analysis of Data from High-Throughput Molecular Biology Experiments 7.0 credits
Analys av data från storskaliga molekylärbiologiska experiment
Educational levelSecond cycle
Academic level (A-D)
Grade scaleA, B, C, D, E, FX, F
Autumn 17 P2 (7.0 credits)
2017 week: 44
2018 week: 3
Language of instruction
Number of lectures
Number of exercises
Form of study
Number of places *
*) The Course date may be cancelled if number of admitted are less than minimum of places.
Lukas Käll <email@example.com>
Olof Emanuelsson <firstname.lastname@example.org>
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.
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, grade scale: P, F
- PRO2 - Project, 6.0, grade 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.
Lukas Käll <email@example.com>
Course syllabus valid from: Autumn 2017.
Examination information valid from: Autumn 2017.