BB2490 Analysis of Data from High-throughput Molecular Biology Experiments

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 biochemistry, microbiology and gene technology/molecular biology. 30 university credits (hp)  chemistry, as well as 20 university credits (hp) in mathematics and computer science as well as bioinformatics 3,5 university credits (hp) and statistics 3,5 university credits (hp) or corresponding. Documented proficiency in English corresponding to English B.

The course contains the fundamental theory of, and the use of, bioinformatics analysis of large data sets from high-throughput genomics 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 handling, analyzing, and visualizing 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 medical relevant context.

The course consists of lectures, student-prepared presentations, computer-based laboratory exercises, and a project.

The course is primarily aimed at students at the Biotechnology Master of Science in Engineering Degree program and the master programmes Medical Biotechnology and Molecular Techniques in Life Science.

This is an advanced course in bioinformatics. After passing the course, the student should be able to:

• describe widely used high-throughput experimental techniques employed to investigate the DNA, RNA, and protein contents of a cell, tissue, or organism
• explain the theory of state-of-the-art tools/algorithms for processing data from high-throughput molecular biology experiments.
• choose appropriate methods and tools for processing data from high-throughput molecular biology experiments.
• Use tools for processing data from high-throughput molecular biology experiments.
• interpret the results of the data analyses in a biologically or medical relevant context.
• reflect over the choice of methods and tools and how it influences the outcome of the analyses