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Course Review: High-throughput data analysis

The course, BB2491 High-throughput data analysis, is the core of MTLS education at KTH. How does BB2491 differ from traditional biology lectures that we have so far? In this blog I will talk a bit about why, and how we should learn and master high-throughput science!

First, why study high-throughput? It is involved in a myriad of industrial applications, as well as the frontiers of research, to name but a few:

  • Next Generation Sequencing (NGS) is the best illustration of generating and analysing high-throughput data
  • High-throuput compound screening dominates lead discovery for manufacturing of small molecule drug
  • Traditional gene-targeting methods are sufficient for analysis of mendelian diseases, but diseases that involves more complex interplay, requires collecting and analysing “bigger data”

In BB2491, the teaching follows a logical transition from theory, practice to a hand-on project.

THEORY

In biology, high throughput analysis can be split into three parts, namely Genomics, Transcriptomics, and Proteomics. Correspondingly, we have three professors responsible for each part:

        

Lukas Käll                                Lars Arvestad                               Olof Emanuelsson

In contrast to traditional biology lectures, we have no designated text book; alternatively, we have 33 research papers or scientific reviews as mandatory reading materials! It sounds a bit daunting in the beginning, but under the careful guidance of teachers, as well as  fundamental building blocks in previous course (Genomics, Proteomics, Bioinformatics), we are able to dive into the ocean of knowledge!

For example, while Olof introduced the basic concept of RPKM in abundance estimation at the start of the transcriptomics part, it is concluded by four excellent students at the edge of RNA sequencing techniques.

COMPUTER LAB

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