Computational Methods for Functional Genomics

The course starts by presenting microarray technologies and the preprocessing steps required before any biological investigation can be carried out. Next, I introduce methods basically extending the pre-genomics, low-throughput gene expression experiments to genome-wide screens. Then the course unfolds with the presentation of recent tools that operate, not at the level of individual gene, but at the level of functionally related gene sets and global molecular phenotypes.

Understand how and why functional genomics contributes to transform our understanding of biological systems. Provide critical assessment of the functional genomics literature. Acquire practical know how of basic concept of tools.

The course does not present a nicely polished texbook view of science, but science in the making with its ectics, turn arounds and controversies. Lectures are interupted by hands-on exercises in which the student actually use the methods on real life data and reproduce published research results.

Contribution to the teaching profile

Functional genomics is the study of the genome and the deployement of its products in living systems. The key feature of functional genomics assays is their exhaustiveness. Instead of focusing on particular genes or biological function, functional genomics investigation addresses all genes and all functions at once. This results in massive data generation and requires specific computational approaches.

The course focuses on genome-wide mRNA gene expression, i.e. the first and most tractable level of genomic information deployement. It will present the strenghts and limits of computational methods deployed in current functional genomics research. The objective is to promote critical reading of the literature in the field, to introduce tools and promote their creative but biologically relevant use to tackle real life research problems. Thus, the course is biologically oriented: I focus on research applications. Details on the mathematical underpinning of the methods I present are addressed in other courses of the master, for example the statistics and machine learning modules.

The course most specifically addresses the teaching goals 1.1, 1.2,1.5, 2.1-3, 3.1-4, 4.1, 4.2, 5.1 and 5.3 of the Master.

References, bibliography, and recommended reading

NA

Contacts

vdetours@ulb.ac.be

Method(s) of evaluation

• Other

Other

The student will be evaluated from a personal project.

Mark calculation method (including weighting of intermediary marks)

NA

Language(s) of evaluation

• french
• english