The project aims at developing computational methods for integrative modelling of big cancer
data sets, using a range of network inference methods. These inferences will result in
mechanistic gene regulatory networks, and to better understand the biological basis of these,
this position will contribute with pathway analysis using the novel BinoX network crosstalk
method as well as classical methods. A further aim is to make pathway analysis available in the
project web portal.
The goal is also to develop methods for using prior information in gene regulatory network
inference. Previous work has been done on using the FunCoup functional association networks
as a prior, but the goal here is to instead use physical regulatory evidence such as ChIP-Seq or
ATAC-seq which are likely to be more relevant. As such data is hard to simulate in a sensible
way, benchmarking needs to be done on real datasets, using databases such as Trrust or other
compilations of known regulatory links. The project involves programming, data analysis, benchmarking, and modelling, as well as
application of the developed methods to experimental data generated by the project.
Only European citizens are eligible.
The successful candidate should be highly motivated and have an M.Sc. in bioinformatics or related
field, and knowledge of molecular biology. Alternatively, an M.Sc. in molecular biology or
related field and at least 1 year of practical experience in bioinformatics research.
Demonstrable familiarity with network and pathway analysis techniques is essential. Computer
programming (ideally R, Matlab, Python, Perl, Java, C, C++), UNIX skills, and knowledge of
biological database systems are necessary merits.
Send your CV, a cover letter, and the email address of 2 references to Erik.Sonnhammer@scilifelab.se