What do we do?At the Computational Biology Group we develop concepts and algorithmic tools for the analysis of large-scale biological and clinical data. We participate in many genome-wide association studies (GWAS) for human traits and have a particular interest in the integration of genotypic and complex phenotypic datasets (like gene expression or metabolomics). A key approach is the reduction of complexity through modular and network analysis. A complementary direction of our research pertains to relatively small genetic networks whose components are well known. (See our homepage for more details.)
Highlights 2016In 2016, we studied the gene expression response to drugs affecting heart rate and blood pressure across a panel of genetically different mice [BMC Genomics 17:717]. Using our computational software PASCAL for fast and accurate computation of gene and pathway scores from SNP-wise association statistics [PLOS Genet. 12(1):e1005616], we analyzed data from the FANTOM5 project revealing that genetic variants associated with different diseases can be used to identify the relevant tissues enriched for genetic networks that are perturbed by the variants [Nature Methods 13:366]. We also used PASCAL in combination with a large panel of GWAS data for the evaluation of modules that were identified from a set of gene networks by participants of a DREAM challenge we organized.
Main publications 2016
- Andrea Prunotto, Brian J Stevenson, Corinne Berthonneche, Fanny Schüpfer, Jacques S Beckmann, Fabienne Maurer, Sven Bergmann.
RNAseq analysis of heart tissue from mice treated with atenolol and isoproterenol reveals a reciprocal transcriptional response. BMC Genomics 17:717.
- David Lamparter, Daniel Marbach, Rico Rueedi, Zoltán Kutalik, Sven Bergmann. Fast and Rigorous Computation of Gene and Pathway Scores from SNP-Based Summary Statistics. PLOS Comput Biol 12(1):e1004714.
- Daniel Marbach, David Lamparter, Gerald Quon, Manolis Kellis, Zoltán Kutalik, Sven Bergmann. Tissue-specific regulatory circuits reveal variable modular perturbations across complex diseases. Nature Methods 13:366