What we do

In 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 (such as 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.

Highlights 2017

Sven’s Computational Biology Group had a productive year: We continue to explore how to integrate molecular phenotypes, such as gene expression data and metabolomics profiles, with information from genome-wide association studies (GWAS). In our Disease Module Identification DREAM challenge we asked the community for algorithms that partition Genomic Networks and identified those that best reveal subsets of linked genes that play a role in various human traits and diseases, according to GWAS. We also found that Genome-Wide Association between Transcription Factor Expression and Chromatin Accessibility Reveals Regulators of Chromatin Accessibility and contributed to a paper showing that cis-Acting Complex-Trait-Associated lincRNA Expression Correlates with Modulation of Chromosomal Architecture. We also collaborated in a study on High capacity in G-protein-coupled receptor signalling (to be published in Nat Comm soon).

Find out more about the Group’s activities

Main publications 2017

  • Lamparter D et al. Genome-Wide Association between Transcription Factor Expression and Chromatin Accessibility Reveals Regulators of Chromatin Accessibility. PLoS Comput. Biol.: 2017, 13(1);e1005311
  • Yihong Tan J et al. cis-Acting Complex-Trait-Associated lincRNA Expression Correlates with Modulation of Chromosomal Architecture. Cell Rep: 2017, 18(9);2280-2288
  • Rueedi R et al. Metabomatching: Using genetic association to identify metabolites in proton NMR spectroscopy. PLoS Comput Biol 13(12): e1005839.

Members

epfl lausanne

Sven Bergmann
Computational Biology Group
University of Lausanne
Group Webpage

Domains of activity:

  • Genes and genomes
  • Biostatistics
  • DNA Microarrays
  • Epigenetics
  • Gene regulatory network analysis
  • GWAS
  • Metabolomics
  • Protein interactions
  • Software engineering
  • Systems biology
  • Transcriptomics

Domains of application:

  • Medicine and health
  • Basic research

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