Christian von Mering
Bioinformatics / Systems Biology
University of Zurich
Group Webpage

What do we do?

In the Bioinformatics / Systems Biology Group, we study the dynamics of entire biological systems, both at evolutionary time-scales and at shorter time-scales – down to a few minutes. We often work in close collaboration with laboratory scientists, focusing on the computational aspects of studying such systems, in fields ranging from genetics to genomics and proteomics. In addition, we produce and maintain several online resources for the life science community, including STRING-db (protein networks), EGGNOG-db (gene orthology relations), and PAX-db (protein abundances).

Highlights 2017

In 2017, our group conducted an extensive data-update to our protein-protein interaction database, STRING. This brought the predicted and experimental interaction data up to speed with the latest developments, and allowed users to analyse and annotate their networks with the latest functional subsystems. This and other improvements have been very well received by the users, with more than 4,000 distinct users now working with the database on a daily basis, worldwide. We have also developed a highly parallelized software pipeline for the mapping of 16S rRNA-based microbial reads against reference data, complete with the most comprehensive available catalogue ("MAPseq"). Furthermore, our group has conducted a number of collaborations with experimental and medical labs, including a pioneering study on the use of whole-genome sequencing in diagnosing and managing the disease cystic fibrosis.

Main publications 2017

  • Matias Rodrigues JF et al. MAPseq: highly efficient k-mer search with confidence estimates, for rRNA sequence analysis. Bioinformatics. 2017; 33(23):3808-3810.
  • Feigelman R et al. Sputum DNA sequencing in cystic fibrosis: non-invasive access to the lung microbiome and to pathogen details. Microbiome. 2017; 5(1):20.
  • Szklarczyk D et al. The STRING database in 2017: quality-controlled protein-protein association networks, made broadly accessible. Nucleic Acids Res. 2017; 45(D1):D362-D368.
In Brief

Our main research topic: