What 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 2019

In 2019, our group has prepared the release of our new online resource dedicated to microbes in their natural environments. This new resource ("microbeatlas.org") is based on integrated DNA-based microbial survey data from a large variety of published datasets. We re-process all sequences by means of a standardised pipeline and against a common collection of reference sequences. This yields one of most comprehensive publicly available set of microbial species definitions ("operational taxonomic units"), mapped to the globe and annotated to broadly defined environment categories. We have already used this data to study higher-order structure in the ecological interaction network (Tackmann et al). Apart from MicrobeAtlas, the group has continued its work on the protein-protein interaction database STRING (preparing the upcoming up to version 12 with more than 10'000 genomes covered).

Find out more about the Group’s activities

Main publications 2019

  • Tackmann J et al.
    Rapid inference of direct interactions in large-scale ecological networks from heterogeneous microbial sequencing data
    Cell Systems, https://doi.org/10.1016/j.cels.2019.08.002
  • Szklarczyk D et al.
    STRING v11: protein–protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets
    Nucleic Acids Res, https://doi.org/10.1093/nar/gky1131
  • Heller D et al.
    Tree reconciliation combined with subsampling improves large scale inference of orthologous group hierarchies
    BMC Bioinformatics, https://doi.org/10.1186/s12859-019-2828-z
In Brief

Members

University zurich

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

Domain(s) of activity:

  • Proteins and proteomes
  • Benchmarking
  • Comparative genomics
  • Database curation
  • Data visualisation
  • Deep sequencing data
  • Metagenomics
  • Protein interactions

Domain(s) of application:

  • Basic research

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