Science for Life seminars – Campus Solna: Johannes Söding


Tuesday, June 4 at 15:00

Prof. Johannes Söding

Max Planck Institute for Biophysical Chemistry, Germany

After his PhD and postdoc in experimental physics and a 3 year stint at a consulting company, Johannes started as staff scientist with A. Lupas in Tuebingen in 2002, working on protein evolutin and structure prediction. He startet his own group in 2007 in Munich, and since 2014 is a group leader for Quantitiative Biology and Bioinformatics at the Max-Planck institute in Goettingen.

Title: Bioinformatics tools for the age of metagenomics 

Metagenomics allows us to study microbes and phages in their natural environment without the need for cultivation. It is revolutionizing our understanding of the impacts of our gut microbiomes on our healt and the biochemical and geological processes that microbes catalyse in the environment. However, the deluge of data is challenging previous analysis tools, and the analysis of metagenomic datasets is now the main time and cost bottleneck. We present three methods that together allow us to move from an experiment-by-experiment analysis to large-scale analyses of thousands of metagenomic datasets. I will explain the critical ideas leading to the success of the presented methods, in particular the design goal to maximize the utilisation of information and how to reduce runtime complexity in sequence clustering from quadratic to linear.
[1] Steinegger M and Söding J. MMseqs2 enables sensitive protein sequence searching for the analysis of massive data sets. Nature Biotechnology (2017). https://doi.org/10.1038/nbt.3988 
[2] Steinegger M and Söding J. Clustering huge protein sequence sets in linear time. Nature Communications (2018). https://www.nature.com/articles/s41467-018-04964-5
[3] Steinegger M, Mirdita, and Söding J. Protein-level assembly increases protein sequence recovery from metagenomic samples manyfold. Nature Methods (accepted). https://doi.org/10.1101/386110
Host: Arne Elofsson