Soeding, Johannes, Dr.

Research Group Leader at the Max Planck Institute for Biophysical Chemistry

  • 1992 Diploma in physics at the University of Heidelberg
  • 1996 PhD in physics at the University of Heidelberg
  • 1996 – 1998 Post-doc with C. Cohen-Tannoudji and J. Dalibard at the École Normale Supérieure in Paris
  • 1999 – 2002 Strategy management consultant for the Boston Consulting Group in Frankfurt
  • 2002 – 2007 Staff scientist with Andrei Lupas at the Max-Planck-Institute for Developmental Biology in Tuebingen
  • 2007 – 2013 Group leader at the Gene Center and Department of Biochemistry, University of Munich (LMU)
  • since 2014 Group Leader of the Computational Biology Group at the Max Planck Institute of Biophysical Chemistry

Major Research Interests

Computational Biology Research Group

We are interested in two broad areas of research. First, we develop computational methods for predicting the structure, function, and evolution of proteins from sequence. We develop statistical methods that enable us to make use of the vast amount of sequence information that is becoming available at an ever-increasing pace. The goal is to provide life scientists with more and more powerful tools for predicting the functions and structures of proteins in order to guide their experimental work.
Second, we want to understand how transcriptional regulation, which represents the most important level of cellular regulation, is encoded in each gene's regulatory regions. We develop computational methods to analyse regulatory sequences and to detect regulatory motifs. We also want to predict transcription rates, using probabilistic modeling, statistical physics, and machine learning techniques. We collaborate extensively with experimental groups to elucidate the molecular processes regulating transcription initiation, elongation, mRNA processing, and chromatin states.

Homepage Department / Research Group

Selected Recent Publications

  • Banerjee S, Zeng L, Schunkert H, Söding J (2018) Bayesian multiple logistic regression for meta-analyses of GWAS. PLoS Genet 14:e1087856

  • Steinegger M, Söding J (2018) Clustering huge protein sequence sets in linear time. Nature Commun 9:2542-2550

  • Steinegger M, Söding J (2017) MMseqs2 enables sensitive protein sequence searching for the analysis of massive data sets. Nature Biotechnol. 35:1026-1028

  • Baejen C, Andreani J, Torkler P, Battaglia S, Schwalb B, Lidschreiber M, Maier K, Boltendahl A, Rus P, Esslinger S, Söding J, Cramer P (2017) Genome-wide analysis of RNA polymerase II termination at protein-coding genes. Mol Cell 66:38-49

  • Siebert M, Söding J (2016) Bayesian Markov models consistently outperform PWMs at predicting motifs in nucleotide sequences. Nucleic Acids Res 44:6055-6069