Quantitative and Computational Biology

Quantitative and Computational Biology

Our group develops statistical and computational methods for analyzing data from high-throughput biological experiments. Our work is focussed on protein function and structure prediction, sequence search and assembly in metagenomics, transcription regulation, gene regulatory networks, and systems medicine.

Google scholar profile for Johannes Söding 

Selected publications:

Steinegger, M., Mirdita, M., and Söding, J. (2018) Protein-level assembly increases protein sequence recovery from metagenomic samples manyfold. bioRxiv 386110.  https://doi.org/10.1101/386110

Banerjee, S., Zeng, L., Schunkert, H., and Söding, J. (2018) Bayesian multiple logistic regression for GWAS analysis.  bioRxiv  https://doi.org/10.1101/198911

Vorberg, S., Seemayer, S. and Söding, J. (2018) Synthetic protein alignments by CCMgen quantify noise in residue-residue contact prediction.  PLoS Comput. Biol., accepted. bioRxiv  https://doi.org/10.1101/344333

Steinegger, M., and Söding, J. (2018) Clustering huge protein sequence sets in linear time. Nature Commun. 9, 2542.  https://doi.org/10.1101/104034

Steinegger, M., and Söding, J. (2017) MMseqs2 enables sensitive protein sequence searching for the analysis of massive data sets. Nature Biotechnol. 35, 1026–1028.  https://doi.org/10.1038/nbt.3988

Söding, J. (2017) Big-data approaches to protein structure prediction. Science (perspective), 355, 248-249. https://doi.org/10.1126/science.aal4512

Siebert M. and Söding, J. (2016) Markov models consistently outperform PWMs atpredicting regulatory motifs in nucleotide sequences. Nucleic Acids Res., 44, 6055-6069. https://doi.org/10.1093/nar/gkw521

Baejen, C.,# Andreani, J.,# Torkler, P., Battaglia, S., Schwalb, B., Lidschreiber, M., Maier, K.C., Boltendahl, A., Rus, P., Esslinger, S., Söding, J.*, and Cramer, P.* (2017) Genome-wide analysis of RNA polymerase II termination at protein-coding genes. Molecular Cell 66, 38-49.e6.  https://doi.org/10.1016/j.molcel.2017.02.009

Meier, A. and Söding, J. (2015) Automatic prediction of protein 3D Structures by probabilistic multi-template homology modeling. PLoS Comput. Biol., 11:e1004343. doi: 10.1371/journal.pcbi.1004343

Siebert, M. and Söding, J. (2014) Universality of core promoter motifs? Nature (Brief Commun. Arising), 511, E11–E12. https://doi.org/10.1038/nature13587

Schulz, D.#, Schwalb, B.#, Kiesel, A., Baejen, C., Torkler, P., Gagneur, J., Söding,J.* and Cramer, P.* (2013) Transcriptome surveillance by selective termination of noncoding RNA synthesis. Cell, 155, 1075-1087. https://doi.org/10.1016/j.cell.2013.10.024

Hartmann, H., Guthohrlein, E. W., Siebert, M., Luehr, S., and Söding, J. (2013)  P-value based regulatory motif discovery using positional weight matrices. Genome Res. 23, 181-194. https://doi.org/10.1101/gr.139881.112

Remmert, M., Biegert, A., Hauser, A., and Söding, J. (2012) HHblits: Lightning-fast iterative protein sequence searching by HMM-HMM alignment. Nat. Methods, 9, 173-175. https://doi.org/10.1038/nmeth.1818

Biegert, A. and Söding, J. (2009) Sequence context-specic amino acid similarities for homology searching. Proc. Natl. Acad. Sci. USA, 106, 3770-3775. https://doi.org/10.1073/pnas.0810767106

Söding, J.*, Biegert, A., and Lupas, A. N. (2005) The HHpred interactive server for protein homology detection and structure prediction. Nucleic Acids Res., 33, W244-W248. https://doi.org/10.1093/nar/gki408.

Söding, J. (2005) Protein homology detection by HMM-HMM comparison. Bioinformatics, 21, 951-960. https://doi.org/10.1093/bioinformatics/bti125

(#Equal contributions. *Corresponding authors.)

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