Quantitative and Computational Biology

Google scholar profile for Johannes Söding 

Selected publications:

Söding, J, Zwicker, D, Sohrabi-Jahromi, S, Boehning, M, Kirschbaum, J.  (2020) Mechanisms of active regulation of biomolecular condensates. Trends Cell Biol. 30, 4--14 doi: https://doi.org/10.1016/j.tcb.2019.10.006. bioRxiv: https:/doi.org//10.1101/694406

Erijman, A., Kozlowski, L., Sohrabi-Jahromi, S., Fishburn, J., Warfield, L., Schreiber, J., Noble, W.S., Söding, J^*,  and Hahn, S*. (2020) A high-throughput screen for transcription activation domains reveals their sequence characteristics and permits reliable prediction by deep learning. Mol Cell 78, 890-902. https://doi.org/10.1016/j.molcel.2020.04.020.

Steinegger, M., Mirdita, M., and Söding, J. (2019) Protein-level assembly increases protein sequence recovery from metagenomic samples manyfold. Nature Methods 16, 603–606. https://doi.org/10.1038/s41592-019-0437-4, bioRxiv: https://doi.org/10.1101/386110

Banerjee, S., Zeng, L., Schunkert, H., and Söding, J. (2018) Bayesian multiple logistic regression for GWAS analysis.  PloS Genetics 14, e1007856.  https://doi.org/10.1371/journal.pgen.1007856

Vorberg, S., Seemayer, S. and Söding, J. (2018) Synthetic protein alignments by CCMgen quantify noise in residue-residue contact prediction.  PLoS Comput. Biol.14, e1006526. 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 at predicting 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.)