Publications of Johannes Söding

Basu, S.; Zhao, B.; Biró, B.; Faraggi, E.; Gsponer, J.; Hu, G.; Kloczkowski, A.; Malhis, N.; Mirdita, M.; Söding, J. et al.; Steinegger, M.; Wang, D.; Wang, K.; Xu, D.; Zhang, J.; Kurgan, L.: DescribePROT in 2023: more, higher-quality and experimental annotations and improved data download options. Nucleic Acids Research, gkad985 (2023)

Stitzinger, S. H.; Sohrabi-Jahromi, S.; Söding, J.: Cooperativity boosts affinity and specificity of proteins with multiple RNA-binding domains. NAR: genomics and bioinformatics 5 (2), lqad057 (2023)

van Kempen, M.; Kim, S. S.; Tumescheit, C.; Mirdita, M.; Lee, J.; Gilchrist, C. L. M.; Söding, J.; Steinegger, M.: Fast and accurate protein structure search with Foldseek. Nature Biotechnology (2023)

Raghavan, V.; Eichele, G.; Larink, O.; Levy Karin, E.; Söding, J.: RNA sequencing indicates widespread conservation of circadian clocks in marine zooplankton. NAR: genomics and bioinformatics 5 (1), lqad007 (2023)

Luebben, A. V.; Bender, D.; Becker, S.; Crowther, L. M.; Erven, I.; Hofmann, K.; Söding, J.; Klemp, H.; Bellotti, C.; Stäuble, A. et al.; Qiu, T.; Kathayat, R. S.; Dickinson, B. C.; Gärtner, J.; Sheldrick, G. M.; Krätzner, R.; Steinfeld, R.: Cln5 represents a new type of cysteine-basedS-depalmitoylase linked to neurodegeneration. Science Advances 8 (15), eabj8633 (2022)

Qi, Z.; Jung, C.; Bandilla, P.; Ludwig, C.; Heron, M.; Sophie Kiesel, A.; Museridze, M.; Philippou‐Massier, J.; Nikolov, M.; Renna Max Schnepf, A. et al.; Unnerstall, U.; Ceolin, S.; Mühlig, B.; Gompel, N.; Söding, J.; Gaul, U.: Large-scale analysis of Drosophila core promoter function using synthetic promoters. Molecular Systems Biology 18 (2), e9816 (2022)

Zhang, R.; Mirdita, M.; Levy Karin, E.; Norroy, C.; Galiez, C.; Söding, J.: SpacePHARER: sensitive identification of phages from CRISPR spacers in prokaryotic hosts. Bioinformatics 37 (19), pp. 3364 - 3366 (2021)

Mirdita, M.; Steinegger, M.; Breitwieser, F.; Söding, J.; Levy Karin, E.: Fast and sensitive taxonomic assignment to metagenomic contigs. Bioinformatics 37 (18), pp. 3029 - 3031 (2021)

Sohrabi-Jahromi, S.; Söding, J.: Thermodynamic modeling reveals widespread multivalent binding by RNA-binding proteins. Bioinformatics 37, pp. i308 - i316 (2021)

Sohrabi-Jahromi, S.; Söding, J.: Thermodynamic modeling reveals widespread multivalent binding by RNA-binding proteins. Bioinformatics 37 (Suppl. 1), pp. i308 - i316 (2021)

Aevarsson, A.; Kaczorowska, A.-K.; Adalsteinsson, B. T.; Ahlqvist, J.; Al-Karadaghi, S.; Altenbuchner, J.; Arsin, H.; Átlasson, Ú. Á.; Brandt, D.; Cichowicz-Cieślak, M. et al.; Cornish, K. A. S.; Courtin, J.; Dabrowski, S.; Dahle, H.; Djeffane, S.; Dorawa, S.; Dusaucy, J.; Enault, F.; Fedøy, A.-E.; Freitag-Pohl, S.; Fridjonsson, O. H.; Galiez, C.; Glomsaker, E.; Guérin, M.; Gundesø, S. E.; Gudmundsdóttir, E. E.; Gudmundsson, H.; Håkansson, M.; Henke, C.; Helleux, A.; Henriksen, J. R.; Hjörleifdóttir, S.; Hreggvidsson, G. O.; Jasilionis, A.; Jochheim, A.; Jónsdóttir, I.; Jónsdóttir, L. B.; Jurczak-Kurek, A.; Kaczorowski, T.; Kalinowski, J.; Kozlowski, L. P.; Krupovic, M.; Kwiatkowska-Semrau, K.; Lanes, O.; Lange, J.; Lebrat, J.; Linares-Pastén, J.; Liu, Y.; Lorentsen, S. A.; Lutterman, T.; Mas, T.; Merré, W.; Mirdita, M.; Morzywołek, A.; Ndela, E. O.; Karlsson, E. N.; Olgudóttir, E.; Pedersen, C.; Perler, F.; Pétursdóttir, S. K.; Plotka, M.; Pohl, E.; Prangishvili, D.; Ray, J. L.; Reynisson, B.; Róbertsdóttir, T.; Sandaa, R.-A.; Sczyrba, A.; Skírnisdóttir, S.; Söding, J.; Solstad, T.; Steen, I. H.; Stefánsson, S. K.; Steinegger, M.; Overå, K. S.; Striberny, B.; Svensson, A.; Szadkowska, M.; Tarrant, J.; Terzian, P.; Tourigny, M.; van den Bergh, T.; Vanhalst, J.; Vincent, J.; Vroling, B.; Walse, B.; Wang, L.; Watzlawick, H.; Welin, M.; Werbowy, O.; Wons, E.; Zhang, R.: Going to extremes: A metagenomic journey into the dark matter of life. FEMS Microbiology Letters 368 (12), fnab067 (2021)

Banerjee, S.; Simonetti, F.L.; Detrois, K. E.; Kaphle, A.; Mitra, R.; Nagial, R.; Söding, J.: Tejaas: reverse regression increases power for detecting trans-eQTLs. Genome Biology 22, 142 (2021)

Ge, W.; Meier, M.; Roth, C.; Söding, J.: Bayesian Markov models improve the prediction of binding motifs beyond first order. NAR: Genomics and Bioinformatics 3 (2), lquab026 (2021)

Choi, J.; Lysakovskaia, K.; Stik, G.; Demel, C.; Söding, J.; Tian, T. V.; Graf, T.; Cramer, P.: Evidence for additive and synergistic action of mammalian enhancers during cell fate determination. eLife 10, e65381 (2021)

Zhao, B.; Katuwawala, A.; Oldfield, C. J.; Dunker, A. K.; Faraggi, E.; Gsponer, J.; Kloczkowski, A.; Malhis, N.; Mirdita, M.; Obradovic, Z. et al.; Soding, J.; Steinegger, M.; Zhou, Y.; Kurgan, L.: DescribePROT: database of amino acid-level protein structure and function predictions. Nucleic Acids Research 49 (D1), pp. D298 - D308 (2021)

Gabler, F.; Nam, S.-Z.; Till, S.; Mirdita, M.; Steinegger, M.; Söding, J.; Lupas, A. N.; Alva, V.: Protein sequence analysis using the MPI Bioinformatics Toolkit. Current Protocols in Bioinformatics 72 (1), e108 (2020)

Erijman, A.; Kozlowski, L.; Sohrabi-Jahromi, S.; Fishburn, J.; Warfield, L.; Schreiber, J.; Noble, W. S.; Söding, J.; Hahn, S.: A high-throughput screen for transcription activation domains reveals their sequence features and permits prediction by deep learning (Correction). Molecular Cell 79 (6), p. 1066 (2020)

Erijman, A.; Kozlowski, L. P.; Sohrabi-Jahromi, S.; Fishburn, J.; Warfield, L.; Schreiber, J.; Noble, W. S.; Söding, J.; Hahn, S.: A high-throughput screen for transcription activation domains reveals their sequence features and permits prediction by deep learning. Molecular Cell 78 (5), pp. 890 - 902.e6 (2020)

Levy Karin, E.; Mirdita, M.; Söding, J.: MetaEuk—sensitive, high-throughput gene discovery, and annotation for large-scale eukaryotic metagenomics. Microbiome 8, 48 (2020)

Söding, J.; Zwicker, D.; Sohrabi-Jahromi, S.; Boehning, M.; Kirschbaum, J.: Mechanisms for active regulation of biomolecular condensates. Trends in Cell Biology 30 (1), pp. 4 - 14 (2020)