Martin Steinegger
Martin Steinegger
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Cited by
Cited by
Highly accurate protein structure prediction with AlphaFold
J Jumper, R Evans, A Pritzel, T Green, M Figurnov, O Ronneberger, ...
Nature, 2021
ColabFold: making protein folding accessible to all
M Mirdita, K Schütze, Y Moriwaki, L Heo, S Ovchinnikov, M Steinegger
Nature Methods, 679–682, 2022
MMseqs2 enables sensitive protein sequence searching for the analysis of massive data sets
M Steinegger, J Söding
Nature biotechnology 35 (11), 1026-1028, 2017
ProtTrans: Towards Cracking the Language of Lifes Code Through Self-Supervised Deep Learning and High Performance Computing
A Elnaggar, M Heinzinger, C Dallago, G Rehawi, Y Wang, L Jones, ...
IEEE Transactions on Pattern Analysis and Machine Intelligence 14 (8), 2021
HH-suite3 for fast remote homology detection and deep protein annotation
M Steinegger, M Meier, M Mirdita, H Vöhringer, SJ Haunsberger, J Söding
BMC Bioinformatics 20, 2019
Clustering huge protein sequence sets in linear time
M Steinegger, J Söding
Nature communications 9 (1), 2542, 2018
Fast and accurate protein structure search with Foldseek
M van Kempen, SS Kim, C Tumescheit, M Mirdita, J Lee, CLM Gilchrist, ...
Nature Biotechnology 42, 243–246, 2023
Uniclust databases of clustered and deeply annotated protein sequences and alignments
M Mirdita, L von den Driesch, C Galiez, MJ Martin, J Söding, M Steinegger
Nucleic Acids Research, 2016
Protein sequence analysis using the MPI bioinformatics toolkit
F Gabler, SZ Nam, S Till, M Mirdita, M Steinegger, J Söding, AN Lupas, ...
Current Protocols in Bioinformatics 72 (1), e108, 2020
MMseqs2 desktop and local web server app for fast, interactive sequence searches
M Mirdita, M Steinegger, J Söding
Bioinformatics 35 (16), 2856–2858, 2019
Protein-level assembly increases protein sequence recovery from metagenomic samples manyfold
M Steinegger, M Mirdita, J Söding
Nature Methods 16, 603–606, 2019
Applying and improving AlphaFold at CASP14
J Jumper, R Evans, A Pritzel, T Green, M Figurnov, O Ronneberger, ...
Proteins: Structure, Function, and Bioinformatics 89 (12), 1711-1721, 2021
High accuracy protein structure prediction using deep learning
J Jumper, R Evans, A Pritzel, T Green, M Figurnov, K Tunyasuvunakool, ...
Fourteenth critical assessment of techniques for protein structure …, 2020
Metagenome analysis using the Kraken software suite
J Lu, N Rincon, DE Wood, FP Breitwieser, C Pockrandt, B Langmead, ...
Nature Protocols, 2022
PredictProtein - Predicting Protein Structure and Function for 29 Years
M Bernhofer, C Dallago, T Karl, V Satagopam, M Heinzinger, M Littmann, ...
Nucleic Acids Research, 2021
MMseqs software suite for fast and deep clustering and searching of large protein sequence sets
M Hauser, M Steinegger, J Söding
Bioinformatics 32 (9), 1323-1330, 2016
Terminating contamination: large-scale search identifies more than 2,000,000 contaminated entries in GenBank
M Steinegger, SL Salzberg
Genome Biology 21, 2020
Fast and sensitive taxonomic assignment to metagenomic contigs
M Mirdita, M Steinegger, F Breitwieser, J Söding, E Levy Karin
Bioinformatics, 2021
Clustering predicted structures at the scale of the known protein universe
I Barrio-Hernandez, J Yeo, J Jänes, M Mirdita, CLM Gilchrist, T Wein, ...
Nature, 2023
ProtTrans: Towards cracking the language of Life’s code through self-supervised deep learning and high performance computing. arXiv 2020
A Elnaggar, M Heinzinger, C Dallago, G Rihawi, Y Wang, L Jones, ...
arXiv preprint arXiv:2007.06225, 2020
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