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Martin Steinegger
Martin Steinegger
Verified email at snu.ac.kr - Homepage
Title
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Cited by
Year
Highly accurate protein structure prediction with AlphaFold
J Jumper, R Evans, A Pritzel, T Green, M Figurnov, O Ronneberger, ...
Nature, 2021
207842021
ColabFold: making protein folding accessible to all
M Mirdita, K Schütze, Y Moriwaki, L Heo, S Ovchinnikov, M Steinegger
Nature Methods, 679–682, 2022
37102022
MMseqs2 enables sensitive protein sequence searching for the analysis of massive data sets
M Steinegger, J Söding
Nature biotechnology 35 (11), 1026-1028, 2017
19152017
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
1040*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
7992019
Clustering huge protein sequence sets in linear time
M Steinegger, J Söding
Nature communications 9 (1), 2542, 2018
6032018
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
5462016
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
4922020
Fast and accurate protein structure search with Foldseek
M van Kempen, SS Kim, C Tumescheit, M Mirdita, J Lee, CLM Gilchrist, ...
Nature Biotechnology, 1-4, 2023
449*2023
MMseqs2 desktop and local web server app for fast, interactive sequence searches
M Mirdita, M Steinegger, J Söding
Bioinformatics 35 (16), 2856–2858, 2019
3132019
Protein-level assembly increases protein sequence recovery from metagenomic samples manyfold
M Steinegger, M Mirdita, J Söding
Nature Methods 16, 603–606, 2019
2962019
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
2552021
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
223*2020
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
1612021
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
1592016
Terminating contamination: large-scale search identifies more than 2,000,000 contaminated entries in GenBank
M Steinegger, SL Salzberg
Genome Biology 21, 2020
1552020
Metagenome analysis using the Kraken software suite
J Lu, N Rincon, DE Wood, FP Breitwieser, C Pockrandt, B Langmead, ...
Nature Protocols, 2022
1302022
Fast and sensitive taxonomic assignment to metagenomic contigs
M Mirdita, M Steinegger, F Breitwieser, J Söding, E Levy Karin
Bioinformatics, 2021
1232021
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
852020
Unifying the known and unknown microbial coding sequence space
C Vanni, MS Schechter, SG Acinas, A Barberán, PL Buttigieg, ...
Elife 11, e67667, 2022
63*2022
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