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Clustering huge protein sequence sets in linear time

View ORCID ProfileMartin Steinegger, Johannes Söding
doi: https://doi.org/10.1101/104034
Martin Steinegger
1Quantitative and Computational Biology group, Max-Planck Institute for Biophysical Chemistry, Am Fassberg 11, 37077 Göttingen, Germany
2Department for Bioinformatics and Computational Biology, Technische Universität München, 85748 Garching, Germany
3Department of Chemistry, Seoul National University, Seoul, Korea
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  • ORCID record for Martin Steinegger
Johannes Söding
1Quantitative and Computational Biology group, Max-Planck Institute for Biophysical Chemistry, Am Fassberg 11, 37077 Göttingen, Germany
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  • For correspondence: soeding@mpibpc.mpg.de
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Abstract

Metagenomic datasets contain billions of protein sequences that could greatly enhance large-scale functional annotation and structure prediction. Utilizing this enormous resource would require reducing its redundancy by similarity clustering. However, clustering hundreds of million of sequences is impractical using current algorithms because their runtimes scale as the input set size N times the number of clusters K, which is typically of similar order as N, resulting in runtimes that increase almost quadratically with N. We developed Linclust, the first clustering algorithm whose runtime scales as N, independent of K. It can also cluster datasets several times larger than the available main memory. We cluster 1.6 billion metagenomic sequence fragments in 10 hours on a single server to 50% sequence identity, > 1000 times faster than has been possible before. Linclust will help to unlock the great wealth contained in metagenomic and genomic sequence databases. (Open-source software and Metaclust database: https://mmseqs.org/).

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Posted March 26, 2018.
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Clustering huge protein sequence sets in linear time
Martin Steinegger, Johannes Söding
bioRxiv 104034; doi: https://doi.org/10.1101/104034
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Clustering huge protein sequence sets in linear time
Martin Steinegger, Johannes Söding
bioRxiv 104034; doi: https://doi.org/10.1101/104034

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