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  Clustering huge protein sequence sets in linear time.

Steinegger, M., & Söding, J. (2018). Clustering huge protein sequence sets in linear time. Nature Communications, 9(1): 2542. doi:10.1038/s41467-018-04964-5.

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Item Permalink: http://hdl.handle.net/21.11116/0000-0001-9887-B Version Permalink: http://hdl.handle.net/21.11116/0000-0001-988C-6
Genre: Journal Article

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 Creators:
Steinegger, M.1, Author              
Söding, J.1, Author              
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1Research Group of Computational Biology, MPI for Biophysical Chemistry, Max Planck Society, ou_1933286              

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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 millions 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 h 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.

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Language(s): eng - English
 Dates: 2018-06-29
 Publication Status: Published online
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 Rev. Method: Peer
 Identifiers: DOI: 10.1038/s41467-018-04964-5
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Title: Nature Communications
Source Genre: Journal
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Pages: 8 Volume / Issue: 9 (1) Sequence Number: 2542 Start / End Page: - Identifier: -