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Clustering huge protein sequence sets in linear time

Cited 334 time in Web of Science Cited 370 time in Scopus
Authors

Steinegger, Martin; Soeding, Johannes

Issue Date
2018-06
Publisher
Nature Publishing Group
Citation
Nature Communications, Vol.9 No.1, p. 2542
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 meta-genomic 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.
ISSN
2041-1723
URI
https://hdl.handle.net/10371/202578
DOI
https://doi.org/10.1038/s41467-018-04964-5
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Related Researcher

  • College of Natural Sciences
  • School of Biological Sciences
Research Area Development of algorithms to search, cluster and assemble sequence data, Metagenomic analysis, Pathogen detection in sequencing data

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