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学術論文

Clustering huge protein sequence sets in linear time.

MPS-Authors
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Steinegger,  M.
Research Group of Computational Biology, MPI for Biophysical Chemistry, Max Planck Society;

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Söding,  J.
Research Group of Computational Biology, MPI for Biophysical Chemistry, Max Planck Society;

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フルテキスト (公開)

2609064.pdf
(出版社版), 2MB

付随資料 (公開)

2609064_Suppl_1.pdf
(付録資料), 997KB

2609064_Suppl_2.pdf
(付録資料), 400KB

引用

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


引用: https://hdl.handle.net/21.11116/0000-0001-9887-B
要旨
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.