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Search and clustering orders of magnitude faster than BLAST
Open accessAbstract
Motivation: Biological sequence data is accumulating rapidly, motivating the development of improved high-throughput methods for sequence classification. Results: UBLAST and USEARCH are new algorithms enabling sensitive local and global search of large sequence databases at exceptionally high speeds. They are often orders of magnitude faster than BLAST in practical applications, though sensitivity to distant protein relationships is lower. UCLUST is a new clustering method that exploits USEARCH to assign sequences to clusters. UCLUST offers several advantages over the widely used program CD-HIT, including higher speed, lower memory use, improved sensitivity, clustering at lower identities and classification of much larger datasets. Availability: Binaries are available at no charge for non-commercial use at http://www.drive5.com/usearch Contact: robert@drive5.com Supplementary information: Supplementary data are available at Bioinformatics online.
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Metadata
- Authors
- Edgar, R. C.
- Venue
- Bioinformatics
- Publisher
- Oxford University Press (OUP)
- Published
- 2010-08-12
- Volume
- 26
- Issue
- 19
- Pages
- 2460-2461
- ISSN
- 1367-4803 (ISSN-L)
- 1367-4811
- OpenAlex
- W2124351063
- Cited by
- 20,826
- References
- 2
- FWCI
- 86.49
- Type
- journal article
- Language
- en
- Open access
- bronze
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.