UniRef: comprehensive and non-redundant UniProt reference clusters

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ID: 292136
2007
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Abstract
Abstract Motivation: Redundant protein sequences in biological databases hinder sequence similarity searches and make interpretation of search results difficult. Clustering of protein sequence space based on sequence similarity helps organize all sequences into manageable datasets and reduces sampling bias and overrepresentation of sequences. Results: The UniRef (UniProt Reference Clusters) provide clustered sets of sequences from the UniProt Knowledgebase (UniProtKB) and selected UniProt Archive records to obtain complete coverage of sequence space at several resolutions while hiding redundant sequences. Currently covering >4 million source sequences, the UniRef100 database combines identical sequences and subfragments from any source organism into a single UniRef entry. UniRef90 and UniRef50 are built by clustering UniRef100 sequences at the 90 or 50% sequence identity levels. UniRef100, UniRef90 and UniRef50 yield a database size reduction of ∼10, 40 and 70%, respectively, from the source sequence set. The reduced redundancy increases the speed of similarity searches and improves detection of distant relationships. UniRef entries contain summary cluster and membership information, including the sequence of a representative protein, member count and common taxonomy of the cluster, the accession numbers of all the merged entries and links to rich functional annotation in UniProtKB to facilitate biological discovery. UniRef has already been applied to broad research areas ranging from genome annotation to proteomics data analysis. Availability: UniRef is updated biweekly and is available for online search and retrieval at http://www.uniprot.org, as well as for download at ftp://ftp.uniprot.org/pub/databases/uniprot/uniref Contact: bes23@georgetown.edu Supplementary information: Supplementary data are available at Bioinformatics online.
Reference Key
openalex_W2076048958 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Barış Ethem Süzek, Hongzhan Huang, Peter B. McGarvey, Raja Mazumder, Cathy Wu
Journal BMC Bioinformatics
Year 2007
DOI
10.1093/bioinformatics/btm098
URL
https://doi.org/10.1093/bioinformatics/btm098
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