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UniRef: comprehensive and non-redundant UniProt reference clusters
Open accessAbstract
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 approximately 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. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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“The second set contains 1964 unique mammalian protein complexes filtered against the IntAct43 dataset from Negatome31.”
From Improved prediction of protein-protein interactions using AlphaFold2 (2021) · cites this paper as [43]
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Genome reduction in an abundant and ubiquitous soil bacterium ‘Candidatus Udaeobacter copiosus’
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Metatranscriptome of human faecal microbial communities in a cohort of adult men
Abu-Ali, G., Mehta, R. S., Lloyd‐Price, J., Mallick, H., Branck, T. A., Ivey, K. L., Drew, D. A., DuLong, C. R., Rimm, E. B., Izard, J., et al. (2018) · Nature Microbiology · doi:10.1038/s41564-017-0084-4 · Cited by 238
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Protein Bioinformatics Databases and Resources
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Self-citationMegaphages infect Prevotella and variants are widespread in gut microbiomes
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Large-scale determination of previously unsolved protein structures using evolutionary information
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Challenges and perspectives of metaproteomic data analysis
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Extraordinary phylogenetic diversity and metabolic versatility in aquifer sediment
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Ultra-deep sequencing of Hadza hunter-gatherers recovers vanishing gut microbes
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Metadata
- Authors
- Suzek, B. E., Huang, H., McGarvey, P., Mazumder, R., & Wu, C. H.
- Affiliations
- Protein Information Resource, Department of Biochemistry and Molecular & Cellular Biology, Georgetown University Medical Center, Washington, DC 20007, USA
- Georgetown University Medical Center (US)
- Venue
- Bioinformatics
- Publisher
- Oxford University Press (OUP)
- Published
- 2007-03-22
- Volume
- 23
- Issue
- 10
- Pages
- 1282-1288
- ISSN
- 1367-4803 (ISSN-L)
- 1367-4811
- OpenAlex
- W2076048958
- Cited by
- 1,613
- References
- 51
- Citations by year
- 2026: 137 · 2025: 197 · 2024: 164 · 2023: 132 · 2022: 93 · 2021: 84 · 2020: 68 · 2019: 69 · 2018: 55 · 2017: 74 · 2016: 87 · 2015: 91 · 2014: 64 · 2013: 82 · 2012: 63
- FWCI
- 13.26
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by-nc
- Locations
- Bioinformatics (publishedVersion, cc-by-nc)
- PubMed (publishedVersion)
- Funders
- European Bioinformatics Institute
- National Institutes of Health
- National Human Genome Research Institute (U01HG02712, U01HG002712)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Physical Sciences › Chemistry › Spectroscopy › Advanced Proteomics Techniques and Applications
- Keywords
- protein sequence clustering, sequence similarity search, protein sequence space, distant homology detection, functional annotation
- MeSH terms
- Amino Acid Sequence, Animals, Humans, Proteins, Information Storage and Retrieval, Computational Biology, Databases, Protein
About these checks
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.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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