UniRef clusters: a comprehensive and scalable alternative for improving sequence similarity searches
Open accessAbstract
Abstract Motivation: UniRef databases provide full-scale clustering of UniProtKB sequences and are utilized for a broad range of applications, particularly similarity-based functional annotation. Non-redundancy and intra-cluster homogeneity in UniRef were recently improved by adding a sequence length overlap threshold. Our hypothesis is that these improvements would enhance the speed and sensitivity of similarity searches and improve the consistency of annotation within clusters. Results: Intra-cluster molecular function consistency was examined by analysis of Gene Ontology terms. Results show that UniRef clusters bring together proteins of identical molecular function in more than 97% of the clusters, implying that clusters are useful for annotation and can also be used to detect annotation inconsistencies. To examine coverage in similarity results, BLASTP searches against UniRef50 followed by expansion of the hit lists with cluster members demonstrated advantages compared with searches against UniProtKB sequences; the searches are concise (∼7 times shorter hit list before expansion), faster (∼6 times) and more sensitive in detection of remote similarities (>96% recall at e-value <0.0001). Our results support the use of UniRef clusters as a comprehensive and scalable alternative to native sequence databases for similarity searches and reinforces its reliability for use in functional annotation. Availability and implementation: Web access and file download from UniProt website at http://www.uniprot.org/uniref and ftp://ftp.uniprot.org/pub/databases/uniprot/uniref. BLAST searches against UniRef are available at http://www.uniprot.org/blast/ Contact: huang@dbi.udel.edu
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76.8% of high confidence predictions are separate from UniRef90 by at least 90% sequence identity.
“the vast majority (76.8%) of high confidence predictions being separate from UniRef90 (32) by at least 90% sequence identity”
From Evolutionary-scale prediction of atomic level protein structure with a language model (2022) · cites this paper as [32]
ChocoPhlAn 3 is based on 99.2 k high-quality, fully annotated reference microbial genomes from 16.8 k species available in UniProt Proteomes as of January 2019 and 87.3M UniRef90 gene families.
“ChocoPhlAn 3 is based on a genomic repository of 99.2 k high-quality, fully annotated reference microbial genomes from 16.8 k species available in the UniProt Proteomes portal as of January 2019 (The UniProt Consortium, 2019) and the corresponding functionally-annotated 87.3M UniRef90 gene families (Suzek et al., 2015).”
From Integrating taxonomic, functional, and strain-level profiling of diverse microbial communities with bioBakery 3 (2021) · cites this paper as Suzek et al., 2015
UniRef90 clusters have at least 90% sequence identity.
“Sequences in UniRef90 clusters have at least 90% sequence identity (Suzek et al., 2015).”
From Integrating taxonomic, functional, and strain-level profiling of diverse microbial communities with bioBakery 3 (2021) · cites this paper as Suzek et al., 2015
The ESM-1b Transformer Network was trained with about 27 million protein sequences from UniRef50 to create 1280-dimensional numerical protein vectors.
“The Facebook AI Research team trained such a Transformer Network, called ESM-1b, with a dataset of ~27 million protein sequences from the UniRef50 dataset 31 to create 1280-dimensional numerical protein vectors.”
From Turnover number predictions for kinetically uncharacterized enzymes using machine and deep learning (2023) · cites this paper as [31]
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“We performed MSA analysis using HHblits from the HH-suite3 software suite ( Suzek et al., 2007 ; Suzek et al., 2015 ; Remmert et al., 2012 ; Steinegger et al., 2019 ), a widely used open-source toolkit known for its sensitivity in detecting sequence similarities and identifying protein folds.”
- Authors
- Suzek, B. E., Wang, Y., Huang, H., McGarvey, P. B., Wu, C. H., & the UniProt Consortium
- Affiliations
- 1 Protein Information Resource, Georgetown University Medical Center, Washington, DC 20007, USA, 2Department of Computer Engineering, Muğla Sıtkı Koçman University, Muğla 48000, Turkey, 3Center for Bioinformatics and Computational Biology and Protein Information Resource, University of Delaware, Newark, DE 19711, USA, 4European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK and 5Swiss Institute of Bioinformatics, Centre Medical Universitaire, 1 rue Michel Servet, 1211 Geneva 4, Switzerland
- European Bioinformatics Institute (GB)
- Corresponding author
- Huang, Hongzhan
- Venue
- Bioinformatics
- Publisher
- Oxford University Press (OUP)
- Published
- 2014-11-13
- Volume
- 31
- Issue
- 6
- Pages
- 926-932
- ISSN
- 1367-4803 (ISSN-L)
- 1367-4811
- PMC
- PMC4375400
- OpenAlex
- W2102461176
- Cited by
- 1,928
- References
- 30
- Citations by year
- 2026: 193 · 2025: 298 · 2024: 306 · 2023: 252 · 2022: 225 · 2021: 226 · 2020: 130 · 2019: 95 · 2018: 84 · 2017: 69 · 2016: 36 · 2015: 12 · 2012: 1
- FWCI
- 16.12
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- Bioinformatics (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- reroDoc Digital Library (submittedVersion)
- Muğla Sıtkı Koçman University Institutional Repository (submittedVersion, other-oa)
- Funders
- National Institutes of Health
- National Human Genome Research Institute (U41HG006104, U41HG007822)
- 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
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Machine Learning in Bioinformatics
- Keywords
- protein sequence clustering, sequence similarity search, functional annotation, Gene Ontology, BLASTP, remote homology detection, UniProtKB
- MeSH terms
- AlkB Homolog 5, RNA Demethylase, Humans, Membrane Proteins, Proteins, Software, Cluster Analysis, Information Storage and Retrieval, Computational Biology, Sequence Analysis, Protein, Databases, Protein, Dioxygenases, Molecular Sequence Annotation, Gene Ontology
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