UniRef: comprehensive and non-redundant UniProt reference clusters

By Baris E. Suzek, Hongzhan Huang, Peter B. McGarvey, Raja Mazumder, Cathy H. Wu. 2007

1,485 citations recorded by the source corpus.

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.

Subfields: Molecular Biology, Spectroscopy, Computational Biology & Bioinformatics

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Citations in the corpus

Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences (2019, background): “Similarity-based spatial organization of orthologous genes Proteins that are related by orthology share a high level of sequence similarity by virtue of their common ancestry.”
UniProt: the universal protein knowledgebase (2016, background): “We also provide a series of UniRef databases that provide sequence sets trimmed at various levels of sequence identity (1,2).”
Large-scale determination of previously unsolved protein structures using evolutionary information (2015, methodology): “N 8, [ Eddy, 2009 ]) with uniref100 database ( Suzek et al., 2007 ) from January, 2015 and eliminated identical sequences and sequences aligned over less than 75% of the protein; the number of remaining sequences for each of the 58 families is listed in Table 2 .”
Transformer protein language models are unsupervised structure learners (2020, methodology): “We compare ESM-1b (Rives et al., 2020), a large-scale (650M parameters) Transformer model trained on UniRef50 (Suzek et al., 2007) to the Gremlin (Kamisetty et al., 2013) pipeline which implements a log linear model trained with pseudolikelihood (Balakrishnan et al., 2011; Ekeberg et al., 2013).”
Language models enable zero-shot prediction of the effects of mutations on protein function (2021, methodology): “We use Uniref90 2020-03 [24], employing the ESM-1b architecture and masked language modeling approach of Rives et al.”
The Universal Protein Resource (UniProt) 2009 (2008, methodology): “UniRef provides clustered sets of all sequences from the UniProt Knowledgebase (including splice forms as separate entries) and selected UniProt Archive records to obtain complete coverage of sequence space at resolutions of 100, 90 and 50% identity while hiding redundant sequences (11).”
Improved model quality assessment using ProQ2 (2012, methodology): “Sequence profiles were derived using three iterations of PSI-BLAST [38] against UniRef90, release 2010 3 [39] with a 10−3 E–value cutoff for inclusion (-h) and all other parameters at default settings.”
PEP-FOLD: an online resource for de novo peptide structure prediction (2009, neutral): “In the first step, the SA letter profiles are predicted at each independent position, using an amino acid profile obtained by running PSI-BLAST (19) against the uniref data-bank (20).”