For Publishers
DiscoveryMetadataPeer reviewHostingPublishing
For Researchers
JoinPublishReviewCollect
Register
Blog
About
Search
Advanced search
My ScienceOpen
Search
For Publishers
For Researchers
Blog
About
261
views
12
references
 Top references
cited by
332
    
0 reviews
 Review
0
comments
 Comment
0
recommends
+1 Recommend
0 collections
    0
    shares
      193
      similar
       All similar
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      The Universal Protein Resource (UniProt)

      research-article
      Author(s): The UniProt Consortium *
      Publication date (Electronic):
      Journal: Nucleic Acids Research
      Publisher: Oxford University Press

      Read this article at

      ScienceOpenPublisherPMC
      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          The Universal Protein Resource (UniProt) provides a stable, comprehensive, freely accessible, central resource on protein sequences and functional annotation. The UniProt Consortium is a collaboration between the European Bioinformatics Institute (EBI), the Protein Information Resource (PIR) and the Swiss Institute of Bioinformatics (SIB). The core activities include manual curation of protein sequences assisted by computational analysis, sequence archiving, development of a user-friendly UniProt website, and the provision of additional value-added information through cross-references to other databases. UniProt is comprised of four major components, each optimized for different uses: the UniProt Knowledgebase, the UniProt Reference Clusters, the UniProt Archive and the UniProt Metagenomic and Environmental Sequences database. UniProt is updated and distributed every three weeks, and can be accessed online for searches or download at http://www.uniprot.org.

          Related collections

          Most cited references12

          • Record: found
          • Abstract: found
          • Article: not found

          UniRef: comprehensive and non-redundant UniProt reference clusters.

          Baris Suzek, Hongzhan Huang, Peter McGarvey … (2007)
          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. 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. 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 data are available at Bioinformatics online.
          Article 0 comments Cited 574 times – based on 0 reviews     Review now
            Bookmark
            • Record: found
            • Abstract: not found
            • Article: not found

            The genetic association database.

            Kevin G. Becker, Kathleen C Barnes, Tiffani Bright … (2004)
            Article 0 comments Cited 435 times – based on 0 reviews     Review now
              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found

              The Gene Ontology Annotation (GOA) Database: sharing knowledge in Uniprot with Gene Ontology.

              Evelyn Camon, Michele Magrane, Daniel Barrell … (2004)
              The Gene Ontology Annotation (GOA) database (http://www.ebi.ac.uk/GOA) aims to provide high-quality electronic and manual annotations to the UniProt Knowledgebase (Swiss-Prot, TrEMBL and PIR-PSD) using the standardized vocabulary of the Gene Ontology (GO). As a supplementary archive of GO annotation, GOA promotes a high level of integration of the knowledge represented in UniProt with other databases. This is achieved by converting UniProt annotation into a recognized computational format. GOA provides annotated entries for nearly 60,000 species (GOA-SPTr) and is the largest and most comprehensive open-source contributor of annotations to the GO Consortium annotation effort. By integrating GO annotations from other model organism groups, GOA consolidates specialized knowledge and expertise to ensure the data remain a key reference for up-to-date biological information. Furthermore, the GOA database fully endorses the Human Proteomics Initiative by prioritizing the annotation of proteins likely to benefit human health and disease. In addition to a non-redundant set of annotations to the human proteome (GOA-Human) and monthly releases of its GO annotation for all species (GOA-SPTr), a series of GO mapping files and specific cross-references in other databases are also regularly distributed. GOA can be queried through a simple user-friendly web interface or downloaded in a parsable format via the EBI and GO FTP websites. The GOA data set can be used to enhance the annotation of particular model organism or gene expression data sets, although increasingly it has been used to evaluate GO predictions generated from text mining or protein interaction experiments. In 2004, the GOA team will build on its success and will continue to supplement the functional annotation of UniProt and work towards enhancing the ability of scientists to access all available biological information. Researchers wishing to query or contribute to the GOA project are encouraged to email: goa@ebi.ac.uk.
              Article 0 comments Cited 326 times – based on 0 reviews     Review now
                Bookmark
                All references

                Author and article information

                Journal
                Journal ID (nlm-ta): Nucleic Acids Res
                Journal ID (iso-abbrev): Nucleic Acids Res
                Journal ID (publisher-id): nar
                Journal ID (hwp): nar
                Title: Nucleic Acids Research
                Publisher: Oxford University Press
                ISSN (Print): 0305-1048
                ISSN (Electronic): 1362-4962
                Publication date (Print): January 2008
                Publication date (Electronic): 27 November 2007
                Publication date PMC-release: 27 November 2007
                Volume: 36
                Issue: Database issue , Database issue
                Pages: D190-D195
                Affiliations
                The EMBL Outstation, The European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK, Protein Information Resource, Georgetown University Medical Center, 3300 Whitehaven St. NW, Suite 1200, Washington, DC 20007, USA and Swiss Institute of Bioinformatics, Centre Medical Universitaire 1 rue Michel Servet, 1211 Geneva 4, Switzerland
                Author notes
                *To whom correspondence should be addressed.+44 1223 494435+44 1223 494468 apweiler@ 123456ebi.ac.uk
                Article
                DOI: 10.1093/nar/gkm895
                PMC ID: 2238893
                PubMed ID: 18045787
                SO-VID: 038c4719-e715-42ab-bda3-cc52de39363c
                Copyright © © 2007 The Author(s)
                License:

                This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License ( http://creativecommons.org/licenses/by-nc/2.0/uk/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                Date received : 17 September 2007
                Date revision received : 3 October 2007
                Date accepted : 3 October 2007
                Categories
                Subject: Articles

                ScienceOpen disciplines: Genetics
                Data availability:
                ScienceOpen disciplines: Genetics

                Comments

                Comment on this article

                scite_

                Similar content193

                See all similar

                Cited by304

                See all cited by