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      STRING v10: protein–protein interaction networks, integrated over the tree of life

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          Abstract

          The many functional partnerships and interactions that occur between proteins are at the core of cellular processing and their systematic characterization helps to provide context in molecular systems biology. However, known and predicted interactions are scattered over multiple resources, and the available data exhibit notable differences in terms of quality and completeness. The STRING database ( http://string-db.org) aims to provide a critical assessment and integration of protein–protein interactions, including direct (physical) as well as indirect (functional) associations. The new version 10.0 of STRING covers more than 2000 organisms, which has necessitated novel, scalable algorithms for transferring interaction information between organisms. For this purpose, we have introduced hierarchical and self-consistent orthology annotations for all interacting proteins, grouping the proteins into families at various levels of phylogenetic resolution. Further improvements in version 10.0 include a completely redesigned prediction pipeline for inferring protein–protein associations from co-expression data, an API interface for the R computing environment and improved statistical analysis for enrichment tests in user-provided networks.

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          Most cited references 26

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          Gene ontology: tool for the unification of biology. The Gene Ontology Consortium.

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            Cluster analysis and display of genome-wide expression patterns.

            A system of cluster analysis for genome-wide expression data from DNA microarray hybridization is described that uses standard statistical algorithms to arrange genes according to similarity in pattern of gene expression. The output is displayed graphically, conveying the clustering and the underlying expression data simultaneously in a form intuitive for biologists. We have found in the budding yeast Saccharomyces cerevisiae that clustering gene expression data groups together efficiently genes of known similar function, and we find a similar tendency in human data. Thus patterns seen in genome-wide expression experiments can be interpreted as indications of the status of cellular processes. Also, coexpression of genes of known function with poorly characterized or novel genes may provide a simple means of gaining leads to the functions of many genes for which information is not available currently.
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              Activities at the Universal Protein Resource (UniProt)

              The mission of the Universal Protein Resource (UniProt) (http://www.uniprot.org) is to provide the scientific community with a comprehensive, high-quality and freely accessible resource of protein sequences and functional annotation. It integrates, interprets and standardizes data from literature and numerous resources to achieve the most comprehensive catalog possible of protein information. The central activities are the biocuration of the UniProt Knowledgebase and the dissemination of these data through our Web site and web services. UniProt is produced by the UniProt Consortium, which consists of groups from the European Bioinformatics Institute (EBI), the SIB Swiss Institute of Bioinformatics (SIB) and the Protein Information Resource (PIR). UniProt is updated and distributed every 4 weeks and can be accessed online for searches or downloads.
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                Author and article information

                Journal
                Nucleic Acids Res
                Nucleic Acids Res
                nar
                nar
                Nucleic Acids Research
                Oxford University Press
                0305-1048
                1362-4962
                28 January 2015
                28 October 2014
                28 October 2014
                : 43
                : Database issue , Database issue
                : D447-D452
                Affiliations
                [1 ]Institute of Molecular Life Sciences and Swiss Institute of Bioinformatics, University of Zurich, 8057 Zurich, Switzerland
                [2 ]European Molecular Biology Laboratory, 69117 Heidelberg, Germany
                [3 ]Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, 2200 Copenhagen N, Denmark
                [4 ]Biotechnology Center, Technische Universität Dresden, 01062 Dresden, Germany
                [5 ]Max Planck Institute of Molecular Cell Biology and Genetics, 01062 Dresden, Germany
                Author notes
                [* ]To whom correspondence should be addressed. Tel: +41 44 6353147; Fax: +41 44 6356864; mering@ 123456imls.uzh.ch
                Correspondence may also be addressed to Peer Bork. Tel: +49 6221 387 8526; Fax: +49 6221 387 517; bork@ 123456embl.de
                Correspondence may also be addressed to Lars J. Jensen. Tel: +45 353 25025; Fax: +45 353 25001; lars.juhl.jensen@ 123456cpr.ku.dk
                Article
                10.1093/nar/gku1003
                4383874
                25352553
                © The Author(s) 2014. Published by Oxford University Press on behalf of Nucleic Acids Research.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.

                Page count
                Pages: 6
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                Database Issue
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                28 January 2015

                Genetics

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