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      STRING 7—recent developments in the integration and prediction of protein interactions

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          Abstract

          Information on protein–protein interactions is still mostly limited to a small number of model organisms, and originates from a wide variety of experimental and computational techniques. The database and online resource STRING generalizes access to protein interaction data, by integrating known and predicted interactions from a variety of sources. The underlying infrastructure includes a consistent body of completely sequenced genomes and exhaustive orthology classifications, based on which interaction evidence is transferred between organisms. Although primarily developed for protein interaction analysis, the resource has also been successfully applied to comparative genomics, phylogenetics and network studies, which are all facilitated by programmatic access to the database backend and the availability of compact download files. As of release 7, STRING has almost doubled to 373 distinct organisms, and contains more than 1.5 million proteins for which associations have been pre-computed. Novel features include AJAX-based web-navigation, inclusion of additional resources such as BioGRID, and detailed protein domain annotation. STRING is available at http://string.embl.de/

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          Most cited references14

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          Comparative metagenomics of microbial communities.

          The species complexity of microbial communities and challenges in culturing representative isolates make it difficult to obtain assembled genomes. Here we characterize and compare the metabolic capabilities of terrestrial and marine microbial communities using largely unassembled sequence data obtained by shotgun sequencing DNA isolated from the various environments. Quantitative gene content analysis reveals habitat-specific fingerprints that reflect known characteristics of the sampled environments. The identification of environment-specific genes through a gene-centric comparative analysis presents new opportunities for interpreting and diagnosing environments.
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            Assigning protein functions by comparative genome analysis: protein phylogenetic profiles.

            Determining protein functions from genomic sequences is a central goal of bioinformatics. We present a method based on the assumption that proteins that function together in a pathway or structural complex are likely to evolve in a correlated fashion. During evolution, all such functionally linked proteins tend to be either preserved or eliminated in a new species. We describe this property of correlated evolution by characterizing each protein by its phylogenetic profile, a string that encodes the presence or absence of a protein in every known genome. We show that proteins having matching or similar profiles strongly tend to be functionally linked. This method of phylogenetic profiling allows us to predict the function of uncharacterized proteins.
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              The use of gene clusters to infer functional coupling.

              Previously, we presented evidence that it is possible to predict functional coupling between genes based on conservation of gene clusters between genomes. With the rapid increase in the availability of prokaryotic sequence data, it has become possible to verify and apply the technique. In this paper, we extend our characterization of the parameters that determine the utility of the approach, and we generalize the approach in a way that supports detection of common classes of functionally coupled genes (e.g., transport and signal transduction clusters). Now that the analysis includes over 30 complete or nearly complete genomes, it has become clear that this approach will play a significant role in supporting efforts to assign functionality to the remaining uncharacterized genes in sequenced genomes.
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                Author and article information

                Journal
                Nucleic Acids Res
                Nucleic Acids Res
                nar
                Nucleic Acids Research
                Nucleic Acids Research
                Oxford University Press
                0305-1048
                1362-4962
                January 2007
                10 November 2006
                10 November 2006
                : 35
                : Database issue
                : D358-D362
                Affiliations
                1European Molecular Biology Laboratory, Meyerhofstrasse 1 69117 Heidelberg, Germany
                2University of Zurich, Winterthurerstrasse 190 8057 Zurich, Switzerland
                3Utrecht University, Padualaan 8 3584 CH Utrecht, The Netherlands
                4Max-Delbrück-Centre for Molecular Medicine, Robert-Rössle-Str. 10 13092 Berlin, Germany
                Author notes
                *To whom correspondence should be addressed. Tel: +41 44 6353147; Fax: +41 44 6356864; Email: mering@ 123456molbio.unizh.ch

                The authors wish it to be known that, in their opinion, the first two authors should be regarded as joint First Authors

                Article
                10.1093/nar/gkl825
                1669762
                17098935
                4358feca-82d2-43da-92a3-0fb757eb7ca7
                © 2006 The Author(s)

                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
                : 15 September 2006
                : 05 October 2006
                : 05 October 2006
                Categories
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                Genetics
                Genetics

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