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      STITCH 5: augmenting protein–chemical interaction networks with tissue and affinity data

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          Abstract

          Interactions between proteins and small molecules are an integral part of biological processes in living organisms. Information on these interactions is dispersed over many databases, texts and prediction methods, which makes it difficult to get a comprehensive overview of the available evidence. To address this, we have developed STITCH (‘Search Tool for Interacting Chemicals’) that integrates these disparate data sources for 430 000 chemicals into a single, easy-to-use resource. In addition to the increased scope of the database, we have implemented a new network view that gives the user the ability to view binding affinities of chemicals in the interaction network. This enables the user to get a quick overview of the potential effects of the chemical on its interaction partners. For each organism, STITCH provides a global network; however, not all proteins have the same pattern of spatial expression. Therefore, only a certain subset of interactions can occur simultaneously. In the new, fifth release of STITCH, we have implemented functionality to filter out the proteins and chemicals not associated with a given tissue. The STITCH database can be downloaded in full, accessed programmatically via an extensive API, or searched via a redesigned web interface at http://stitch.embl.de.

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          Most cited references 24

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          Network-based prediction of protein function

          Functional annotation of proteins is a fundamental problem in the post-genomic era. The recent availability of protein interaction networks for many model species has spurred on the development of computational methods for interpreting such data in order to elucidate protein function. In this review, we describe the current computational approaches for the task, including direct methods, which propagate functional information through the network, and module-assisted methods, which infer functional modules within the network and use those for the annotation task. Although a broad variety of interesting approaches has been developed, further progress in the field will depend on systematic evaluation of the methods and their dissemination in the biological community.
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            The BioGRID interaction database: 2013 update

            The Biological General Repository for Interaction Datasets (BioGRID: http//thebiogrid.org) is an open access archive of genetic and protein interactions that are curated from the primary biomedical literature for all major model organism species. As of September 2012, BioGRID houses more than 500 000 manually annotated interactions from more than 30 model organisms. BioGRID maintains complete curation coverage of the literature for the budding yeast Saccharomyces cerevisiae, the fission yeast Schizosaccharomyces pombe and the model plant Arabidopsis thaliana. A number of themed curation projects in areas of biomedical importance are also supported. BioGRID has established collaborations and/or shares data records for the annotation of interactions and phenotypes with most major model organism databases, including Saccharomyces Genome Database, PomBase, WormBase, FlyBase and The Arabidopsis Information Resource. BioGRID also actively engages with the text-mining community to benchmark and deploy automated tools to expedite curation workflows. BioGRID data are freely accessible through both a user-defined interactive interface and in batch downloads in a wide variety of formats, including PSI-MI2.5 and tab-delimited files. BioGRID records can also be interrogated and analyzed with a series of new bioinformatics tools, which include a post-translational modification viewer, a graphical viewer, a REST service and a Cytoscape plugin.
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              A network of protein-protein interactions in yeast.

              A global analysis of 2,709 published interactions between proteins of the yeast Saccharomyces cerevisiae has been performed, enabling the establishment of a single large network of 2,358 interactions among 1,548 proteins. Proteins of known function and cellular location tend to cluster together, with 63% of the interactions occurring between proteins with a common functional assignment and 76% occurring between proteins found in the same subcellular compartment. Possible functions can be assigned to a protein based on the known functions of its interacting partners. This approach correctly predicts a functional category for 72% of the 1,393 characterized proteins with at least one partner of known function, and has been applied to predict functions for 364 previously uncharacterized proteins.
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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
                04 January 2016
                20 November 2015
                20 November 2015
                : 44
                : Database issue , Database issue
                : D380-D384
                Affiliations
                [1 ]Institute of Molecular Life Sciences, University of Zurich and Swiss Institute of Bioinformatics, Winterthurerstrasse 190, 8057 Zurich, Switzerland
                [2 ]Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, 2200 Copenhagen N, Denmark
                [3 ]European Molecular Biology Laboratory, Structural and Computational Biology Unit, Molecular Medicine Partnership Unit, Meyerhofstrasse 1, 69117 Heidelberg, Germany
                [4 ]Max-Delbrück-Centre for Molecular Medicine, Robert-Rössle-Strasse 10, 13092 Berlin, Germany
                [5 ]Max Planck Institute of Molecular Cell Biology and Genetics, Pfotenhauerstrasse 108, 01307 Dresden
                Author notes
                [* ]To whom correspondence should be addressed. Tel: +49 351 210 2901; Fax: +49 351 210 1389; Email: mkuhn@ 123456mpi-cbg.de
                Correspondence may also be addressed to Peer Bork. Tel: +49 6221 387 8526; Fax: +49 6221 387 8517; Email: bork@ 123456embl.de
                Article
                10.1093/nar/gkv1277
                4702904
                26590256
                © The Author(s) 2015. 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: 5
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                Database Issue
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                04 January 2016

                Genetics

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