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      The BioGRID Interaction Database: 2008 update

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          Abstract

          The Biological General Repository for Interaction Datasets (BioGRID) database ( http://www.thebiogrid.org) was developed to house and distribute collections of protein and genetic interactions from major model organism species. BioGRID currently contains over 198 000 interactions from six different species, as derived from both high-throughput studies and conventional focused studies. Through comprehensive curation efforts, BioGRID now includes a virtually complete set of interactions reported to date in the primary literature for both the budding yeast Saccharomyces cerevisiae and the fission yeast Schizosaccharomyces pombe. A number of new features have been added to the BioGRID including an improved user interface to display interactions based on different attributes, a mirror site and a dedicated interaction management system to coordinate curation across different locations. The BioGRID provides interaction data with monthly updates to Saccharomyces Genome Database, Flybase and Entrez Gene. Source code for the BioGRID and the linked Osprey network visualization system is now freely available without restriction.

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          Systematic identification of protein complexes in Saccharomyces cerevisiae by mass spectrometry.

          Yuen Ho, Albrecht Gruhler, Adrian Heilbut … (2002)
          The recent abundance of genome sequence data has brought an urgent need for systematic proteomics to decipher the encoded protein networks that dictate cellular function. To date, generation of large-scale protein-protein interaction maps has relied on the yeast two-hybrid system, which detects binary interactions through activation of reporter gene expression. With the advent of ultrasensitive mass spectrometric protein identification methods, it is feasible to identify directly protein complexes on a proteome-wide scale. Here we report, using the budding yeast Saccharomyces cerevisiae as a test case, an example of this approach, which we term high-throughput mass spectrometric protein complex identification (HMS-PCI). Beginning with 10% of predicted yeast proteins as baits, we detected 3,617 associated proteins covering 25% of the yeast proteome. Numerous protein complexes were identified, including many new interactions in various signalling pathways and in the DNA damage response. Comparison of the HMS-PCI data set with interactions reported in the literature revealed an average threefold higher success rate in detection of known complexes compared with large-scale two-hybrid studies. Given the high degree of connectivity observed in this study, even partial HMS-PCI coverage of complex proteomes, including that of humans, should allow comprehensive identification of cellular networks.
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            The generic genome browser: a building block for a model organism system database.

            Lincoln D. Stein, Christopher John Mungall, ShengQiang Shu … (2002)
            The Generic Model Organism System Database Project (GMOD) seeks to develop reusable software components for model organism system databases. In this paper we describe the Generic Genome Browser (GBrowse), a Web-based application for displaying genomic annotations and other features. For the end user, features of the browser include the ability to scroll and zoom through arbitrary regions of a genome, to enter a region of the genome by searching for a landmark or performing a full text search of all features, and the ability to enable and disable tracks and change their relative order and appearance. The user can upload private annotations to view them in the context of the public ones, and publish those annotations to the community. For the data provider, features of the browser software include reliance on readily available open source components, simple installation, flexible configuration, and easy integration with other components of a model organism system Web site. GBrowse is freely available under an open source license. The software, its documentation, and support are available at http://www.gmod.org.
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              MINT: the Molecular INTeraction database

              Andrew Chatr-aryamontri, Arnaud Ceol, Luisa Palazzi … (2006)
              The Molecular INTeraction database (MINT, ) aims at storing, in a structured format, information about molecular interactions (MIs) by extracting experimental details from work published in peer-reviewed journals. At present the MINT team focuses the curation work on physical interactions between proteins. Genetic or computationally inferred interactions are not included in the database. Over the past four years MINT has undergone extensive revision. The new version of MINT is based on a completely remodeled database structure, which offers more efficient data exploration and analysis, and is characterized by entries with a richer annotation. Over the past few years the number of curated physical interactions has soared to over 95 000. The whole dataset can be freely accessed online in both interactive and batch modes through web-based interfaces and an FTP server. MINT now includes, as an integrated addition, HomoMINT, a database of interactions between human proteins inferred from experiments with ortholog proteins in model organisms ().
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                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): 13 November 2007
                Publication date PMC-release: 13 November 2007
                Volume: 36
                Issue: Database issue , Database issue
                Pages: D637-D640
                Affiliations
                1Samuel Lunenfeld Research Institute, Mount Sinai Hospital, Toronto, M5G 1X5, 2Department of Molecular Genetics, University of Toronto, Toronto, M5S 1A8, Canada, 3Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ 08544, USA and 4Wellcome Trust Sanger Institute, Cambridge CB10 1HH, UK
                Author notes
                *To whom correspondence should be addressed.416 586 8371416 586 8869 tyers@ 123456mshri.on.ca

                Present address: Mike Tyers, Wellcome Trust Centre for Cell Biology, Institute of Cell Biology, School of Biological Sciences, The University of Edinburgh, Mayfield Road, Edinburgh EH9 3JR, UK

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

                Article
                DOI: 10.1093/nar/gkm1001
                PMC ID: 2238873
                PubMed ID: 18000002
                SO-VID: 0a0a4ab2-6ef0-4e95-912e-2a78177e8f6c
                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 : 20 September 2007
                Date revision received : 19 October 2007
                Date accepted : 22 October 2007
                Categories
                Subject: Articles

                ScienceOpen disciplines: Genetics
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                ScienceOpen disciplines: Genetics

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