46
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Evidence mining and novelty assessment of protein–protein interactions with the ConsensusPathDB plugin for Cytoscape

      research-article

      Read this article at

      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

          Summary: Protein–protein interaction detection methods are applied on a daily basis by molecular biologists worldwide. After generating a set of potential interactions, biologists face the problem of highlighting the ones that are novel and collecting evidence with respect to literature and annotation. This task can be as tedious as searching for every predicted interaction in several interaction data repositories, or manually screening the scientific literature. To facilitate the task of evidence mining and novelty assessment of protein–protein interactions, we have developed a Cytoscape plugin that automatically mines publication references, database references, interaction detection method descriptions and pathway annotation for a user-supplied network of interactions. The basis for the annotation is ConsensusPathDB—a meta-database that integrates numerous protein–protein, signaling, metabolic and gene regulatory interaction repositories for currently three species: Homo sapiens, Saccharomyces cerevisiae and Mus musculus.

          Availability: The ConsensusPathDB plugin for Cytoscape (version 2.7.0 or later) can be installed within Cytoscape on a major operating system (Windows, Mac OS, Unix/Linux) with Sun Java 1.5 or later installed through Cytoscape's Plugin manager (category ‘Network and Attribute I/O’). The plugin is freely available for download on the ConsensusPathDB web site ( http://cpdb.molgen.mpg.de).

          Supplementary information: Supplementary data are available at Bioinformatics online.

          Contact: kamburov@ 123456molgen.mpg.de

          Related collections

          Most cited references8

          • Record: found
          • Abstract: found
          • Article: found
          Is Open Access

          The Universal Protein Resource (UniProt) in 2010

          The primary mission of UniProt is to support biological research by maintaining a stable, comprehensive, fully classified, richly and accurately annotated protein sequence knowledgebase, with extensive cross-references and querying interfaces freely accessible to the scientific community. UniProt is produced by the UniProt Consortium which consists of groups from the European Bioinformatics Institute (EBI), the Swiss Institute of Bioinformatics (SIB) and the Protein Information Resource (PIR). UniProt is comprised of four major components, each optimized for different uses: the UniProt Archive, the UniProt Knowledgebase, the UniProt Reference Clusters and the UniProt Metagenomic and Environmental Sequence Database. UniProt is updated and distributed every 3 weeks and can be accessed online for searches or download at http://www.uniprot.org.
            Bookmark
            • Record: found
            • Abstract: found
            • Article: found
            Is Open Access

            MINT: the Molecular INTeraction database

            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 ().
              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found

              Large-scale mapping of human protein–protein interactions by mass spectrometry

              Mapping protein–protein interactions is an invaluable tool for understanding protein function. Here, we report the first large-scale study of protein–protein interactions in human cells using a mass spectrometry-based approach. The study maps protein interactions for 338 bait proteins that were selected based on known or suspected disease and functional associations. Large-scale immunoprecipitation of Flag-tagged versions of these proteins followed by LC-ESI-MS/MS analysis resulted in the identification of 24 540 potential protein interactions. False positives and redundant hits were filtered out using empirical criteria and a calculated interaction confidence score, producing a data set of 6463 interactions between 2235 distinct proteins. This data set was further cross-validated using previously published and predicted human protein interactions. In-depth mining of the data set shows that it represents a valuable source of novel protein–protein interactions with relevance to human diseases. In addition, via our preliminary analysis, we report many novel protein interactions and pathway associations.
                Bookmark

                Author and article information

                Journal
                Bioinformatics
                bioinformatics
                bioinfo
                Bioinformatics
                Oxford University Press
                1367-4803
                1367-4811
                1 November 2010
                16 September 2010
                16 September 2010
                : 26
                : 21
                : 2796-2797
                Affiliations
                1Department of Vertebrate Genomics, Max Planck Institute for Molecular Genetics, Ihnestr. 63-73, 14195 Berlin, Germany, 2Department of Medicine and 3Department of Bioengineering, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA 92093, USA
                Author notes
                *To whom correspondence should be addressed.

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

                Associate Editor: Jonathan Wren

                Article
                btq522
                10.1093/bioinformatics/btq522
                2958747
                20847220
                b6f5662d-00c8-4c2a-8d93-7cc7e3ecc091

                This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License ( http://creativecommons.org/licenses/by-nc/2.5), which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 6 July 2010
                : 17 August 2010
                : 7 September 2010
                Categories
                Applications Note
                Databases and Ontologies

                Bioinformatics & Computational biology
                Bioinformatics & Computational biology

                Comments

                Comment on this article