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Deciphering Protein–Protein Interactions. Part I. Experimental Techniques and Databases

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PLoS Computational Biology

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      Most cited references 129

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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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        BioGRID: a general repository for interaction datasets

        Access to unified datasets of protein and genetic interactions is critical for interrogation of gene/protein function and analysis of global network properties. BioGRID is a freely accessible database of physical and genetic interactions available at . BioGRID release version 2.0 includes >116 000 interactions from Saccharomyces cerevisiae, Caenorhabditis elegans, Drosophila melanogaster and Homo sapiens. Over 30 000 interactions have recently been added from 5778 sources through exhaustive curation of the Saccharomyces cerevisiae primary literature. An internally hyper-linked web interface allows for rapid search and retrieval of interaction data. Full or user-defined datasets are freely downloadable as tab-delimited text files and PSI-MI XML. Pre-computed graphical layouts of interactions are available in a variety of file formats. User-customized graphs with embedded protein, gene and interaction attributes can be constructed with a visualization system called Osprey that is dynamically linked to the BioGRID.
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          Functional organization of the yeast proteome by systematic analysis of protein complexes.

          Most cellular processes are carried out by multiprotein complexes. The identification and analysis of their components provides insight into how the ensemble of expressed proteins (proteome) is organized into functional units. We used tandem-affinity purification (TAP) and mass spectrometry in a large-scale approach to characterize multiprotein complexes in Saccharomyces cerevisiae. We processed 1,739 genes, including 1,143 human orthologues of relevance to human biology, and purified 589 protein assemblies. Bioinformatic analysis of these assemblies defined 232 distinct multiprotein complexes and proposed new cellular roles for 344 proteins, including 231 proteins with no previous functional annotation. Comparison of yeast and human complexes showed that conservation across species extends from single proteins to their molecular environment. Our analysis provides an outline of the eukaryotic proteome as a network of protein complexes at a level of organization beyond binary interactions. This higher-order map contains fundamental biological information and offers the context for a more reasoned and informed approach to drug discovery.
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            Author and article information

            Affiliations
            Whitehead Institute, United States of America
            Author notes
            * To whom correspondence should be addressed. E-mail: panch@ 123456ncbi.nlm.nih.gov
            Contributors
            Role: Editor
            Journal
            PLoS Comput Biol
            pcbi
            PLoS Computational Biology
            Public Library of Science (San Francisco, USA )
            1553-734X
            1553-7358
            March 2007
            30 March 2007
            : 3
            : 3
            1847991
            10.1371/journal.pcbi.0030042
            06-PLCB-EN-0421R2 plcb-03-03-13
            17397251
            (Editor)
            Copyright: © 2007 Shoemaker and Panchenko. This is an open-access article distributed under the terms of the Creative Commons Public Domain declaration which stipulates that, once placed in the public domain, this work may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose.
            Counts
            Pages: 8
            Categories
            Education
            Computational Biology
            Molecular Biology
            None
            Custom metadata
            Shoemaker BA, Panchenko, AR (2007) Deciphering protein–protein interactions. Part I. Experimental techniques and databases. PLoS Comput Biol 3(3): e42. doi: 10.1371/journal.pcbi.0030042
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            Quantitative & Systems biology

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