Toward a Comprehensive Atlas of the Physical Interactome of Saccharomyces cerevisiae Export

@article{citeulike:1088398, abstract = {Defining protein complexes is critical to virtually all aspects of cell biology. Two recent affinity purification/mass spectrometry studies in Saccharomyces cerevisiae have vastly increased the available protein interaction data. The practical utility of such high throughput interaction sets, however, is substantially decreased by the presence of false positives. Here we created a novel probabilistic metric that takes advantage of the high density of these data, including both the presence and absence of individual associations, to provide a measure of the relative confidence of each potential protein-protein interaction. This analysis largely overcomes the noise inherent in high throughput immunoprecipitation experiments. For example, of the 12,122 binary interactions in the general repository of interaction data (BioGRID) derived from these two studies, we marked 7504 as being of substantially lower confidence. Additionally, applying our metric and a stringent cutoff we identified a set of 9074 interactions (including 4456 that were not among the 12,122 interactions) with accuracy comparable to that of conventional small scale methodologies. Finally we organized proteins into coherent multisubunit complexes using hierarchical clustering. This work thus provides a highly accurate physical interaction map of yeast in a format that is readily accessible to the biological community. 10.1074/mcp.M600381-MCP200}, address = {Department of Cellular and Molecular Pharmacology, University of California, San Francisco, San Francisco, CA 94158.}, author = {Collins, Sean R. and Kemmeren, Patrick and Zhao, Xue-Chu and Greenblatt, Jack F. and Spencer, Forrest and Holstege, Frank C. P. and Weissman, Jonathan S. and Krogan, Nevan J.}, citeulike-article-id = {1088398}, citeulike-linkout-0 = {http://dx.doi.org/10.1074/mcp.M600381-MCP200}, citeulike-linkout-1 = {http://www.mcponline.org/content/6/3/439.abstract}, citeulike-linkout-2 = {http://www.mcponline.org/content/6/3/439.full.pdf}, citeulike-linkout-3 = {http://www.mcponline.org/cgi/content/abstract/6/3/439}, citeulike-linkout-4 = {http://view.ncbi.nlm.nih.gov/pubmed/17200106}, citeulike-linkout-5 = {http://www.hubmed.org/display.cgi?uids=17200106}, day = {1}, doi = {10.1074/mcp.M600381-MCP200}, issn = {1535-9476}, journal = {Mol Cell Proteomics}, keywords = {protein-protein, yeast}, month = {March}, number = {3}, pages = {439--450}, posted-at = {2007-02-05 14:08:13}, priority = {2}, title = {Toward a Comprehensive Atlas of the Physical Interactome of Saccharomyces cerevisiae}, url = {http://dx.doi.org/10.1074/mcp.M600381-MCP200}, volume = {6}, year = {2007} }

TY - JOUR ID - citeulike:1088398 L3 - citeulike-article-id:1088398 N2 - Defining protein complexes is critical to virtually all aspects of cell biology. Two recent affinity purification/mass spectrometry studies in Saccharomyces cerevisiae have vastly increased the available protein interaction data. The practical utility of such high throughput interaction sets, however, is substantially decreased by the presence of false positives. Here we created a novel probabilistic metric that takes advantage of the high density of these data, including both the presence and absence of individual associations, to provide a measure of the relative confidence of each potential protein-protein interaction. This analysis largely overcomes the noise inherent in high throughput immunoprecipitation experiments. For example, of the 12,122 binary interactions in the general repository of interaction data (BioGRID) derived from these two studies, we marked 7504 as being of substantially lower confidence. Additionally, applying our metric and a stringent cutoff we identified a set of 9074 interactions (including 4456 that were not among the 12,122 interactions) with accuracy comparable to that of conventional small scale methodologies. Finally we organized proteins into coherent multisubunit complexes using hierarchical clustering. This work thus provides a highly accurate physical interaction map of yeast in a format that is readily accessible to the biological community. 10.1074/mcp.M600381-MCP200 IS - 3 JF - Mol Cell Proteomics SN - 1535-9476 AD - Department of Cellular and Molecular Pharmacology, University of California, San Francisco, San Francisco, CA 94158. EP - 450 TI - Toward a Comprehensive Atlas of the Physical Interactome of Saccharomyces cerevisiae VL - 6 SP - 439 KW - protein-protein KW - yeast AU - Collins, Sean AU - Kemmeren, Patrick AU - Zhao, Xue-Chu AU - Greenblatt, Jack AU - Spencer, Forrest AU - Holstege, Frank AU - Weissman, Jonathan AU - Krogan, Nevan PY - 2007/03/01/ UR - http://dx.doi.org/10.1074/mcp.M600381-MCP200 ER -