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Submitted on October 4, 2006
Department of Cellular and Molecular Pharmacology, University of California, San Francisco, San Francisco, CA 94158
Corresponding Author: krogan{at}cmp.ucsf.edu
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 create 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 mark 7,504 as being of substantially lower confidence. Additionally, applying our metric and a stringent cutoff identifies a set of 9,074 interactions (including 4,456 which were not among the 12,122 interactions) with accuracy comparable to that of conventional small-scale methodologies. Finally, we organize proteins into coherent multi-subunit 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.
Revised on January 2, 2007
Accepted on January 2, 2007
Towards a comprehensive atlas of the physical interactome of Saccharomyces cerevisiae
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