Thu, 21 Aug 2008 15:26:34 BST CiteULike: neils Ideker http://www.citeulike.org/user/neils/author/Ideker CiteULike.org en-gb Copyright © 2004-2008 citeulike.org http://www.citeulike.org/user/neils/article/2693993 <i>PLoS Comput Biol, Vol. 4, No. 4. (Apr 2008)</i><br /><br />Recently, a number of advanced screening technologies have allowed for the comprehensive quantification of aggravating and alleviating genetic interactions among gene pairs. In parallel, TAP-MS studies (tandem affinity purification followed by mass spectroscopy) have been successful at identifying physical protein interactions that can indicate proteins participating in the same molecular complex. Here, we propose a method for the joint learning of protein complexes and their functional relationships by integration of quantitative genetic interactions and TAP-MS data. Using 3 independent benchmark datasets, we demonstrate that this method is >50% more accurate at identifying functionally related protein pairs than previous approaches. Application to genes involved in yeast chromosome organization identifies a functional map of 91 multimeric complexes, a number of which are novel or have been substantially expanded by addition of new subunits. Interestingly, we find that complexes that are enriched for aggravating genetic interactions (i.e., synthetic lethality) are more likely to contain essential genes, linking each of these interactions to an underlying mechanism. These results demonstrate the importance of both large-scale genetic and physical interaction data in mapping pathway architecture and function. Functional Maps of Protein Complexes from Quantitative Genetic Interaction Data Sourav Bandyopadhyay Ryan Kelley Nevan Krogan Trey Ideker doi:10.1371/journal.pcbi.1000065 PLoS Comput Biol, Vol. 4, No. 4. (Apr 2008) 2008-04-21T02:13:52-00:00 2008 PLoS Comput Biol 4 4 Public Library of Science bioinformatics complex genetics interaction network prediction protein-protein