title: Predictability of Genetic Interactions from Functional Gene Modules.
abstract: Characterizing genetic interactions is crucial to understanding cellular and organismal response to gene-level perturbations. Such knowledge can inform the selection of candidate disease therapy targets, yet experimentally determining whether genes interact is technically nontrivial and time-consuming. High-fidelity prediction of different classes of genetic interactions in multiple organisms would substantially alleviate this experimental burden. Under the hypothesis that functionally related genes tend to share common genetic interaction partners, we evaluate a computational approach to predict genetic interactions inHomo sapiens,Drosophila melanogaster, andSaccharomyces cerevisiaeBy leveraging knowledge of functional relationships between genes, we cross-validate predictions on known genetic interactions and observe high predictive power of multiple classes of genetic interactions in all three organisms. Additionally, our method suggests high-confidence candidate interaction pairs that can be directly experimentally tested. A web application is provided for users to query genes for predicted novel genetic interaction partners. Finally, by subsampling the known yeast genetic interaction network, we found that novel genetic interactions are predictable even when knowledge of currently known interactions is minimal.
authors.affiliation: Institute for Computational Engineering and Sciences, The University of Texas at Austin, Texas 78712.|Center for Systems and Synthetic Biology, The University of Texas at Austin, Texas 78712.
authors.forename: Jonathan H
authors.identifier: 0000-0003-3365-2702
authors.initials: JH
authors.lastname: Young
authors.affiliation: Center for Systems and Synthetic Biology, The University of Texas at Austin, Texas 78712 marcotte@icmb.utexas.edu.|Department of Molecular Biosciences, The University of Texas at Austin, Texas 78712.
authors.forename: Edward M
authors.initials: EM
authors.lastname: Marcotte
mesh_terms: D000818:Animals; D019295:Computational Biology; D004331:Drosophila melanogaster / Q000235:genetics; D004843:Epistasis, Genetic; D053263:Gene Regulatory Networks / Q000235:genetics*; D006801:Humans; D012441:Saccharomyces cerevisiae / Q000235:genetics
keywords: data mining; drug target; epistasis; gene network; synthetic lethality