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Limited Agreement of Independent RNAi Screens for Virus-Required Host Genes Owes More to False-Negative than False-Positive Factors

Hao, L and He, Q and Wang, Z and Craven, M and Newton, MA and Ahlquist, P (2013) Limited Agreement of Independent RNAi Screens for Virus-Required Host Genes Owes More to False-Negative than False-Positive Factors. PLoS Computational Biology.

Abstract

Systematic, genome-wide RNA interference (RNAi) analysis is a powerful approach to identify gene functions that support or modulate selected biological processes. An emerging challenge shared with some other genome-wide approaches is that independent RNAi studies often show limited agreement in their lists of implicated genes. To better understand this, we analyzed four genome-wide RNAi studies that identified host genes involved in influenza virus replication. These studies collectively identified and validated the roles of 614 cell genes, but pair-wise overlap among the four gene lists was only 3% to 15% (average 6.7%). However, a number of functional categories were overrepresented in multiple studies. The pair-wise overlap of these enriched-category lists was high, ~19%, implying more agreement among studies than apparent at the gene level. Probing this further, we found that the gene lists implicated by independent studies were highly connected in interacting networks by independent functional measures such as protein-protein interactions, at rates significantly higher than predicted by chance. We also developed a general, model-based approach to gauge the effects of false-positive and false-negative factors and to estimate, from a limited number of studies, the total number of genes involved in a process. For influenza virus replication, this novel statistical approach estimates the total number of cell genes involved to be ~2,800. This and multiple other aspects of our experimental and computational results imply that, when following good quality control practices, the low overlap between studies is primarily due to false negatives rather than false-positive gene identifications. These results and methods have implications for and applications to multiple forms of genome-wide analysis. 2013 Hao et al

Item Type: Article
Additional Information: pubid: 134 nvp_institute: NIBR contributor_address: (Hao, Ahlquist) Institute of Molecular Virology, University of Wisconsin-Madison, Madison, WI, United States (Hao, Ahlquist) Howard Hughes Medical Institute, University of Wisconsin-Madison, Madison, WI, United States (He, Wang, Newton) Department of Statistics, University of Wisconsin-Madison, Madison, WI, United States (Craven, Newton) Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI, United States (Craven) Department of Computer Sciences, University of Wisconsin-Madison, Madison, WI, United States (Ahlquist) Morgridge Institute for Research, Madison, WI, United States (He) Novartis Institute of Biomedical Research, Boston, MA, United States
Date Deposited: 13 Oct 2015 13:12
Last Modified: 13 Oct 2015 13:12
URI: https://oak.novartis.com/id/eprint/21969

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