Abstract
RNA interference (RNAi) has become a powerful technique for reverse genetics and drug discovery, and in both of these areas large-scale high-throughput RNAi screens are commonly performed. The statistical techniques used to analyze these screens are frequently borrowed directly from small-molecule screening; however, small-molecule and RNAi data characteristics differ in meaningful ways. We examine the similarities and differences between RNAi and small-molecule screens, highlighting particular characteristics of RNAi screen data that must be addressed during analysis. Additionally, we provide guidance on selection of analysis techniques in the context of a sample workflow.
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Acknowledgements
Work at ICCB-Longwood was funded by US National Institutes of Health grants CA078048, AI067751 and AI057159. Funding to P.G. was provided by the Wellcome Trust and the Biotechnology and Biological Sciences Research Council (BBSRC). The Centre for Systems Biology at Edinburgh is a Centre for Integrative Systems Biology (CISB) funded by BBSRC and the Engineering and Physical Sciences Research Council (EPSRC), reference BB/D019621/1. Work at the University of Edinburgh was supported by edikt2 (Scottish Funding Council grant HR04019; http://www.edikt.org/). R.L.B. is supported by Shering-Plough and TIPharma. D.J.D., A.L. and D.K. are supported by Marie Curie MTKD-CT-2005-029798 (European Union FP6).
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A.B. and Q.S. are employed by Thermo Fisher Scientific. T.F. and P.G. are directors of Fios Genomics Ltd. C.J.K. is now employed by Life Technologies.
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Birmingham, A., Selfors, L., Forster, T. et al. Statistical methods for analysis of high-throughput RNA interference screens. Nat Methods 6, 569–575 (2009). https://doi.org/10.1038/nmeth.1351
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DOI: https://doi.org/10.1038/nmeth.1351
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