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Statistical methods for analysis of high-throughput RNA interference screens

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NATURE METHODS
卷 6, 期 8, 页码 569-575

出版社

NATURE PORTFOLIO
DOI: 10.1038/nmeth.1351

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资金

  1. US National Institutes of Health [CA078048, AI067751, AI057159]
  2. Wellcome Trust
  3. Biotechnology and Biological Sciences Research Council (BBSRC)
  4. Engineering and Physical Sciences Research Council (EPSRC) [BB/D019621/1]
  5. Scottish Funding Council [HR04019]
  6. Shering-Plough and TIPharma
  7. Marie Curie [MTKD-CT-2005-029798]
  8. Biotechnology and Biological Sciences Research Council [BB/D019621/1] Funding Source: researchfish
  9. BBSRC [BB/D019621/1] Funding Source: UKRI

向作者/读者索取更多资源

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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