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Statistical methods for analysis of high-throughput RNA interference screens

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  • A. Birmingham
  • L. M. Selfors
  • T. Forster
  • D. Wrobel
  • C. J. Kennedy
  • E. Shanks
  • J. Santoyo-Lopez
  • D. J. Dunican
  • A. Long
  • D. Kelleher
  • Q. Smith
  • R. L. Beijersbergen
  • P. Ghazal
  • C. E. Shamu

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    Rights statement: Published in final edited form as: Nat Methods. 2009 August ; 6(8): 569–575. doi:10.1038/nmeth.1351.

    Accepted author manuscript, 678 KB, PDF document

http://www.nature.com/nmeth/journal/v6/n8/abs/nmeth.1351.html
Original languageEnglish
Pages (from-to)569-575
Number of pages7
JournalNature Methods
Volume6
Issue number8
DOIs
Publication statusPublished - Aug 2009

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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