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Statistical methods for analysis of high-throughput RNA interference screens

Nature Methods volume 6, pages 569–575 (2009)Cite this article

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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Figure 1: Methods for visualizing data outputs of a screen.

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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).

Author information

Authors and Affiliations

  1. The RNAi Global Initiative, Lafayette, Colorado, USA

    Amanda Birmingham, Laura M Selfors, Thorsten Forster, David Wrobel, Caleb J Kennedy, Emma Shanks, Javier Santoyo-Lopez, Dara J Dunican, Aideen Long, Dermot Kelleher, Queta Smith, Roderick L Beijersbergen, Peter Ghazal & Caroline E Shamu

  2. Thermo Fisher Scientific, Lafayette, Colorado, USA

    Amanda Birmingham & Queta Smith

  3. ICCB-Longwood Screening Facility, Harvard Medical School, Boston, Massachusetts, USA

    Laura M Selfors, David Wrobel & Caroline E Shamu

  4. Division of Pathway Medicine and Centre for Systems Biology, The University of Edinburgh, Edinburgh, UK

    Thorsten Forster, Javier Santoyo-Lopez & Peter Ghazal

  5. Department of Systems Biology, Harvard Medical School, Boston, Massachusetts, USA

    Caleb J Kennedy & Caroline E Shamu

  6. Division of Biological Chemistry and Drug Discovery, Drug Discovery Unit, College of Life Sciences, University of Dundee, Dundee, Scotland

    Emma Shanks

  7. Institute of Molecular Medicine, Trinity Centre for Health Sciences, Trinity College, Dublin, Ireland

    Dara J Dunican, Aideen Long & Dermot Kelleher

  8. Division of Molecular Carcinogenesis and National Cancer Institute Robotics and Screening Center, The Netherlands Cancer Institute, Amsterdam, The Netherlands

    Roderick L Beijersbergen

Authors
  1. Amanda Birmingham
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  14. Caroline E Shamu
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Corresponding author

Correspondence to Amanda Birmingham.

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Competing interests

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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