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A probability-based approach for the analysis of large-scale RNAi screens

Nature Methods volume 4, pages 847849 (2007) | Download Citation

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Abstract

We describe a statistical analysis methodology designed to minimize the impact of off-target activities upon large-scale RNA interference (RNAi) screens in mammalian cells. Application of this approach enhances reconfirmation rates and facilitates the experimental validation of new gene activities through the probability-based identification of multiple distinct and active small interfering RNAs (siRNAs) targeting the same gene. We further extend this approach to establish that the optimal redundancy for efficacious RNAi collections is between 4–6 siRNAs per gene.

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Acknowledgements

We thank L. Miraglia for helpful discussions and oversight of screens, J. Zhang for excellent technical assistance, S. Batalov (Genomics Institute of the Novartis Research Foundation) and P. Aza-Blanc (Burnham Institute) for the identification of negative control siRNA sequences, E. Lader (Qiagen) for facilitating collaboration, D. Elleder (Salk Institute) for providing the MLV supernatant, N.R. Landau (New York University, School of Medicine) for providing pNL43-luc-r+e, and N. Somia (University of Minnesota) for the gift of pCMVgp. R-language implementation of the RSA algorithm was provided by B. Zhou (Genomics Institute of the Novartis Research Foundation). This work was supported by the Novartis Research Foundation and a grant from the US National Institutes of Health (1 R01 AI072645-01).

Author information

Author notes

    • Renate König
    • , Paul D DeJesus
    •  & Sumit K Chanda

    Present address: Infectious & Inflammatory Disease Center, Burnham Institute for Medical Research, 10901 North Torrey Pines Road, La Jolla, California 92037, USA.

Affiliations

  1. The Genomics Institute of the Novartis Research Foundation, 10675 John J Hopkins Drive, San Diego, California 92121, USA.

    • Renate König
    • , Chih-yuan Chiang
    • , Buu P Tu
    • , S Frank Yan
    • , Paul D DeJesus
    • , Angelica Romero
    • , Anthony Orth
    • , Yingyao Zhou
    •  & Sumit K Chanda
  2. Qiagen GmbH, Qiagen Str. 1, 40724 Hilden, Germany.

    • Tobias Bergauer
    •  & Ute Krueger

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Competing interests

T.B. and U.K. are employees of Qiagen GmbH.

Corresponding authors

Correspondence to Yingyao Zhou or Sumit K Chanda.

Supplementary information

PDF files

  1. 1.

    Supplementary Text and Figures

    Supplementary Figures 1–2, Supplementary Methods

Excel files

  1. 1.

    Supplementary Table 1

    Original screen data for screen A.

  2. 2.

    Supplementary Table 2

    Original screen data for screen B.

  3. 3.

    Supplementary Table 3

    Scrambled Control Sequences.

  4. 4.

    Supplementary Table 4

    Reconfirmation screen data for screen A.

  5. 5.

    Supplementary Table 5

    Reconfirmation screen data for screen B.

About this article

Publication history

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DOI

https://doi.org/10.1038/nmeth1089

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