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Minimizing the risk of reporting false positives in large-scale RNAi screens

Nature Methods volume 3, pages 777–779 (2006)Cite this article

Large-scale RNA interference (RNAi)-based analyses, very much as other 'omic' approaches, have inherent rates of false positives and negatives. The variability in the standards of care applied to validate results from these studies, if left unchecked, could eventually begin to undermine the credibility of RNAi as a powerful functional approach. This Commentary is an invitation to an open discussion started among various users of RNAi to set forth accepted standards that would insure the quality and accuracy of information in the large datasets coming out of genome-scale screens. Please visit methagora to view and post comments on this article

In recent years the large-scale application of RNAi to functional genomic screens in Caenorhabditis elegans, Drosophila melanogaster and, more recently, mammalian cells has led to the publication of a fast-growing number of 'hit lists', that is, genes that elicit a positive response (or score) in various functional assays. As with other 'omic' approaches, these studies have inherent rates of false positives and negatives that can be caused by the quality of reagents, sensitivity of the assay, data sampling issues and so forth. The explosive speed at which RNAi technology has been adopted in many research fields, combined with the rapid evolution in our understanding of the underlying silencing pathways, has made it particularly difficult for users to keep up with what represents 'best scientific practice' in this field. Though general guidelines have been discussed recently to insure sound experimental approaches to maximize RNAi specificity1,2, many of us involved in genome-scale screens have felt a need for a public discussion on adopting ever-improving quality standards in such screens to minimize the contamination of false positives in future RNAi datasets. We propose, by way of this Commentary, to trigger an open and hopefully constructive dialog throughout the community centered around some key issues that we, as contributors to this field, have come to appreciate as important caveats in RNAi experiments to date.

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Figure 1: Appropriate experimental controls to minimize risks of misinterpretation of RNAi data due to off-target effects (OTEs).

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Authors and Affiliations

  1. Cenix BioScience GmbH, Tatzberg 47, Dresden, 10307, Germany

    Christophe J Echeverri

  2. Howard Hughes Medical Institute, The Johns Hopkins University School of Medicine, Baltimore, 21205, Maryland, USA

    Philip A Beachy & Yong Ma

  3. Morphogenesis Group, Ludwig Institute for Cancer Research UCL Branch, Courtauld Building, 91 Riding House Street, London, W1W 7BS, UK

    Buzz Baum

  4. Signaling and Functional Genomics, German Cancer Research Center (DKFZ/B110), Im Neuenheimer Feld 580, Heidelberg, D-69120, Germany

    Michael Boutros

  5. Max Plank Institute of Molecular Cell Biology and Genetics, 108 Pfotenhauerstrasse, Dresden, 01307, Germany

    Frank Buchholz

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

    Sumit K Chanda

  7. Cancer Research UK London Research Institute, 44 Lincoln's Inn Fields, London, WC2A 3PX, UK

    Julian Downward

  8. Gene Expression Unit, European Molecular Biology Laboratory, Heidelberg, 69117, Germany

    Jan Ellenberg, David E Root & David M Sabatini

  9. The Wellcome Trust Sanger Institute, Hinxton, Cambridge, CB10 1HH, UK

    Andrew G Fraser

  10. Broad Institute of MIT and Harvard, 7 Cambridge Center, Cambridge, 02142, Massachusetts, USA

    Nir Hacohen & William C Hahn

  11. Center for Immunology and Inflammatory Diseases, Massachusetts General Hospital Boston, 55 Fruit Street, Massachusetts, 02114, USA

    Nir Hacohen

  12. Department of Medical Oncology, Dana-Farber Cancer Institute, 44 Binney Street, Boston, 02115, Massachusetts, USA

    William C Hahn

  13. Rosetta Inpharmatics, 401 Terry Ave. N, Seattle, 98109, Washington, USA

    Aimee L Jackson & Peter S Linsley

  14. Department of Cell and Developmental Biology, University of California San Diego, Gilman Drive, La Jolla, 9500, 92093, California, USA

    Amy Kiger

  15. Department of Cell Biology, UT Southwestern Medical Center, 5323 Harry Hines Blvd., Dallas, 75390, Texas, USA

    Lawrence Lum

  16. Drosophila RNAi Screening Center and Department of Genetics, Harvard Medical School, 77 Avenue Louis Pasteur, Boston, 02115, Massachusetts, USA

    Bernard Mathey-Prévôt & Norbert Perrimon

  17. Whitehead Institute for Biomedical Research, 9 Cambridge Center, Cambridge, 02142, Massachusetts, USA

    David M Sabatini

  18. Molecular and Cancer Biology Program, Biomedicum Helsinki, PO Box 63 (Haartmaninkatu 8), FI-00014 University of Helsinki, Finland

    Jussi Taipale

  19. Howard Hughes Medical Institute, Harvard Medical School, 77 Avenue Louis Pasteur, Boston, 02115, Massachusetts, USA

    Norbert Perrimon

  20. Division of Molecular Carcinogenesis and Center for Biomedical Genetics, The Netherlands Cancer Institute, Plesmanlaan 121, Amsterdam, 1066 CX, The Netherlands

    René Bernards

Authors
  1. Christophe J Echeverri
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  2. Philip A Beachy
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  7. Julian Downward
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  8. Jan Ellenberg
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  9. Andrew G Fraser
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  10. Nir Hacohen
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  12. Aimee L Jackson
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  13. Amy Kiger
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  14. Peter S Linsley
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  15. Lawrence Lum
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  16. Yong Ma
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  17. Bernard Mathey-Prévôt
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  18. David E Root
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  19. David M Sabatini
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  20. Jussi Taipale
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  21. Norbert Perrimon
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  22. René Bernards
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Echeverri, C., Beachy, P., Baum, B. et al. Minimizing the risk of reporting false positives in large-scale RNAi screens. Nat Methods 3, 777–779 (2006). https://doi.org/10.1038/nmeth1006-777

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  • DOI: https://doi.org/10.1038/nmeth1006-777

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