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RNAiCut: automated detection of significant genes from functional genomic screens

Nature Methods volume 6, pages 476–477 (2009)Cite this article

To the Editor: RNA interference (RNAi) is a popular functional genomic technology for identifying genes involved in a biological process. Although higher scores for genes in an RNAi screen suggest more central roles in the pathway, estimating the score threshold separating pathway- or process-relevant hits from noise remains difficult (Supplementary Table 1) and is typically done manually.

RNAiCut chose successful thresholds, as measured by Gene Ontology (GO)5 enrichment: the gene lists with above-threshold scores were enriched for functions relevant to the screen, compared to the rest (Supplementary Table 4). When the manual screener's threshold was later in the ranked list of hits than the RNAiCut threshold, choosing RNAiCut's threshold may reduce the potentially high number of false positives. When RNAiCut's threshold was later, the GO enrichment for RNAiCut's cutoff was at least as good as for the manually determined cutoff, revealing additional pathway-relevant genes (Supplementary Results). Although some of the additional hits identified by RNAiCut may be false positives, analyzing them may be useful given their apparent connectivity to the core signaling pathway. RNAiCut was robust to Z-score noise generated by randomly scrambling close Z scores (Supplementary Fig. 11 and Supplementary Table 5).

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Figure 1: RNAiCut results for insulin-triggered MAPK pathway screen in D. melanogaster4.

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Acknowledgements

We thank J. Bienkowska, M. Booker and K.G. Lu. I.M.K. was partially supported by the John Reed Fund.

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Author notes

  1. Irene M Kaplow and Rohit Singh: These authors contributed equally to this work.

Authors and Affiliations

  1. Department of Mathematics Massachusetts Institute of Technology, Cambridge, Massachusetts, USA

    Irene M Kaplow & Bonnie Berger

  2. Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA

    Rohit Singh, Chris Bakal & Bonnie Berger

  3. Department of Genetics Harvard Medical School, Boston, Massachusetts, USA

    Adam Friedman, Chris Bakal & Norbert Perrimon

  4. Howard Hughes Medical Institute, Harvard Medical School, Boston, Massachusetts, USA

    Adam Friedman, Chris Bakal & Norbert Perrimon

  5. Harvard–Massachusetts Institute of Technology Division of Health Sciences and Technology, Boston, Massachusetts, USA

    Adam Friedman & Bonnie Berger

Authors
  1. Irene M Kaplow
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  2. Rohit Singh
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  3. Adam Friedman
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  4. Chris Bakal
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  5. Norbert Perrimon
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  6. Bonnie Berger
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Corresponding authors

Correspondence to Norbert Perrimon or Bonnie Berger.

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1-12, Supplementary Tables 1-5, Supplementary Results and Supplementary Methods (PDF 849 kb)

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Kaplow, I., Singh, R., Friedman, A. et al. RNAiCut: automated detection of significant genes from functional genomic screens. Nat Methods 6, 476–477 (2009). https://doi.org/10.1038/nmeth0709-476

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