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A bioinformatics method identifies prominent off-targeted transcripts in RNAi screens

Nature Methods volume 9, pages 363–366 (2012)Cite this article

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Abstract

Because off-target effects hamper interpretation and validation of RNAi screen data, we developed a bioinformatics method, genome-wide enrichment of seed sequence matches (GESS), to identify candidate off-targeted transcripts in primary screening data. GESS analysis revealed a prominent off-targeted transcript in several screens, including MAD2 (MAD2L1) in a screen for genes required for the spindle assembly checkpoint. GESS analysis results can enhance the validation rate in RNAi screens.

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Figure 1: Summary of the GESS method.
Figure 2: Identification of major off-targeted transcripts in RNAi-screen datasets using the GESS method.
Figure 3: GESS-informed selection of siRNA pools enriches for genes that reproduce the primary phenotype upon targeting with additional siRNAs.

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Acknowledgements

We thank members of the Institute of Chemistry and Cell Biology and C. Shamu for providing siRNA sequences for hits from the Eg5-inhibitor RNAi screen as well as the use of facility equipment for our screening experiments, S. Natesan and P. August for helpful discussions in early stages of this work, S. Elledge for helpful discussions and for critical reading of the manuscript and J. Ware at the Harvard Catalyst Biostatistics consulting group for help in devising the statistical analysis workflow in the present manuscript. Funding for statistical analysis was supported in part by grant 1 UL1 RR025758-01, Harvard Clinical and Translational Science Center, from the US National Center for Research Resources; the content is solely the responsibility of the authors and does not necessarily represent the official views of the National Center for Research Resources or the US National Institutes of Health. This research was funded by a Sanofi-Aventis grant and US National Institutes of Health grant GM66492 to R.W.K.

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Author notes

  1. Susan Lyman, Jeremy F Huckins & Brian Quattrochi

    Present address: Present addresses: Gilead Sciences, Inc., Foster City, California, USA (S.L.), Department of Psychology and Brain Sciences, Dartmouth College, Hanover, New Hampshire, USA (J.F.H.) and Center for Academy Achievements, University of Massachusetts Medical School, Worcester, Massachusetts, USA (B.Q.).,

Authors and Affiliations

  1. Department of Cell Biology, Harvard Medical School, Boston, Massachusetts, USA

    Frederic D Sigoillot, Susan Lyman, Jeremy F Huckins, Eunah Chung, Brian Quattrochi & Randall W King

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

    Britt Adamson

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  1. Frederic D Sigoillot
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Contributions

F.D.S., S.L. and R.W.K. conceived the study. F.D.S., S.L., E.C., B.Q. and B.A. performed the experiments. F.D.S. and J.F.H. wrote the GESS program code. F.D.S. and R.W.K. wrote the manuscript.

Corresponding author

Correspondence to Randall W King.

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Competing interests

The authors declare no competing financial interests.

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–12, Supplementary Tables 1–3, Supplementary Results 1–4 (PDF 1158 kb)

Supplementary Data 1

All siRNA sequences and associated phenotype data files analyzed in this report are provided as a compressed archive file. (ZIP 328 kb)

Supplementary Data 2

All GESS analysis result excel files are provided as a compressed archive file. (ZIP 9541 kb)

Supplementary Data 3

Transcript sequence databases for the human and mouse genomes. (ZIP 47153 kb)

Supplementary Software 1

GESS standalone package with manual, Windows 32-bit. (ZIP 181068 kb)

Supplementary Software 2

GESS standalone package with manual, Windows 64-bit. (ZIP 203935 kb)

Supplementary Software 3

GESS standalone package with manual, Linux 32-bit. (ZIP 403658 kb)

Supplementary Software 4

GESS standalone package with manual, Linux 64-bit. (ZIP 245937 kb)

Supplementary Software 5

GESS standalone package with manual, Mac. (ZIP 159083 kb)

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Sigoillot, F., Lyman, S., Huckins, J. et al. A bioinformatics method identifies prominent off-targeted transcripts in RNAi screens. Nat Methods 9, 363–366 (2012). https://doi.org/10.1038/nmeth.1898

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