Abstract

We present SplashRNA, a sequential classifier to predict potent microRNA-based short hairpin RNAs (shRNAs). Trained on published and novel data sets, SplashRNA outperforms previous algorithms and reliably predicts the most efficient shRNAs for a given gene. Combined with an optimized miR-E backbone, >90% of high-scoring SplashRNA predictions trigger >85% protein knockdown when expressed from a single genomic integration. SplashRNA can significantly improve the accuracy of loss-of-function genetics studies and facilitates the generation of compact shRNA libraries.

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Acknowledgements

We thank J.A. Doudna, G.J. Hannon, L.E. Dow and S.N. Floor for continuous support and valuable discussions. We gratefully acknowledge assistance and support from A. Banito, V. Sridhar, L. Faletti, C.C. Chen and S. Tian. C.F. was supported in part by a K99/R00 Pathway to Independence Award (K99GM118909) from the National Institutes of Health (NIH), National Institute of General Medical Sciences (NIGMS). C.F. is a founder of Mirimus Inc., a company that develops RNAi-based reagents and transgenic mice. This work was also supported in part by grant CA013106 (S.W.L.). S.W.L. is a founder and member of the scientific advisory board of Mirimus Inc., the Geoffrey Beene Chair of Cancer Biology at MSKCC and an investigator of the Howard Hughes Medical Institute. J.Z. is a member of the scientific advisory board, and P.K.P. is a founder and employee of Mirimus Inc. C.S.L. was supported in part by NHGRI U01 grants HG007033 and HG007893 and NCI U01 grant CA164190. A375 cells were a kind gift from Neal Rosen, MSKCC.

Author information

Author notes

    • Raphael Pelossof
    •  & Lauren Fairchild

    These authors contributed equally to this work.

Affiliations

  1. Computational Biology Program, Memorial Sloan Kettering Cancer Center, New York, New York, USA.

    • Raphael Pelossof
    • , Lauren Fairchild
    • , Christian Widmer
    • , Vipin T Sreedharan
    • , Gunnar Rätsch
    •  & Christina S Leslie
  2. Tri-Institutional Training Program in Computational Biology and Medicine, New York, New York, USA.

    • Lauren Fairchild
  3. Department of Cancer Biology and Genetics, Memorial Sloan Kettering Cancer Center, New York, New York, USA.

    • Chun-Hao Huang
    • , Darjus F Tschaharganeh
    • , Vishal Thapar
    •  & Scott W Lowe
  4. Cell and Developmental Biology Program, Weill Graduate School of Medical Sciences, Cornell University, New York, New York, USA.

    • Chun-Hao Huang
    •  & Scott W Lowe
  5. Machine Learning Group, Department of Computer Science, Berlin Institute of Technology, Berlin, Germany.

    • Christian Widmer
  6. Mirimus Inc., Woodbury, New York, USA.

    • Nishi Sinha
    • , Dan-Yu Lai
    • , Yuanzhe Guan
    • , Prem K Premsrirut
    •  & Christof Fellmann
  7. Research Institute of Molecular Pathology, Vienna Biocenter, Vienna, Austria.

    • Thomas Hoffmann
    •  & Johannes Zuber
  8. RNAi Core, Memorial Sloan Kettering Cancer Center, New York, New York, USA.

    • Qing Xiang
    •  & Ralph J Garippa
  9. Department of Computer Science, ETH Zurich, Zurich, Switzerland.

    • Gunnar Rätsch
  10. Howard Hughes Medical Institute and Memorial Sloan Kettering Cancer Center, New York, New York, USA.

    • Scott W Lowe
  11. Department of Molecular and Cell Biology, University of California, Berkeley, Berkeley, California, USA.

    • Christof Fellmann

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Contributions

R.P., L.F., C.S.L. and C.F. conceived and designed the study, and developed the data integration framework. R.P., L.F., and C.W. built the algorithm, and carried out the model training and computational validation. C.-H.H., N.S., D.-Y.L., Y.G., P.K.P., D.F.T., T.H., J.Z., S.W.L. and C.F. generated the biological data sets and validated knockdown potency. R.P., L.F., C.W. and V.T.S. built the web page. V.T. and G.R. assisted with study design and advised on algorithmic development. Q.X. and R.J.G. helped with validation of predictions. R.P., L.F., C.-H.H., T.H., J.Z., S.W.L., C.S.L. and C.F. analyzed data and wrote the manuscript.

Competing interests

C.F. is a founder of Mirimus Inc., a company that develops RNAi-based reagents and transgenic mice. S.W.L. is a founder and member of the scientific advisory board of Mirimus Inc. J.Z. is a member of the scientific advisory board of Mirimus Inc. P.K.P. is a founder and employee of Mirimus Inc. R.P. and L.F. have filed intellectual property on SplashRNA.

Corresponding authors

Correspondence to Christina S Leslie or Christof Fellmann.

Integrated supplementary information

Supplementary information

PDF files

  1. 1.

    Supplementary Text and Figures

    Supplementary Figures 1–6 and Supplementary Table 1

Excel files

  1. 1.

    Supplementary Table 2

    Novel datasets and sequences of validated shRNAs

  2. 2.

    Supplementary Table 3

    Genome-wide SplashRNA predictions for all human and mouse protein coding genes.

Zip files

  1. 1.

    Supplementary Code

    Source code that implements the main SplashRNA algorithm

About this article

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DOI

https://doi.org/10.1038/nbt.3807

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