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Nat Biotechnol. 2017 Apr;35(4):350-353. doi: 10.1038/nbt.3807. Epub 2017 Mar 6.

Prediction of potent shRNAs with a sequential classification algorithm.

Author information

1
Computational Biology Program, Memorial Sloan Kettering Cancer Center, New York, New York, USA.
2
Tri-Institutional Training Program in Computational Biology and Medicine, New York, New York, USA.
3
Department of Cancer Biology and Genetics, Memorial Sloan Kettering Cancer Center, New York, New York, USA.
4
Cell and Developmental Biology Program, Weill Graduate School of Medical Sciences, Cornell University, New York, New York, USA.
5
Machine Learning Group, Department of Computer Science, Berlin Institute of Technology, Berlin, Germany.
6
Mirimus Inc., Woodbury, New York, USA.
7
Research Institute of Molecular Pathology, Vienna Biocenter, Vienna, Austria.
8
RNAi Core, Memorial Sloan Kettering Cancer Center, New York, New York, USA.
9
Department of Computer Science, ETH Zurich, Zurich, Switzerland.
10
Howard Hughes Medical Institute and Memorial Sloan Kettering Cancer Center, New York, New York, USA.
11
Department of Molecular and Cell Biology, University of California, Berkeley, Berkeley, California, USA.

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.

PMID:
28263295
PMCID:
PMC5416823
DOI:
10.1038/nbt.3807
[Indexed for MEDLINE]
Free PMC Article

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