Journal
NATURE BIOTECHNOLOGY
Volume 35, Issue 4, Pages 350-+Publisher
NATURE PUBLISHING GROUP
DOI: 10.1038/nbt.3807
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Funding
- K99/R00 Pathway to Independence Award National Institutes of Health (NIH), National Institute of General Medical Sciences (NIGMS) [K99GM118909]
- NHGRI U01 grants [HG007033, HG007893]
- NCI U01 grant [CA164190]
- [CA013106]
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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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