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Pelossof, Raphael; Fairchild, Lauren; Huang, Chun-Hao; Widmer, Christian; Sreedharan, Vipin T; Sinha, Nishi; Lai, Dan-Yu; Guan, Yuanzhe; Premsrirut, Prem K; Tschaharganeh, Darjus F; Hoffmann, Thomas; Thapar, Vishal; Xiang, Qing; Garippa, Ralph J; Rätsch, Gunnar; Zuber, Johannes; Lowe, Scott W; Leslie, Christina S; Fellmann, Christof
Nature biotechnology, 04/2017, Volume: 35, Issue: 4Journal Article
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.
