Abstract LB-271: SplashRNA, a sequential classification algorithm for ultra-potent RNAi

We present SplashRNA, a sequential classifier - analogous to face detection algorithms - to predict ultra-potent microRNA-based short hairpin RNAs (shRNAs) for virtually any gene. Trained on existing and novel large-scale datasets, SplashRNA outperforms previous algorithms and reliably predicts the most efficient shRNAs for a given gene. Combined with the 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. The open source SplashRNA platform completes the RNAi toolkit to harness microRNA-based shRNAs for robust single-gene and multiplexed inducible and reversible target inhibition. Citation Format: Raphael Pelossof, Lauren Fairchild, Christina S. Leslie, Christof Fellmann. SplashRNA, a sequential classification algorithm for ultra-potent RNAi [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr LB-271. doi:10.1158/1538-7445.AM2017-LB-271