Prediction of potent shRNAs with a sequential classification algorithm.

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.

SUBMITTER: Pelossof R 

PROVIDER: S-EPMC5416823 | biostudies-literature | 2017 Apr

REPOSITORIES: biostudies-literature