A Computational Algorithm to Predict shRNA Potency

The strength of conclusions drawn from RNAi-based studies is heavily influenced by the quality of tools used to elicit knockdown. Prior studies have developed algorithms to design siRNAs. However, to date, no established method has emerged to identify effective shRNAs, which have lower intracellular abundance than transfected siRNAs and undergo additional processing steps. We recently developed a multiplexed assay for identifying potent shRNAs and used this method to generate ∼250,000 shRNA efficacy data points. Using these data, we developed shERWOOD, an algorithm capable of predicting, for any shRNA, the likelihood that it will elicit potent target knockdown. Combined with additional shRNA design strategies, shERWOOD allows the ab initio identification of potent shRNAs that specifically target the majority of each gene’s multiple transcripts. We validated the performance of our shRNA designs using several orthogonal strategies and constructed genome-wide collections of shRNAs for humans and mice based on our approach. [Display omitted] •Key sequence characteristics for predicting shRNA potency are identified•An shRNA-specific algorithm allows for increases in shRNA screen quality•Structure-guided strategies allow for an expanded shRNA search space•An alternative miR scaffold increases shRNA processing and potency The identification of potent shRNAs is difficult, therefore limiting their utility for identifying gene targets. Knott et al. have developed a computational algorithm capable of predicting potent shRNAs for any target sequence. shRNAs selected with this algorithm were found to be more efficacious than those in currently available libraries.