A COMPUTATIONAL ALGORITHM TO PREDICT shRNA POTENCY
Knockdown of gene expression is an important tool for studying mechanisms in cancer. One of the most effective and easily manipulated experimental paradigms for gene knockdown is known as shRNA, for short-hairpin RNA. We constructed an algorithm called shERWOOD to predict the most effective sequence to use for efficacious knockdown of a gene in cells.
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.
RELEVANCE TO OC
shRNA knockdown of gene expression is an important tool for the study of ovarian cancer, and efficient design of these reagents is critical to study large numbers of genes in a hi-throughput manner.