Functional identification of optimized RNAi triggers using a massively parallel sensor assay

Abstract

Short hairpin RNAs (shRNAs) provide powerful experimental tools by enabling stable and regulated gene silencing through programming of endogenous microRNA pathways. Since requirements for efficient shRNA biogenesis and target suppression are largely unknown, many predicted shRNAs fail to efficiently suppress their target. To overcome this barrier, we developed a "Sensor assay" that enables the biological identification of effective shRNAs at large scale. By constructing and evaluating 20,000 RNAi reporters covering every possible target site in nine mammalian transcripts, we show that our assay reliably identifies potent shRNAs that are surprisingly rare and predominantly missed by existing algorithms. Our unbiased analyses reveal that potent shRNAs share various predicted and previously unknown features associated with specific microRNA processing steps, and suggest a model for competitive strand selection. Together, our study establishes a powerful tool for large-scale identification of highly potent shRNAs and provides insights into sequence requirements of effective RNAi.