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Mol Cell. 2014 Dec 18;56(6):796-807. doi: 10.1016/j.molcel.2014.10.025. Epub 2014 Nov 26.

A computational algorithm to predict shRNA potency.

Author information

1
Watson School of Biological Sciences, Howard Hughes Medical Institute, Cold Spring Harbor Laboratory, 1 Bungtown Road, Cold Spring Harbor, New York 11724, USA.
2
Cancer Research UK Cambridge Insitute, University of Cambridge, Li Ka Shing Centre, Robinson Way, Cambridge CB20RE, UK.
#
Contributed equally

Abstract

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.

PMID:
25435137
PMCID:
PMC4272634
DOI:
10.1016/j.molcel.2014.10.025
[Indexed for MEDLINE]
Free PMC Article

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