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...

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Bibliographic Details
Published in:Molecular cell Vol. 56; no. 6; p. 796
Main Authors: Knott, Simon R V, Maceli, Ashley, Erard, Nicolas, Chang, Kenneth, Marran, Krista, Zhou, Xin, Gordon, Assaf, Demerdash, Osama El, Wagenblast, Elvin, Kim, Sun, Fellmann, Christof, Hannon, Gregory J
Format: Journal Article
Language:English
Published: United States 18.12.2014
Subjects:
Algorithms
Base Sequence
Cell Line, Tumor
Computer Simulation
Consensus Sequence
Gene Knockdown Techniques
Humans
MicroRNAs - genetics
Models, Genetic
Molecular Sequence Data
RNA, Small Interfering - genetics
Software
ISSN:1097-4164, 1097-4164
Online Access:Get more information
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Summary: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.
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ISSN:1097-4164
1097-4164
DOI:10.1016/j.molcel.2014.10.025