Prediction of Potent ShRNAs with a Sequential Classification Algorithm

Overview

Journal Nat Biotechnol

Specialty Biotechnology

Date 2017 Mar 7

PMID 28263295

Citations 97

Authors

Raphael Pelossof

Lauren Fairchild

Chun-Hao Huang

Christian Widmer

Vipin T Sreedharan

Nishi Sinha

Dan-Yu Lai

Yuanzhe Guan

Prem K Premsrirut

Darjus F Tschaharganeh

Thomas Hoffmann

Vishal Thapar

Qing Xiang

Ralph J Garippa

Gunnar Ratsch

Johannes Zuber

Scott W Lowe

Christina S Leslie

Christof Fellmann

Affiliations

Soon will be listed here.

Abstract

We present SplashRNA, a sequential classifier to predict potent microRNA-based short hairpin RNAs (shRNAs). Trained on published and novel data sets, SplashRNA outperforms previous algorithms and reliably predicts the most efficient shRNAs for a given gene. Combined with an optimized miR-E backbone, >90% of high-scoring SplashRNA predictions trigger >85% protein knockdown when expressed from a single genomic integration. SplashRNA can significantly improve the accuracy of loss-of-function genetics studies and facilitates the generation of compact shRNA libraries.

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