Prediction of potent shRNAs with a sequential classification algorithm

The most effective shRNAs to silence a gene are calculated by a machine learning algorithm. 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 pre...

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Vydáno v:	Nature biotechnology Ročník 35; číslo 4; s. 350 - 353
Hlavní autoři:	Pelossof, Raphael, Fairchild, Lauren, Huang, Chun-Hao, Widmer, Christian, Sreedharan, Vipin T, Sinha, Nishi, Lai, Dan-Yu, Guan, Yuanzhe, Premsrirut, Prem K, Tschaharganeh, Darjus F, Hoffmann, Thomas, Thapar, Vishal, Xiang, Qing, Garippa, Ralph J, Rätsch, Gunnar, Zuber, Johannes, Lowe, Scott W, Leslie, Christina S, Fellmann, Christof
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	New York Nature Publishing Group US 01.04.2017 Nature Publishing Group
Témata:	13/44 13/89 631/114/1305 631/114/2401 631/337/505 631/61/191/505 631/67/68 Agriculture Algorithms Analysis Bioinformatics Biology Biomedical Engineering/Biotechnology Biomedicine Biotechnology brief-communication Cancer Chromosome Mapping - methods Classification Clustered Regularly Interspaced Short Palindromic Repeats - genetics Computer science CRISPR-Cas Systems - genetics Datasets Gene Silencing Genetics Life Sciences Machine Learning Methods MicroRNA MicroRNAs Physiological aspects RNA sequencing RNA, Small Interfering - genetics Sequence Analysis, RNA - methods Software Support vector machines New York United States > US California
ISSN:	1087-0156, 1546-1696, 1546-1696
On-line přístup:	Získat plný text
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Popis
Shrnutí:	The most effective shRNAs to silence a gene are calculated by a machine learning algorithm. 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.
Bibliografie:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23
ISSN:	1087-0156 1546-1696 1546-1696
DOI:	10.1038/nbt.3807