Prediction of potent shRNAs with a sequential classification algorithm.
basic_science · Level V
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- Record sourced from PubMed, PMID 28263295.
- Also identified by DOI 10.1038/nbt.3807 and PMC identifier 5416823.
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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.
Medical subject headings
- Algorithms
- Clustered Regularly Interspaced Short Palindromic Repeats
- Gene Silencing
- Machine Learning
- RNA, Small Interfering
- Software