ArticleNature biotechnology2017
Prediction of potent shRNAs with a sequential classification algorithm.
Article in Nature biotechnology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 134 papers.
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Who cites it
134 citing papers in PubMed.
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- Adaptable centriole biogenesis via the intrinsically disordered protein ALMS1.Nature communications · 2026Article
- High-titer modular retroviral vectors enabled by an antisense cassette design preventing dsRNA formation during virus production.Molecular therapy. Advances · 2026Article
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- Dysregulated nuclear Lamin B1 in DYT1 dystonia thickens nuclear lamina and disrupts 14-3-3 proteins.Cell death discovery · 2026Article
- CAG-targeting artificial miRNA with reduced off-target risk for efficient lowering of pathogenic polyglutamine proteins.NAR molecular medicine · 2026Article
- Fidelity-Ensuring Consistency of Mitosis is Safeguarded by the 53BP1-USP28-p53 Pathway.bioRxiv : the preprint server for biology · 2026Article
- Article
- DCAF12 Ubiquitin Ligase Promotes Lung Cancer Metastasis by Modulating the TRiC/CCT Chaperonin Complex.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- A cell-state axis underlying colonization in carcinomas with implications for metastasis risk prediction and interception.Cell reports · 2025Article
- shRNAI: A deep neural network for the design of highly potent shRNAs.Molecular therapy. Nucleic acids · 2025Article
- Glucose and Oxygen Metabolism Coordinate Human Cortical Developmental Decisions.bioRxiv : the preprint server for biology · 2025Article
74 more citing papers are in PubMed but not listed here.
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Authors and funding
19 authors.
Funding
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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