Prediction of potent shRNAs with a sequential classification algorithm

Nat Biotechnol. 2017 Apr;35(4):350-353. doi: 10.1038/nbt.3807. Epub 2017 Mar 6.


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.

Publication types

  • Research Support, Non-U.S. Gov't
  • Research Support, N.I.H., Extramural

MeSH terms

  • Algorithms*
  • CRISPR-Cas Systems / genetics
  • Chromosome Mapping / methods
  • Clustered Regularly Interspaced Short Palindromic Repeats / genetics*
  • Gene Silencing*
  • Machine Learning*
  • RNA, Small Interfering / genetics*
  • Sequence Analysis, RNA / methods
  • Software*


  • RNA, Small Interfering