König Renate, Chiang Chih-yuan, Tu Buu P, Yan S Frank, DeJesus Paul D, Romero Angelica, Bergauer Tobias, Orth Anthony, Krueger Ute, Zhou Yingyao, Chanda Sumit K
The Genomics Institute of the Novartis Research Foundation, 10675 John J Hopkins Drive, San Diego, California 92121, USA.
Nat Methods. 2007 Oct;4(10):847-9. doi: 10.1038/nmeth1089. Epub 2007 Sep 9.
We describe a statistical analysis methodology designed to minimize the impact of off-target activities upon large-scale RNA interference (RNAi) screens in mammalian cells. Application of this approach enhances reconfirmation rates and facilitates the experimental validation of new gene activities through the probability-based identification of multiple distinct and active small interfering RNAs (siRNAs) targeting the same gene. We further extend this approach to establish that the optimal redundancy for efficacious RNAi collections is between 4-6 siRNAs per gene.
我们描述了一种统计分析方法，旨在将脱靶活性对哺乳动物细胞大规模RNA干扰（RNAi）筛选的影响降至最低。这种方法的应用提高了重新确认率，并通过基于概率识别针对同一基因的多个不同且活跃的小干扰RNA（siRNA），促进了新基因活性的实验验证。我们进一步扩展了这种方法，以确定有效的RNAi文库的最佳冗余度为每个基因4-6个siRNA。
