A probability-based approach for the analysis of large-scale RNAi screens

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.