Factors affecting reproducibility between genome-scale siRNA-based screens.

Barrows NJ, Le Sommer C, Garcia-Blanco MA, Pearson JL.

Citation

Barrows NJ, Le Sommer C, Garcia-Blanco MA, Pearson JL. (2010) Factors affecting reproducibility between genome-scale siRNA-based screens. Journal of Biomolecular Screening 15(7):735-747.

Abstract

RNA interference-based screening is a powerful new genomic technology that addresses gene function en masse. To evaluate factors influencing hit list composition and reproducibility, the authors performed 2 identically designed small interfering RNA (siRNA)-based, whole-genome screens for host factors supporting yellow fever virus infection. These screens represent 2 separate experiments completed 5 months apart and allow the direct assessment of the reproducibility of a given siRNA technology when performed in the same environment. Candidate hit lists generated by sum rank, median absolute deviation, z-score, and strictly standardized mean difference were compared within and between whole-genome screens. Application of these analysis methodologies within a single screening data set using a fixed threshold equivalent to a p-value < or = 0.001 resulted in hit lists ranging from 82 to 1140 members and highlighted the tremendous impact analysis methodology has on hit list composition. Intra- and interscreen reproducibility was significantly influenced by the analysis methodology and ranged from 32% to 99%. This study also highlighted the power of testing at least 2 independent siRNAs for each gene product in primary screens. To facilitate validation, the authors conclude by suggesting methods to reduce false discovery at the primary screening stage. In this study, they present the first comprehensive comparison of multiple analysis strategies and demonstrate the impact of the analysis methodology on the composition of the “hit list.” Therefore, they propose that the entire data set derived from functional genome-scale screens, especially if publicly funded, should be made available as is done with data derived from gene expression and genome-wide association studies.