Dutta Bhaskar, Azhir Alaleh, Merino Louis-Henri, Guo Yongjian, Revanur Swetha, Madhamshettiwar Piyush B, Germain Ronald N, Smith Jennifer A, Simpson Kaylene J, Martin Scott E, Buehler Eugen, Fraser Iain D C
Laboratory of Systems Biology, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, Maryland 20892, USA.
Victorian Centre for Functional Genomics, Peter MacCallum Cancer Centre, East Melbourne, Victoria 3002, Australia.
Nat Commun. 2016 Feb 23;7:10578. doi: 10.1038/ncomms10578.
RNAi screens are widely used in functional genomics. Although the screen data can be susceptible to a number of experimental biases, many of these can be corrected by computational analysis. For this purpose, here we have developed a web-based platform for integrated analysis and visualization of RNAi screen data named CARD (for Comprehensive Analysis of RNAi Data; available at https://card.niaid.nih.gov). CARD allows the user to seamlessly carry out sequential steps in a rigorous data analysis workflow, including normalization, off-target analysis, integration of gene expression data, optimal thresholds for hit selection and network/pathway analysis. To evaluate the utility of CARD, we describe analysis of three genome-scale siRNA screens and demonstrate: (i) a significant increase both in selection of subsequently validated hits and in rejection of false positives, (ii) an increased overlap of hits from independent screens of the same biology and (iii) insight to microRNA (miRNA) activity based on siRNA seed enrichment.
RNA干扰筛选在功能基因组学中被广泛应用。尽管筛选数据可能易受多种实验偏差的影响，但其中许多偏差可通过计算分析加以校正。为此，我们开发了一个名为CARD（RNA干扰数据综合分析；网址为https://card.niaid.nih.gov）的基于网络的平台，用于RNA干扰筛选数据的综合分析和可视化。CARD允许用户在严格的数据分析工作流程中无缝执行连续步骤，包括标准化、脱靶分析、基因表达数据整合、命中选择的最佳阈值以及网络/通路分析。为评估CARD的效用，我们描述了对三个全基因组规模的小干扰RNA筛选的分析，并证明：（i）后续验证命中的选择以及假阳性的排除均显著增加；（ii）来自相同生物学的独立筛选的命中重叠增加；（iii）基于小干扰RNA种子富集对微小RNA（miRNA）活性的深入了解。
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