ScreenBEAM: a novel meta-analysis algorithm for functional genomics screens via Bayesian hierarchical modeling
ScreenBEAM: a novel meta-analysis algorithm for functional genomics screens via Bayesian hierarchical modeling
复制标题
DOI：
10.1093/bioinformatics/btv556
复制
发表时间：
2016-01-15
期刊：
影响因子：
5.8
通讯作者：
Califano, Andrea
中科院分区：
文献类型：
--
作者：
Yu, Jiyang;Silva, Jose;Califano, Andrea
动机：使用RNAi或CRISPR技术的功能基因组学（FG）筛选已成为系统性、全基因组功能缺失研究的标准工具，用于发现治疗靶点。然而，在许多大规模测定中，必须考虑脱靶效应、可变试剂效力和实验噪声，以适当控制假阳性。事实上，高通量FG筛选数据的严格统计分析仍然具有挑战性，特别是当整合分析用于组合文库中靶向相同基因联合收割机多个sh/sgRNA时。方法：我们使用公开可用的大型RNAi和CRISPR库，通过贝叶斯分层建模，筛选贝叶斯评估和分析方法，评估FG筛选的新荟萃分析方法结果：我们的分析结果表明，所提出的策略，它无缝地结合了所有可用的数据，鲁棒性优于经典的微阵列数据集的算法，以及最近的方法设计的下一代测序技术。值得注意的是，ScreenBEAM算法即使在FG屏幕质量相对较低的情况下也能很好地工作，FG屏幕约占公共数据集的80-95%。
Motivation: Functional genomics (FG) screens, using RNAi or CRISPR technology, have become a standard tool for systematic, genome-wide loss-of-function studies for therapeutic target discovery. As in many large-scale assays, however, off-target effects, variable reagents' potency and experimental noise must be accounted for appropriately control for false positives. Indeed, rigorous statistical analysis of high-throughput FG screening data remains challenging, particularly when integrative analyses are used to combine multiple sh/sgRNAs targeting the same gene in the library.Method: We use large RNAi and CRISPR repositories that are publicly available to evaluate a novel meta-analysis approach for FG screens via Bayesian hierarchical modeling, Screening Bayesian Evaluation and Analysis Method (ScreenBEAM).Results: Results from our analysis show that the proposed strategy, which seamlessly combines all available data, robustly outperforms classical algorithms developed for microarray data sets as well as recent approaches designed for next generation sequencing technologies. Remarkably, the ScreenBEAM algorithm works well even when the quality of FG screens is relatively low, which accounts for about 80-95% of the public datasets.