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首页> 外文期刊>BMC Genomics >A network-based integrative approach to prioritize reliable hits from multiple genome-wide RNAi screens in Drosophila
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A network-based integrative approach to prioritize reliable hits from multiple genome-wide RNAi screens in Drosophila

机译：一种基于网络的集成方法，可对果蝇中多个全基因组RNAi筛选的可靠命中进行优先排序

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Background The recently developed RNA interference (RNAi) technology has created an unprecedented opportunity which allows the function of individual genes in whole organisms or cell lines to be interrogated at genome-wide scale. However, multiple issues, such as off-target effects or low efficacies in knocking down certain genes, have produced RNAi screening results that are often noisy and that potentially yield both high rates of false positives and false negatives. Therefore, integrating RNAi screening results with other information, such as protein-protein interaction (PPI), may help to address these issues. Results By analyzing 24 genome-wide RNAi screens interrogating various biological processes in Drosophila, we found that RNAi positive hits were significantly more connected to each other when analyzed within a protein-protein interaction network, as opposed to random cases, for nearly all screens. Based on this finding, we developed a network-based approach to identify false positives (FPs) and false negatives (FNs) in these screening results. This approach relied on a scoring function, which we termed NePhe, to integrate information obtained from both PPI network and RNAi screening results. Using a novel rank-based test, we compared the performance of different NePhe scoring functions and found that diffusion kernel-based methods generally outperformed others, such as direct neighbor-based methods. Using two genome-wide RNAi screens as examples, we validated our approach extensively from multiple aspects. We prioritized hits in the original screens that were more likely to be reproduced by the validation screen and recovered potential FNs whose involvements in the biological process were suggested by previous knowledge and mutant phenotypes. Finally, we demonstrated that the NePhe scoring system helped to biologically interpret RNAi results at the module level. Conclusion By comprehensively analyzing multiple genome-wide RNAi screens, we conclude that network information can be effectively integrated with RNAi results to produce suggestive FPs and FNs, and to bring biological insight to the screening results.
机译：背景技术最近开发的RNA干扰（RNAi）技术创造了前所未有的机会，可以在整个基因组范围内查询整个生物体或细胞系中单个基因的功能。但是，多个问题，例如脱靶效应或敲除某些基因的效率低下，已经产生了RNAi筛选结果，该结果通常很嘈杂，并且可能同时导致假阳性和假阴性的发生率很高。因此，将RNAi筛查结果与其他信息（例如蛋白质-蛋白质相互作用（PPI））整合在一起，可能有助于解决这些问题。结果通过分析询问果蝇中各种生物学过程的24个全基因组RNAi筛选，我们发现，在几乎所有筛选中，与随机病例相比，在蛋白质-蛋白质相互作用网络中进行分析时，RNAi阳性命中物之间的联系明显更多。基于此发现，我们开发了一种基于网络的方法来识别这些筛查结果中的假阳性（FPs）和假阴性（FNs）。这种方法依靠评分功能（我们称为NePhe）来整合从PPI网络和RNAi筛选结果中获得的信息。通过使用基于等级的新颖测试，我们比较了不同的NePhe评分功能的性能，发现基于扩散核的方法通常优于其他方法，例如基于直接邻居的方法。以两个全基因组RNAi筛选为例，我们从多个方面广泛验证了我们的方法。我们在原始筛选中优先选择了可能被验证筛选重现的命中，并回收了潜在的FN，这些FN参与了生物学过程，这是先前的知识和突变表型所暗示的。最后，我们证明了NePhe评分系统有助于在模块水平上生物学解释RNAi结果。结论通过全面分析多种全基因组RNAi筛选，我们得出结论，网络信息可以与RNAi结果有效整合，以产生提示性的FP和FN，并为筛选结果带来生物学见解。

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