Limited Agreement of Independent RNAi Screens for Virus-Required Host Genes Owes More to False-Negative than False-Positive Factors
Limited Agreement of Independent RNAi Screens for Virus-Required Host Genes Owes More to False-Negative than False-Positive Factors
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DOI：
10.1371/journal.pcbi.1003235
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发表时间：
2013-09-01
影响因子：
4.3
通讯作者：
Ahlquist, Paul
中科院分区：
文献类型：
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作者：
Hao, Linhui;He, Qiuling;Ahlquist, Paul
全基因组RNA干扰（RNAI）分析是一种强大的方法，可以识别支持或调节选定生物学过程的基因函数。与其他一些全基因组方法分享的新出现的挑战是，独立的RNAi研究通常在其牵连基因列表中表现出有限的一致性。为了更好地理解这一点，我们分析了四项全基因组RNAi研究，这些研究鉴定了涉及流感病毒复制的宿主基因。这些研究共同识别并验证了614个细胞基因的作用，但是在四个基因列表中的成对重叠仅为3％至15％（平均6.7％）。但是，在多个研究中，许多功能类别的代表性过多。这些富集类别列表的成对重叠率很高，类似于19％，这意味着在研究中比在基因水平上明显的一致性更多。进一步探讨这一点，我们发现，独立研究与独立功能措施（如蛋白质 - 蛋白质相互作用）相互作用的网络中所涉及的基因列表高度高于偶然性预测的速率。我们还开发了一种基于模型的一般方法，以评估假阳性和假阴性因素的影响，并根据有限的研究（过程中涉及的基因总数）估计。对于流感病毒复制，这种新型统计方法估计涉及的细胞基因总数与2,800相似。我们实验和计算结果的这一其他方面和其他多个方面表明，遵循良好的质量控制实践时，研究之间的重叠率低主要是由于假阴性而不是假阳性基因识别。这些结果和方法对多种形式的全基因组分析具有影响和应用。
Systematic, genome-wide RNA interference (RNAi) analysis is a powerful approach to identify gene functions that support or modulate selected biological processes. An emerging challenge shared with some other genome-wide approaches is that independent RNAi studies often show limited agreement in their lists of implicated genes. To better understand this, we analyzed four genome-wide RNAi studies that identified host genes involved in influenza virus replication. These studies collectively identified and validated the roles of 614 cell genes, but pair-wise overlap among the four gene lists was only 3% to 15% (average 6.7%). However, a number of functional categories were overrepresented in multiple studies. The pair-wise overlap of these enriched-category lists was high, similar to 19%, implying more agreement among studies than apparent at the gene level. Probing this further, we found that the gene lists implicated by independent studies were highly connected in interacting networks by independent functional measures such as protein-protein interactions, at rates significantly higher than predicted by chance. We also developed a general, model-based approach to gauge the effects of false-positive and false-negative factors and to estimate, from a limited number of studies, the total number of genes involved in a process. For influenza virus replication, this novel statistical approach estimates the total number of cell genes involved to be similar to 2,800. This and multiple other aspects of our experimental and computational results imply that, when following good quality control practices, the low overlap between studies is primarily due to false negatives rather than false-positive gene identifications. These results and methods have implications for and applications to multiple forms of genome-wide analysis.