Increasing confidence of protein interactomes using network topological metrics
Increasing confidence of protein interactomes using network topological metrics
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DOI：
10.1093/bioinformatics/btl335
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发表时间：
2006-08-15
期刊：
影响因子：
5.8
通讯作者：
Ng, See-Kiong
中科院分区：
文献类型：
--
作者：
Chen, Jin;Hsu, Wynne;Ng, See-Kiong
动机：高通量蛋白质-蛋白质相互作用检测方法的实验限制导致了低质量的相互作用数据集，其中包含相当大比例的假阳性和假阴性。然后需要小规模的、有针对性的实验来补充高通量方法，以提取真正的蛋白质相互作用。然而，自然巨大的相互作用体将需要更多的可扩展的approaches.Results：我们描述了一种新的方法，称为IRAP（*）作为一个计算的补充高度错误的实验衍生的蛋白质相互作用体的再纯化。我们的方法涉及一个迭代过程，去除被确信为假阳性的相互作用，并将检测为假阴性的相互作用添加到相互作用组中。在IRAP（*）中使用基于网络拓扑度量的交互置信度度量来识别误报和漏报。在检测到的交互中，潜在的假阳性被识别为具有非常低的计算置信度值的交互，而潜在的假阴性被发现为具有高计算置信度值的未检测到的交互。我们将IRAP（*）应用于酵母、果蝇和蠕虫的流行酵母双杂交测定法生成的大规模相互作用数据集的结果表明，基于功能同质性，计算重新纯化的相互作用数据集包含的假阳性和假阴性错误可能更低。
Motivation: Experimental limitations in high-throughput protein-protein interaction detection methods have resulted in low quality interaction datasets that contained sizable fractions of false positives and false negatives. Small-scale, focused experiments are then needed to complement the high-throughput methods to extract true protein interactions. However, the naturally vast interactomes would require much more scalable approaches.Results: We describe a novel method called IRAP(*) as a computational complement for repurification of the highly erroneous experimentally derived protein interactomes. Our method involves an iterative process of removing interactions that are confidently identified as false positives and adding interactions detected as false negatives into the interactomes. Identification of both false positives and false negatives are performed in IRAP(*) using interaction confidence measures based on network topological metrics. Potential false positives are identified amongst the detected interactions as those with very low computed confidence values, while potential false negatives are discovered as the undetected interactions with high computed confidence values. Our results from applying IRAP(*) on large-scale interaction datasets generated by the popular yeast-two-hybrid assays for yeast, fruit fly and worm showed that the computationally repurified interaction datasets contained potentially lower fractions of false positive and false negative errors based on functional homogeneity.