Department of Statistics, University of Oxford, OX1 3TG, UK.
Bioinformatics. 2009 Dec 1;25(23):3166-73. doi: 10.1093/bioinformatics/btp569. Epub 2009 Oct 1.
Functional module detection within protein interaction networks is a challenging problem due to the sparsity of data and presence of errors. Computational techniques for this task range from purely graph theoretical approaches involving single networks to alignment of multiple networks from several species. Current network alignment methods all rely on protein sequence similarity to map proteins across species.
Here we carry out network alignment using a protein functional similarity measure. We show that using functional similarity to map proteins across species improves network alignment in terms of functional coherence and overlap with experimentally verified protein complexes. Moreover, the results from functional similarity-based network alignment display little overlap (<15%) with sequence similarity-based alignment. Our combined approach integrating sequence and function-based network alignment alongside graph clustering properties offers a 200% increase in coverage of experimental datasets and comparable accuracy to current network alignment methods.
Program binaries and source code is freely available at http://www.stats.ox.ac.uk/research/bioinfo/resources.
Supplementary data are available at Bioinformatics online.
由于数据稀疏和存在误差，在蛋白质相互作用网络中检测功能模块是一个具有挑战性的问题。用于此任务的计算技术从涉及单个网络的纯图论方法到来自多个物种的多个网络的对齐范围。当前的网络对齐方法都依赖于蛋白质序列相似性来在物种之间映射蛋白质。
在这里，我们使用蛋白质功能相似性度量进行网络对齐。我们表明，使用功能相似性在物种之间映射蛋白质可以提高功能一致性和与实验验证的蛋白质复合物的重叠度的网络对齐。此外，基于功能相似性的网络对齐的结果与基于序列相似性的对齐的结果重叠很少（<15%）。我们的综合方法将基于序列和功能的网络对齐与图聚类特性相结合，可将实验数据集的覆盖率提高 200%，并与当前的网络对齐方法具有可比的准确性。
程序二进制文件和源代码可在 http://www.stats.ox.ac.uk/research/bioinfo/resources 上免费获得。
补充数据可在生物信息学在线获得。
