Ali Waqar, Rito Tiago, Reinert Gesine, Sun Fengzhu, Deane Charlotte M
Department of Statistics, University of Oxford, Oxford OX1 3TG, UK and Molecular and Computational Biology Program, Department of Biological Sciences, University of Southern California, CA 90089-2910, USA.
Bioinformatics. 2014 Sep 1;30(17):i430-7. doi: 10.1093/bioinformatics/btu447.
Biological network comparison software largely relies on the concept of alignment where close matches between the nodes of two or more networks are sought. These node matches are based on sequence similarity and/or interaction patterns. However, because of the incomplete and error-prone datasets currently available, such methods have had limited success. Moreover, the results of network alignment are in general not amenable for distance-based evolutionary analysis of sets of networks. In this article, we describe Netdis, a topology-based distance measure between networks, which offers the possibility of network phylogeny reconstruction.
We first demonstrate that Netdis is able to correctly separate different random graph model types independent of network size and density. The biological applicability of the method is then shown by its ability to build the correct phylogenetic tree of species based solely on the topology of current protein interaction networks. Our results provide new evidence that the topology of protein interaction networks contains information about evolutionary processes, despite the lack of conservation of individual interactions. As Netdis is applicable to all networks because of its speed and simplicity, we apply it to a large collection of biological and non-biological networks where it clusters diverse networks by type.
The source code of the program is freely available at http://www.stats.ox.ac.uk/research/proteins/resources.
Supplementary data are available at Bioinformatics online.
生物网络比较软件在很大程度上依赖于比对的概念,即寻找两个或多个网络节点之间的紧密匹配。这些节点匹配基于序列相似性和/或相互作用模式。然而,由于目前可用的数据集不完整且容易出错,此类方法取得的成功有限。此外,网络比对的结果通常不适用于基于距离的网络集进化分析。在本文中,我们描述了Netdis,一种基于拓扑结构的网络间距离度量方法,它提供了重建网络系统发育的可能性。
我们首先证明,Netdis能够正确区分不同的随机图模型类型,而与网络大小和密度无关。然后通过仅基于当前蛋白质相互作用网络的拓扑结构构建物种正确的系统发育树,展示了该方法在生物学上的适用性。我们的结果提供了新的证据,表明尽管个体相互作用缺乏保守性,但蛋白质相互作用网络的拓扑结构包含有关进化过程的信息。由于Netdis速度快且简单,适用于所有网络,我们将其应用于大量生物和非生物网络,按类型对不同网络进行聚类。
该程序的源代码可在http://www.stats.ox.ac.uk/research/proteins/resources免费获取。
补充数据可在《生物信息学》在线获取。