Investigating the validity of current network analysis on static conglomerate networks by protein network stratification.
Investigating the validity of current network analysis on static conglomerate networks by protein network stratification.
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DOI：
10.1186/1471-2105-11-466
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发表时间：
2010-09-16
影响因子：
3
通讯作者：
Lu LJ
中科院分区：
文献类型：
--
作者：
Zhang M;Lu LJ
分子网络的观点构成了系统生物学的基础。分析蛋白质-蛋白质相互作用（PPI）网络的一种常见做法是对一个综合网络进行网络分析，该网络是给定生物体中来自不同数据源的所有可用二元相互作用的集合。最近对网络动力学的研究表明，这种方法可能忽略了依赖于上下文的分子系统的动态特性。在本研究中，我们采用网络分层策略来考察当前网络分析对企业集团PPI网络的有效性。利用拟南芥基因组规模的组织和条件特异性蛋白质组学数据，我们在这里提出了对这个问题的第一个系统调查。我们将一个综合的拟南芥PPI网络分层为三个层次的上下文相关子网络。然后，我们重点研究了三种最常用的网络分析，即拓扑分析、功能分析和模块化分析，并将这些网络分析的结果与集团网络和对应于特定组织的五个分层上下文相关子网络进行了比较。我们发现基于综合PPI网络的结果通常与对应于特定组织或条件的上下文相关子网络的结果显着不同。这个结论既不依赖于相对任意的截止值（比如那些定义网络集线器或瓶颈的截止值），也不依赖于模块提取的特定网络聚类算法，也不依赖于PPI网络中二元相互作用可能出现的高假阳性率。我们还发现，我们的结论可能在人类PPI网络中是有效的。此外，网络分层可能有助于解决当前系统生物学研究中的许多争议。
A molecular network perspective forms the foundation of systems biology. A common practice in analyzing protein-protein interaction (PPI) networks is to perform network analysis on a conglomerate network that is an assembly of all available binary interactions in a given organism from diverse data sources. Recent studies on network dynamics suggested that this approach might have ignored the dynamic nature of context-dependent molecular systems. In this study, we employed a network stratification strategy to investigate the validity of the current network analysis on conglomerate PPI networks. Using the genome-scale tissue- and condition-specific proteomics data in Arabidopsis thaliana, we present here the first systematic investigation into this question. We stratified a conglomerate A. thaliana PPI network into three levels of context-dependent subnetworks. We then focused on three types of most commonly conducted network analyses, i.e., topological, functional and modular analyses, and compared the results from these network analyses on the conglomerate network and five stratified context-dependent subnetworks corresponding to specific tissues. We found that the results based on the conglomerate PPI network are often significantly different from those of context-dependent subnetworks corresponding to specific tissues or conditions. This conclusion depends neither on relatively arbitrary cutoffs (such as those defining network hubs or bottlenecks), nor on specific network clustering algorithms for module extraction, nor on the possible high false positive rates of binary interactions in PPI networks. We also found that our conclusions are likely to be valid in human PPI networks. Furthermore, network stratification may help resolve many controversies in current research of systems biology.
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影响因子：
14.9
作者：
Cui, Jian;Li, Peng;Li, Guang;Xu, Feng;Zhao, Chen;Li, Yuhua;Yang, Zhongnan;Wang, Guang;Yu, Qingbo;Li, Yixue;Shi, Tieliu
通讯作者：
Shi, Tieliu
影响因子：
9.8
作者：
Batada NN;Reguly T;Breitkreutz A;Boucher L;Breitkreutz BJ;Hurst LD;Tyers M
通讯作者：
Tyers M
影响因子：
64.8
作者：
Gavin, AC;Bösche, M;Superti-Furga, G
通讯作者：
Superti-Furga, G
影响因子：
56.9
作者：
de Lichtenberg, U;Jensen, LJ;Bork, P
通讯作者：
Bork, P
DOI：
10.1093/bioinformatics/btn640
发表时间：
2009-02-01
期刊：
Bioinformatics (Oxford, England)
影响因子：
--
作者：
Huang H;Bader JS
通讯作者：
Bader JS