TopNet: a tool for comparing biological sub-networks, correlating protein properties with topological statistics
TopNet: a tool for comparing biological sub-networks, correlating protein properties with topological statistics
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DOI：
10.1093/nar/gkh164
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发表时间：
2004-01-01
影响因子：
14.9
通讯作者：
Gerstein, M
中科院分区：
文献类型：
--
作者：
Yu, HY;Zhu, XW;Gerstein, M
生物网络是一个当前非常感兴趣的话题，特别是随着许多大型全基因组相互作用数据集的出版。它们具有各种图论统计量的全局特征，如度分布、聚类系数、特征路径长度和直径。而且，真实的蛋白质网络非常复杂，通常可以通过系统地选择不同的节点和边来划分出许多子网络。例如，蛋白质可以根据表达水平、长度、氨基酸组成、溶解度、二级结构和功能进行细分。一个具有挑战性的研究问题是比较子网络的拓扑结构，寻找与不同类型的蛋白质相关的全局差异。TopNet是一个自动化的网络工具，旨在解决这个问题，计算和比较来自任何给定蛋白质网络的不同子网络的拓扑特征。它为嵌入在更大的网络中的子网络的网络统计计算提供了合理的解决方案，并提供了感兴趣的子网络的简化视图，允许人们浏览它。在构建了TopNet之后，我们将其应用于酵母现有的相互作用网络和蛋白质类。我们能够找到一些潜在的生物学相关性。特别是，我们发现可溶性蛋白比膜蛋白有更多的相互作用。此外，在可溶性蛋白中，高表达、极性氨基酸多、α -螺旋多的蛋白往往具有最多的相互作用伙伴。有趣的是，TopNet还在当前的酵母相互作用网络中发现了一些系统性偏差：平均而言，具有已知功能分类的蛋白质比那些没有功能分类的蛋白质有更多的相互作用伙伴。这种现象可能反映了实验确定的酵母相互作用网络的不完整性。
Biological networks are a topic of great current interest, particularly with the publication of a number of large genome-wide interaction datasets. They are globally characterized by a variety of graph-theoretic statistics, such as the degree distribution, clustering coefficient, characteristic path length and diameter. Moreover, real protein networks are quite complex and can often be divided into many sub-networks through systematic selection of different nodes and edges. For instance, proteins can be sub-divided by expression level, length, amino-acid composition, solubility, secondary structure and function. A challenging research question is to compare the topologies of sub- networks, looking for global differences associated with different types of proteins. TopNet is an automated web tool designed to address this question, calculating and comparing topological characteristics for different sub-networks derived from any given protein network. It provides reasonable solutions to the calculation of network statistics for sub-networks embedded within a larger network and gives simplified views of a sub-network of interest, allowing one to navigate through it. After constructing TopNet, we applied it to the interaction networks and protein classes currently available for yeast. We were able to find a number of potential biological correlations. In particular, we found that soluble proteins had more interactions than membrane proteins. Moreover, amongst soluble proteins, those that were highly expressed, had many polar amino acids, and had many alpha helices, tended to have the most interaction partners. Interestingly, TopNet also turned up some systematic biases in the current yeast interaction network: on average, proteins with a known functional classification had many more interaction partners than those without. This phenomenon may reflect the incompleteness of the experimentally determined yeast interaction network.