Unraveling protein networks with power graph analysis.
Unraveling protein networks with power graph analysis.
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DOI：
10.1371/journal.pcbi.1000108
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发表时间：
2008-07-11
影响因子：
4.3
通讯作者：
Schroeder M
中科院分区：
文献类型：
--
作者：
Royer L;Reimann M;Andreopoulos B;Schroeder M
网络在计算生物学中扮演着至关重要的角色，然而它们的分析和表示仍然是一个悬而未决的问题。幂图分析是一种无损的生物网络转换成一个紧凑的，少冗余的表示，利用丰富的团和自行车作为基本拓扑基元。我们用五个例子来说明功率图分析的优点。研究蛋白-蛋白相互作用网络，我们展示了酪蛋白激酶II复合物的催化亚基如何与调节亚基区分，相互作用谱和SH3结构域的序列系统发育如何相关，以及如何发现高通量相互作用之间的假阳性相互作用。此外，我们通过将功率图分析应用于其他两种类型的网络来证明其通用性。我们展示了功率图如何在双部转录网络中诱导转录因子和靶基因的聚类，以及如何检测22型非受体酪氨酸磷酸酶中磷酸酶结构域的侵蚀。我们将功率图分析应用于高通量蛋白质相互作用网络，并显示高达85%（平均56%）的信息是冗余的。实验网络比同度分布的重新布线网络具有更大的可压缩性，这表明实验网络具有丰富的团和团。功率图是一种新颖的网络表示，它通过显式表示重复出现的网络主题来降低网络复杂性。功率图在蛋白质相互作用网络中压缩了85%的边，适用于所有类型的网络，如蛋白质相互作用、调节网络或同源网络。网络在生物学中起着至关重要的作用，经常被用作表示实验结果的一种方式。然而，它们的分析和表征仍然是一个悬而未决的问题。最近的实验和计算进展产生了更大更复杂的网络。例如，有小型和大型相互作用网络、调节网络、遗传网络、蛋白质-配体相互作用网络和同源网络，定期分析和发表。访问网络中信息的一种常用方法是直接可视化，但这种方法失败了，因为它通常只会产生“毛球”，从中收集不到什么见解。另一方面，聚类技术通过对网络进行粗粒度处理，从而抽象出细节，从而避免了大量节点甚至更多边缘所带来的问题。但这些也都失败了，因为事实上，生物学的大部分都在于细节。这项工作提出了一种分析和表示网络的新方法。功率图是网络的无损表示，它通过显式表示重复出现的网络主题来降低网络复杂性。此外，功率图可以清晰地可视化：它们可以压缩生物网络中高达90%的边缘，并且适用于所有类型的网络，例如蛋白质相互作用，调节网络或同源网络。
Networks play a crucial role in computational biology, yet their analysis and representation is still an open problem. Power Graph Analysis is a lossless transformation of biological networks into a compact, less redundant representation, exploiting the abundance of cliques and bicliques as elementary topological motifs. We demonstrate with five examples the advantages of Power Graph Analysis. Investigating protein-protein interaction networks, we show how the catalytic subunits of the casein kinase II complex are distinguishable from the regulatory subunits, how interaction profiles and sequence phylogeny of SH3 domains correlate, and how false positive interactions among high-throughput interactions are spotted. Additionally, we demonstrate the generality of Power Graph Analysis by applying it to two other types of networks. We show how power graphs induce a clustering of both transcription factors and target genes in bipartite transcription networks, and how the erosion of a phosphatase domain in type 22 non-receptor tyrosine phosphatases is detected. We apply Power Graph Analysis to high-throughput protein interaction networks and show that up to 85% (56% on average) of the information is redundant. Experimental networks are more compressible than rewired ones of same degree distribution, indicating that experimental networks are rich in cliques and bicliques. Power Graphs are a novel representation of networks, which reduces network complexity by explicitly representing re-occurring network motifs. Power Graphs compress up to 85% of the edges in protein interaction networks and are applicable to all types of networks such as protein interactions, regulatory networks, or homology networks. Networks play a crucial role in biology and are often used as a way to represent experimental results. Yet, their analysis and representation is still an open problem. Recent experimental and computational progress yields networks of increased size and complexity. There are, for example, small- and large-scale interaction networks, regulatory networks, genetic networks, protein-ligand interaction networks, and homology networks analyzed and published regularly. A common way to access the information in a network is though direct visualization, but this fails as it often just results in “fur balls” from which little insight can be gathered. On the other hand, clustering techniques manage to avoid the problems caused by the large number of nodes and even larger number of edges by coarse-graining the networks and thus abstracting details. But these also fail, since, in fact, much of the biology lies in the details. This work presents a novel methodology for analyzing and representing networks. Power Graphs are a lossless representation of networks, which reduces network complexity by explicitly representing re-occurring network motifs. Moreover, power graphs can be clearly visualized: they compress up to 90% of the edges in biological networks and are applicable to all types of networks such as protein interaction, regulatory networks, or homology networks.
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