GraphCrunch 2: Software tool for network modeling, alignment and clustering.
GraphCrunch 2: Software tool for network modeling, alignment and clustering.
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DOI：
10.1186/1471-2105-12-24
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发表时间：
2011-01-19
影响因子：
3
通讯作者：
Pržulj N
中科院分区：
文献类型：
--
作者：
Kuchaiev O;Stevanović A;Hayes W;Pržulj N
实验生物技术的最新进展产生了大量的蛋白质-蛋白质相互作用（PPI）数据。PPI网络的拓扑结构被认为与它们的功能有很强的联系。因此，许多生物体的PPI数据的丰富刺激了网络建模、比较、对齐和聚类计算技术的发展。此外，寻找PPI网络的代表性模型将提高我们对细胞的理解，就像重力模型帮助我们理解行星运动一样。为了确定一个模型是否具有代表性，我们需要将模型网络与实际网络进行定量比较。然而，精确的网络比较在计算上是难以处理的，因此使用了几种启发式方法来代替。其中一些启发式方法是易于计算的“网络属性”，例如度分布或聚类系数。网络比较的一个重要特例是网络对齐问题。与序列比对类似，这个问题要求找到两个网络中区域之间的“最佳”映射。预计网络比对可能会像序列比对一样对我们对生物学的理解产生强烈的影响。PPI网络中基于拓扑的节点聚类是一个重要的网络分析问题的另一个例子，它可以揭示交互模式和表型之间的关系。我们介绍GraphCrunch 2软件工具，它可以解决这些问题。它是GraphCrunch的一个重要扩展，它实现了最流行的随机网络模型，并将它们与数据网络进行了许多网络属性的比较。此外，GraphCrunch 2实现了用于纯拓扑网络对齐的GRAph ALigner算法（“GRAAL”）。GRAAL可以对齐任何一对网络，并暴露出比任何其他现有工具更大的、密集的、连续的拓扑和功能相似性区域。最后，GraphCruch 2实现了一种算法，仅基于拓扑相似性对网络中的节点进行聚类。使用GraphCrunch 2，我们证明真核生物和病毒的PPI网络可能属于不同的图模型家族，并表明基于拓扑的聚类可以揭示酵母和人类PPI网络中蛋白质之间重要的功能相似性。GraphCrunch 2是一个实现生物网络分析最新研究成果的软件工具。它将计算密集型任务并行化，以充分利用现代多核cpu的潜力。它是开源的，可以免费用于研究。它在Windows和Linux平台下运行。
Recent advancements in experimental biotechnology have produced large amounts of protein-protein interaction (PPI) data. The topology of PPI networks is believed to have a strong link to their function. Hence, the abundance of PPI data for many organisms stimulates the development of computational techniques for the modeling, comparison, alignment, and clustering of networks. In addition, finding representative models for PPI networks will improve our understanding of the cell just as a model of gravity has helped us understand planetary motion. To decide if a model is representative, we need quantitative comparisons of model networks to real ones. However, exact network comparison is computationally intractable and therefore several heuristics have been used instead. Some of these heuristics are easily computable "network properties," such as the degree distribution, or the clustering coefficient. An important special case of network comparison is the network alignment problem. Analogous to sequence alignment, this problem asks to find the "best" mapping between regions in two networks. It is expected that network alignment might have as strong an impact on our understanding of biology as sequence alignment has had. Topology-based clustering of nodes in PPI networks is another example of an important network analysis problem that can uncover relationships between interaction patterns and phenotype. We introduce the GraphCrunch 2 software tool, which addresses these problems. It is a significant extension of GraphCrunch which implements the most popular random network models and compares them with the data networks with respect to many network properties. Also, GraphCrunch 2 implements the GRAph ALigner algorithm ("GRAAL") for purely topological network alignment. GRAAL can align any pair of networks and exposes large, dense, contiguous regions of topological and functional similarities far larger than any other existing tool. Finally, GraphCruch 2 implements an algorithm for clustering nodes within a network based solely on their topological similarities. Using GraphCrunch 2, we demonstrate that eukaryotic and viral PPI networks may belong to different graph model families and show that topology-based clustering can reveal important functional similarities between proteins within yeast and human PPI networks. GraphCrunch 2 is a software tool that implements the latest research on biological network analysis. It parallelizes computationally intensive tasks to fully utilize the potential of modern multi-core CPUs. It is open-source and freely available for research use. It runs under the Windows and Linux platforms.
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影响因子：
--
作者：
Ho H;Milenković T;Memisević V;Aruri J;Przulj N;Ganesan AK
通讯作者：
Ganesan AK
影响因子：
2
作者：
Milenković T;Ng WL;Hayes W;Przulj N
通讯作者：
Przulj N
DOI：
10.1073/pnas.0305199101
发表时间：
2004-10-12
影响因子：
11.1
作者：
Berg, J;Lässig, M
通讯作者：
Lässig, M
影响因子：
64.8
作者：
Gavin, AC;Aloy, P;Superti-Furga, G
通讯作者：
Superti-Furga, G
影响因子：
6.7
作者：
Fossum E;Friedel CC;Rajagopala SV;Titz B;Baiker A;Schmidt T;Kraus T;Stellberger T;Rutenberg C;Suthram S;Bandyopadhyay S;Rose D;von Brunn A;Uhlmann M;Zeretzke C;Dong YA;Boulet H;Koegl M;Bailer SM;Koszinowski U;Ideker T;Uetz P;Zimmer R;Haas J
通讯作者：
Haas J