An automated method for finding molecular complexes in large protein interaction networks
An automated method for finding molecular complexes in large protein interaction networks
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DOI：
10.1186/1471-2105-4-2
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发表时间：
2003-01-13
影响因子：
3
通讯作者：
Hogue, CW
中科院分区：
文献类型：
--
作者：
Bader, GD;Hogue, CW
背景资料：蛋白质组学技术的最新进展，如双杂交，噬菌体展示和质谱，使我们能够创建一个详细的生物分子相互作用网络的地图。初步的测绘工作已经产生了大量数据。随着相互作用集的大小的增加，数据库和计算方法将需要存储，可视化和分析的信息，以有效地帮助在knowledge discovery.Results：本文介绍了一种新的图论聚类算法，“分子复合物检测”（MCODE），检测密集连接的区域在大型蛋白质-蛋白质相互作用网络，可能代表分子复合物。该方法基于局部邻域密度的顶点加权和从局部稠密的种子蛋白向外遍历，根据给定的参数分离出稠密区域。该算法的优势在于具有有向模式，允许微调感兴趣的簇，而不考虑网络的其余部分，并允许检查簇的互连性，这与蛋白质网络相关。从酵母Saccharomyces cerevisiae的蛋白质相互作用和复杂的信息被用于evaluation.Conclusion：蛋白质相互作用网络的密集区域可以被发现，仅基于连接数据，其中许多对应于已知的蛋白质复合物。该算法不受已知的高误报率的高通量交互技术的数据。该程序可从ftp://ftp.mshri.on.ca/pub/BIND/Tools/MCODE获得。
Background: Recent advances in proteomics technologies such as two-hybrid, phage display and mass spectrometry have enabled us to create a detailed map of biomolecular interaction networks. Initial mapping efforts have already produced a wealth of data. As the size of the interaction set increases, databases and computational methods will be required to store, visualize and analyze the information in order to effectively aid in knowledge discovery.Results: This paper describes a novel graph theoretic clustering algorithm, "Molecular Complex Detection" (MCODE), that detects densely connected regions in large protein-protein interaction networks that may represent molecular complexes. The method is based on vertex weighting by local neighborhood density and outward traversal from a locally dense seed protein to isolate the dense regions according to given parameters. The algorithm has the advantage over other graph clustering methods of having a directed mode that allows fine-tuning of clusters of interest without considering the rest of the network and allows examination of cluster interconnectivity, which is relevant for protein networks. Protein interaction and complex information from the yeast Saccharomyces cerevisiae was used for evaluation.Conclusion: Dense regions of protein interaction networks can be found, based solely on connectivity data, many of which correspond to known protein complexes. The algorithm is not affected by a known high rate of false positives in data from high-throughput interaction techniques. The program is available from ftp://ftp.mshri.on.ca/pub/BIND/Tools/MCODE.