Luo Feng, Yang Yunfeng, Chen Chin-Fu, Chang Roger, Zhou Jizhong, Scheuermann Richard H
Department of Computer Science, 100 McAdams Hall, Clemson University, Clemson, SC 29634-0974, USA.
Bioinformatics. 2007 Jan 15;23(2):207-14. doi: 10.1093/bioinformatics/btl562. Epub 2006 Nov 8.
Accumulating evidence suggests that biological systems are composed of interacting, separable, functional modules. Identifying these modules is essential to understand the organization of biological systems.
In this paper, we present a framework to identify modules within biological networks. In this approach, the concept of degree is extended from the single vertex to the sub-graph, and a formal definition of module in a network is used. A new agglomerative algorithm was developed to identify modules from the network by combining the new module definition with the relative edge order generated by the Girvan-Newman (G-N) algorithm. A JAVA program, MoNet, was developed to implement the algorithm. Applying MoNet to the yeast core protein interaction network from the database of interacting proteins (DIP) identified 86 simple modules with sizes larger than three proteins. The modules obtained are significantly enriched in proteins with related biological process Gene Ontology terms. A comparison between the MoNet modules and modules defined by Radicchi et al. (2004) indicates that MoNet modules show stronger co-clustering of related genes and are more robust to ties in betweenness values. Further, the MoNet output retains the adjacent relationships between modules and allows the construction of an interaction web of modules providing insight regarding the relationships between different functional modules. Thus, MoNet provides an objective approach to understand the organization and interactions of biological processes in cellular systems.
MoNet is available upon request from the authors.
越来越多的证据表明，生物系统是由相互作用、可分离的功能模块组成。识别这些模块对于理解生物系统的组织至关重要。
在本文中，我们提出了一个在生物网络中识别模块的框架。在这种方法中，度的概念从单个顶点扩展到子图，并使用了网络中模块的形式化定义。通过将新的模块定义与由Girvan-Newman（G-N）算法生成的相对边序相结合，开发了一种新的凝聚算法来从网络中识别模块。开发了一个JAVA程序MoNet来实现该算法。将MoNet应用于来自相互作用蛋白质数据库（DIP）的酵母核心蛋白质相互作用网络，识别出86个大小大于三个蛋白质的简单模块。所获得的模块在具有相关生物过程基因本体术语的蛋白质中显著富集。MoNet模块与Radicchi等人（2004年）定义的模块之间的比较表明，MoNet模块显示出更强的相关基因共聚类，并且对介数中心性值中的联系更具鲁棒性。此外，MoNet输出保留了模块之间的相邻关系，并允许构建模块相互作用网络，从而深入了解不同功能模块之间的关系。因此，MoNet提供了一种客观的方法来理解细胞系统中生物过程的组织和相互作用。
可向作者索取MoNet。
