Chen Jingchun, Yuan Bo
Integrated Biomedical Science Graduate Program, Department of Biomedical Informatics and Department of Pharmacology, The Ohio State University 333 W. 10th Avenue, Columbus, OH 43210, USA.
Bioinformatics. 2006 Sep 15;22(18):2283-90. doi: 10.1093/bioinformatics/btl370. Epub 2006 Jul 12.
Identification of functional modules in protein interaction networks is a first step in understanding the organization and dynamics of cell functions. To ensure that the identified modules are biologically meaningful, network-partitioning algorithms should take into account not only topological features but also functional relationships, and identified modules should be rigorously validated.
In this study we first integrate proteomics and microarray datasets and represent the yeast protein-protein interaction network as a weighted graph. We then extend a betweenness-based partition algorithm, and use it to identify 266 functional modules in the yeast proteome network. For validation we show that the functional modules are indeed densely connected subgraphs. In addition, genes in the same functional module confer a similar phenotype. Furthermore, known protein complexes are largely contained in the functional modules in their entirety. We also analyze an example of a functional module and show that functional modules can be useful for gene annotation.
Supplementary data are available at Bioinformatics online.
识别蛋白质相互作用网络中的功能模块是理解细胞功能的组织和动态的第一步。为确保所识别的模块具有生物学意义，网络划分算法不仅应考虑拓扑特征，还应考虑功能关系，并且所识别的模块应经过严格验证。
在本研究中，我们首先整合蛋白质组学和微阵列数据集，并将酵母蛋白质 - 蛋白质相互作用网络表示为加权图。然后，我们扩展了一种基于介数的划分算法，并用它来识别酵母蛋白质组网络中的266个功能模块。为了进行验证，我们表明这些功能模块确实是紧密连接的子图。此外，同一功能模块中的基因赋予相似的表型。此外，已知的蛋白质复合物在很大程度上整体包含在功能模块中。我们还分析了一个功能模块的示例，并表明功能模块可用于基因注释。
补充数据可在《生物信息学》在线获取。
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