Stowers Institute for Medical Research, Kansas City, MO, USA.
École Polytechnique, Route de Saclay, Palaiseau, France.
BMC Bioinformatics. 2019 Aug 22;20(1):435. doi: 10.1186/s12859-019-3024-x.
Gene and protein interaction data are often represented as interaction networks, where nodes stand for genes or gene products and each edge stands for a relationship between a pair of gene nodes. Commonly, that relationship within a pair is specified by high similarity between profiles (vectors) of experimentally defined interactions of each of the two genes with all other genes in the genome; only gene pairs that interact with similar sets of genes are linked by an edge in the network. The tight groups of genes/gene products that work together in a cell can be discovered by the analysis of those complex networks.
We show that the choice of the similarity measure between pairs of gene vectors impacts the properties of networks and of gene modules detected within them. We re-analyzed well-studied data on yeast genetic interactions, constructed four genetic networks using four different similarity measures, and detected gene modules in each network using the same algorithm. The four networks induced different numbers of putative functional gene modules, and each similarity measure induced some unique modules. In an example of a putative functional connection suggested by comparing genetic interaction vectors, we predict a link between SUN-domain proteins and protein glycosylation in the endoplasmic reticulum.
The discovery of molecular modules in genetic networks is sensitive to the way of measuring similarity between profiles of gene interactions in a cell. In the absence of a formal way to choose the "best" measure, it is advisable to explore the measures with different mathematical properties, which may identify different sets of connections between genes.
基因和蛋白质相互作用数据通常表示为相互作用网络，其中节点代表基因或基因产物，每条边代表一对基因节点之间的关系。通常，一对基因之间的关系是通过对每个基因与基因组中所有其他基因的实验定义相互作用的谱（向量）之间的高相似度来指定的；只有与相似基因集相互作用的基因对才通过网络中的边连接。通过分析这些复杂的网络，可以发现细胞中共同作用的紧密基因/基因产物组。
我们表明，对基因向量对之间相似度的选择会影响网络的性质和其中检测到的基因模块。我们重新分析了酵母遗传相互作用的已有研究数据，使用四种不同的相似度度量标准构建了四个遗传网络，并使用相同的算法在每个网络中检测基因模块。这四个网络诱导了不同数量的假定功能基因模块，并且每个相似度度量都诱导了一些独特的模块。在比较遗传相互作用向量时，我们预测了 SUN 结构域蛋白和内质网中蛋白质糖基化之间的潜在功能连接。
在遗传网络中发现分子模块对细胞中基因相互作用谱的相似度测量方法很敏感。在没有正式选择“最佳”度量标准的方法的情况下，建议探索具有不同数学性质的度量标准，这可能会识别出基因之间的不同连接集。
