Multiplex methods provide effective integration of multi-omic data in genome-scale models.
Multiplex methods provide effective integration of multi-omic data in genome-scale models.
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DOI：
10.1186/s12859-016-0912-1
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发表时间：
2016-03-02
影响因子：
3
通讯作者：
Lió P
中科院分区：
文献类型：
--
作者：
Angione C;Conway M;Lió P
基因组、转录组和代谢变异塑造了细菌对不同环境条件的复杂适应景观。阐明基因型-表型关系为预测这种影响铺平了道路，但仍然缺乏表征多个环境因素之间关系的方法。在这里，我们解决了从环境条件的集合中提取网络级信息的问题，通过整合多个组学水平来测量细菌的反应。为此，我们建立了一个大的纲要的生长条件作为一个多重网络组成的转录和fluxomic层，我们提出了一个多组学网络的方法来推断相似的生长条件，通过整合层的多重网络。网络的每个节点代表一个单一的条件，而边缘是条件之间的相似性，如通过网络不同层上的表型和转录组学特性所测量的。然后，我们将这些层融合到一个网络中，从而捕获两个组学水平上的条件和相关相似性的全球网络。我们将这种多组学融合应用于更新的大肠杆菌基因组规模重建，其中包括地下代谢和新的基因-蛋白质-反应关联。我们的方法可以很容易地用于评估和交叉比较不同物种之间的条件不同的集合。获取多组学信息的实验条件的空间的拓扑结构，使得有可能推断的位置，并建立未测试的或不完整的配置文件的实验数据是不可用的条件特定的模型。我们用于基因组规模模型的加权网络融合方法可在https://github.com/maxconway/SNFtool上免费获得。本文的在线版本（doi：10.1186/s12859-016-0912-1）包含补充材料，可供授权用户使用。
Genomic, transcriptomic, and metabolic variations shape the complex adaptation landscape of bacteria to varying environmental conditions. Elucidating the genotype-phenotype relation paves the way for the prediction of such effects, but methods for characterizing the relationship between multiple environmental factors are still lacking. Here, we tackle the problem of extracting network-level information from collections of environmental conditions, by integrating the multiple omic levels at which the bacterial response is measured. To this end, we model a large compendium of growth conditions as a multiplex network consisting of transcriptomic and fluxomic layers, and we propose a multi-omic network approach to infer similarity of growth conditions by integrating layers of the multiplex network. Each node of the network represents a single condition, while edges are similarities between conditions, as measured by phenotypic and transcriptomic properties on different layers of the network. We then fuse these layers into one network, therefore capturing a global network of conditions and the associated similarities across two omic levels. We apply this multi-omic fusion to an updated genome-scale reconstruction of Escherichia coli that includes underground metabolism and new gene-protein-reaction associations. Our method can be readily used to evaluate and cross-compare different collections of conditions among different species. Acquiring multi-omic information on the topology of the space of experimental conditions makes it possible to infer the position and to build condition-specific models of untested or incomplete profiles for which experimental data is not available. Our weighted network fusion method for genome-scale models is freely available at https://github.com/maxconway/SNFtool. The online version of this article (doi:10.1186/s12859-016-0912-1) contains supplementary material, which is available to authorized users.