MetaMapR：纳入未知物的非通路依赖性代谢组学网络分析
MetaMapR: pathway independent metabolomic network analysis incorporating unknowns.
作者信息
Grapov Dmitry, Wanichthanarak Kwanjeera, Fiehn Oliver
机构信息
National Institutes of Health West Coast Metabolomics Center, Genome Center, University of California Davis, Davis CA 95616, USA and.
National Institutes of Health West Coast Metabolomics Center, Genome Center, University of California Davis, Davis CA 95616, USA and King Abdulaziz University, Biochemistry Department, Jeddah, Saudi Arabia.
出版信息
Bioinformatics. 2015 Aug 15;31(16):2757-60. doi: 10.1093/bioinformatics/btv194. Epub 2015 Apr 5.
UNLABELLED
Metabolic network mapping is a widely used approach for integration of metabolomic experimental results with biological domain knowledge. However, current approaches can be limited by biochemical domain or pathway knowledge which results in sparse disconnected graphs for real world metabolomic experiments. MetaMapR integrates enzymatic transformations with metabolite structural similarity, mass spectral similarity and empirical associations to generate richly connected metabolic networks. This open source, web-based or desktop software, written in the R programming language, leverages KEGG and PubChem databases to derive associations between metabolites even in cases where biochemical domain or molecular annotations are unknown. Network calculation is enhanced through an interface to the Chemical Translation System, which allows metabolite identifier translation between >200 common biochemical databases. Analysis results are presented as interactive visualizations or can be exported as high-quality graphics and numerical tables which can be imported into common network analysis and visualization tools.
AVAILABILITY AND IMPLEMENTATION
Freely available at http://dgrapov.github.io/MetaMapR/. Requires R and a modern web browser. Installation instructions, tutorials and application examples are available at http://dgrapov.github.io/MetaMapR/.
CONTACT
未标注
代谢网络映射是一种广泛应用的方法，用于将代谢组学实验结果与生物领域知识相结合。然而，当前的方法可能受到生化领域或途径知识的限制，这导致在实际的代谢组学实验中生成稀疏且不相连的图。MetaMapR将酶促转化与代谢物结构相似性、质谱相似性和经验关联相结合，以生成连接丰富的代谢网络。这个用R编程语言编写的开源软件，既可以基于网络使用，也可以作为桌面软件使用，它利用KEGG和PubChem数据库来推导代谢物之间的关联，即使在生化领域或分子注释未知的情况下也能做到。通过与化学翻译系统的接口增强了网络计算功能，该接口允许在200多个常见生化数据库之间进行代谢物标识符的转换。分析结果以交互式可视化的形式呈现，或者可以导出为高质量的图形和数值表，这些可以导入到常见的网络分析和可视化工具中。
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