Zhang Bofei, Hu Senyang, Baskin Elizabeth, Patt Andrew, Siddiqui Jalal K, Mathé Ewy A
Department of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, OH 43210, USA.
Biomedical Engineering Graduate Program, The Ohio State University, Columbus, OH 43210, USA.
Metabolites. 2018 Feb 22;8(1):16. doi: 10.3390/metabo8010016.
The value of metabolomics in translational research is undeniable, and metabolomics data are increasingly generated in large cohorts. The functional interpretation of disease-associated metabolites though is difficult, and the biological mechanisms that underlie cell type or disease-specific metabolomics profiles are oftentimes unknown. To help fully exploit metabolomics data and to aid in its interpretation, analysis of metabolomics data with other complementary omics data, including transcriptomics, is helpful. To facilitate such analyses at a pathway level, we have developed RaMP (Relational database of Metabolomics Pathways), which combines biological pathways from the Kyoto Encyclopedia of Genes and Genomes (KEGG), Reactome, WikiPathways, and the Human Metabolome DataBase (HMDB). To the best of our knowledge, an off-the-shelf, public database that maps genes and metabolites to biochemical/disease pathways and can readily be integrated into other existing software is currently lacking. For consistent and comprehensive analysis, RaMP enables batch and complex queries (e.g., list all metabolites involved in glycolysis and lung cancer), can readily be integrated into pathway analysis tools, and supports pathway overrepresentation analysis given a list of genes and/or metabolites of interest. For usability, we have developed a RaMP R package (https://github.com/Mathelab/RaMP-DB), including a user-friendly RShiny web application, that supports basic simple and batch queries, pathway overrepresentation analysis given a list of genes or metabolites of interest, and network visualization of gene-metabolite relationships. The package also includes the raw database file (mysql dump), thereby providing a stand-alone downloadable framework for public use and integration with other tools. In addition, the Python code needed to recreate the database on another system is also publicly available (https://github.com/Mathelab/RaMP-BackEnd). Updates for databases in RaMP will be checked multiple times a year and RaMP will be updated accordingly.
代谢组学在转化研究中的价值不可否认，并且在大型队列中越来越多地产生代谢组学数据。然而，对疾病相关代谢物的功能解释却很困难，而且细胞类型或疾病特异性代谢组学图谱背后的生物学机制往往并不清楚。为了帮助充分利用代谢组学数据并辅助其解释，将代谢组学数据与其他互补的组学数据（包括转录组学）进行分析是有帮助的。为了在通路水平上促进此类分析，我们开发了RaMP（代谢组学通路关系数据库），它整合了来自京都基因与基因组百科全书（KEGG）、Reactome、WikiPathways和人类代谢组数据库（HMDB）的生物通路。据我们所知，目前缺乏一个现成的公共数据库，该数据库能将基因和代谢物映射到生化/疾病通路，并且可以很容易地集成到其他现有软件中。为了进行一致且全面的分析，RaMP支持批量和复杂查询（例如，列出参与糖酵解和肺癌的所有代谢物），可以很容易地集成到通路分析工具中，并支持基于感兴趣的基因和/或代谢物列表进行通路富集分析。为了便于使用，我们开发了一个RaMP R包（https://github.com/Mathelab/RaMP-DB），包括一个用户友好的RShiny网络应用程序，它支持基本的简单和批量查询、基于感兴趣的基因或代谢物列表进行通路富集分析，以及基因 - 代谢物关系的网络可视化。该包还包括原始数据库文件（mysql转储），从而提供一个可供公众使用并与其他工具集成的独立可下载框架。此外，在另一个系统上重新创建数据库所需的Python代码也已公开提供（https://github.com/Mathelab/RaMP-BackEnd）。每年将多次检查RaMP数据库的更新情况，并相应地更新RaMP。
