NIH West Coast Metabolomics Center, University of California Davis, Davis, CA 95616, United States.
NIH West Coast Metabolomics Center, University of California Davis, Davis, CA 95616, United States; Biochemistry Department, Faculty of Science, King Abdulaziz University, P.O. Box 80203, Jeddah 21589, Saudi Arabia.
Curr Opin Biotechnol. 2018 Dec;54:1-9. doi: 10.1016/j.copbio.2018.01.010. Epub 2018 Feb 6.
Access to high quality metabolomics data has become a routine component for biological studies. However, interpreting those datasets in biological contexts remains a challenge, especially because many identified metabolites are not found in biochemical pathway databases. Starting from statistical analyses, a range of new tools are available, including metabolite set enrichment analysis, pathway and network visualization, pathway prediction, biochemical databases and text mining. Integrating these approaches into comprehensive and unbiased interpretations must carefully consider both caveats of the metabolomics dataset itself as well as the structure and properties of the biological study design. Special considerations need to be taken when adopting approaches from genomics for use in metabolomics. R and Python programming language are enabling an easier exchange of diverse tools to deploy integrated workflows. This review summarizes the key ideas and latest developments in regards to these approaches.
获取高质量的代谢组学数据已经成为生物学研究的常规组成部分。然而，在生物学背景下解释这些数据集仍然是一个挑战，特别是因为许多鉴定出的代谢物在生化途径数据库中找不到。从统计分析开始，一系列新的工具已经可用，包括代谢物集富集分析、途径和网络可视化、途径预测、生化数据库和文本挖掘。将这些方法整合到全面和无偏的解释中，必须仔细考虑代谢组学数据集本身的注意事项以及生物学研究设计的结构和特性。在将基因组学方法应用于代谢组学时，需要特别考虑。R 和 Python 编程语言使不同工具的更轻松交换成为可能，从而部署集成工作流程。本文综述了这些方法的关键思想和最新进展。
