Sumner Lloyd W, Urbanczyk-Wochniak Ewa, Broeckling Corey D
The Samuel Roberts Nobel Foundation, Plant Biology Division, Ardmore, OK, USA.
Methods Mol Biol. 2007;406:409-36. doi: 10.1007/978-1-59745-535-0_20.
Metabolomics is the large-scale analysis of metabolites and as such requires bioinformatics tools for data analysis, visualization, and integration. This chapter describes the basic composition of chromatographically coupled mass spectrometry (MS) data sets used in metabolomics and describes in detail the steps necessary for extracting large-scale qualitative and quantitative information. This process involves noise filtering, peak picking and deconvolution, peak identification, peak alignment, and the creation of a final data matrix for statistical processing. Multivariate tools for comparative analysis are presented and illustrated using data for Medicago truncatula. Additional tools for visualizing and integrating metabolomics data within a biological context are discussed. Two tables are provided listing current metabolomics data processing and visualization software. Because metabolomics is rapidly maturing, a final section is presented concerning the need for data standardization and current efforts.
代谢组学是对代谢物进行大规模分析，因此需要生物信息学工具来进行数据分析、可视化和整合。本章描述了代谢组学中使用的色谱联用质谱（MS）数据集的基本组成，并详细描述了提取大规模定性和定量信息所需的步骤。这个过程包括噪声过滤、峰检测与去卷积、峰识别、峰对齐以及创建用于统计处理的最终数据矩阵。介绍了用于比较分析的多变量工具，并使用蒺藜苜蓿的数据进行了说明。还讨论了在生物学背景下可视化和整合代谢组学数据的其他工具。提供了两个表格，列出了当前的代谢组学数据处理和可视化软件。由于代谢组学正在迅速成熟，最后一节介绍了数据标准化的必要性和当前的努力。
