Broeckling Corey D, Reddy Indira R, Duran Anthony L, Zhao Xuechun, Sumner Lloyd W
Plant Biology Division, Samuel Roberts Noble Foundation, P.O. Box 2180, Ardmore, Oklahoma 73401, USA.
Anal Chem. 2006 Jul 1;78(13):4334-41. doi: 10.1021/ac0521596.
A current and significant limitation to metabolomics is the large-scale, high-throughput conversion of raw chromatographically coupled mass spectrometry datasets into organized data matrices necessary for further statistical processing and data visualization. This article describes a new data extraction tool, MET-IDEA (Metabolomics Ion-based Data Extraction Algorithm) which surmounts this void. MET-IDEA is compatible with a diversity of chromatographically coupled mass spectrometry systems, generates an output similar to traditional quantification methods, utilizes the sensitivity and selectivity associated with selected ion quantification, and greatly reduces the time and effort necessary to obtain large-scale organized datasets by several orders of magnitude. The functionality of MET-IDEA is illustrated using metabolomics data obtained for elicited cell culture exudates from the model legume, Medicago truncatula. The results indicate that MET-IDEA is capable of rapidly extracting semiquantitative data from raw data files, which allows for more rapid biological insight. MET-IDEA is freely available to academic users upon request.
代谢组学目前一个重要的局限性在于，如何将原始的色谱联用质谱数据集大规模、高通量地转换为有组织的数据矩阵，以便进行进一步的统计处理和数据可视化。本文介绍了一种新的数据提取工具MET-IDEA（基于代谢组学离子的数据提取算法），它克服了这一空白。MET-IDEA与多种色谱联用质谱系统兼容，生成与传统定量方法类似的输出结果，利用与选定离子定量相关的灵敏度和选择性，并将获取大规模有组织数据集所需的时间和精力大幅减少了几个数量级。使用从模式豆科植物蒺藜苜蓿诱导的细胞培养渗出物中获得的代谢组学数据说明了MET-IDEA的功能。结果表明，MET-IDEA能够从原始数据文件中快速提取半定量数据，从而实现更快速的生物学洞察。学术用户可按需免费获取MET-IDEA。
