Shahaf Nir, Aharoni Asaph, Rogachev Ilana
Department of Plant and Environmental Sciences, Faculty of Biochemistry, Weizmann Institute of Science, Rehovot, Israel.
Methods Mol Biol. 2018;1778:193-206. doi: 10.1007/978-1-4939-7819-9_14.
Databases containing mass spectrometry (MS) spectral data (i.e., MS reference libraries) are currently the most reliable and widely accepted approach to annotate unknown features in MS-based metabolomics. While for gas chromatography (GC)-MS data, a strategy for collecting, storing, and comparing to raw data has been established, this is not the case for liquid chromatography (LC)-MS data. Here, we present our approach for high-throughput data collection and automated MS reference library generation, as applied recently in the WEIZMASS library of plant metabolites. Methodologies to experimentally generate pools of chemical standards and computationally convert them into a unique source of reference data are detailed.
包含质谱（MS）光谱数据的数据库（即MS参考库）是目前基于MS的代谢组学中注释未知特征最可靠且被广泛接受的方法。虽然对于气相色谱（GC）-MS数据，已经建立了一种收集、存储和与原始数据进行比较的策略，但液相色谱（LC）-MS数据并非如此。在此，我们介绍我们的高通量数据收集和自动生成MS参考库的方法，该方法最近已应用于植物代谢物的WEIZMASS库中。详细阐述了通过实验生成化学标准品池并通过计算将其转化为独特参考数据源的方法。
