When using liquid chromatography/mass spectrometry (LC/MS) to perform untargeted metabolomics, it is now routine to detect tens of thousands of features from biological samples. Poor understanding of the data, however, has complicated interpretation and masked the number of unique metabolites actually being measured in an experiment. Here we place an upper bound on the number of unique metabolites detected in Escherichia coli samples analyzed with one untargeted metabolomics method. We first group multiple features arising from the same analyte, which we call “degenerate features”, using a context-driven annotation approach. Surprisingly, this analysis revealed thousands of previously unreported degeneracies that reduced the number of unique analytes to ∼2961. We then applied an orthogonal approach to remove nonbiological features from the data using the ¹³C-based credentialing technology. This further reduced the number of unique analytes to less than 1000. Our 90% reduction in data is 5-fold greater than previously published studies. On the basis of the results, we propose an alternative approach to untargeted metabolomics that relies on thoroughly annotated reference data sets. To this end, we introduce the creDBle database (http://creDBle.wustl.edu), which contains accurate mass, retention time, and MS/MS fragmentation data as well as annotations of all credentialed features.

中文翻译：

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代谢组学数据集的系统级注释可将25 000个特征减少到少于1000种独特代谢物

现在，使用液相色谱/质谱（LC / MS）进行非目标代谢组学研究时，通常需要从生物样品中检测数以万计的特征。然而，对数据的理解不充分使解释变得复杂，并掩盖了实验中实际测量的独特代谢物的数量。在此，我们对在大肠杆菌中检测到的独特代谢物的数量设置了上限一种非靶向代谢组学方法分析的样本。我们首先使用上下文驱动的注释方法将源自同一分析物的多个特征归为一组，我们称之为“简并特征”。出乎意料的是，该分析揭示了成千上万个以前未报告的简并性，这些独特性将独特的分析物数量减少至〜2961。然后，我们使用^13种正交方法从数据中删除非生物学特征基于C的凭证技术。这进一步将独特分析物的数量减少到少于1000个。我们的数据减少90％，是以前发表的研究的5倍。根据结果​​，我们提出了一种针对目标代谢组学的替代方法，该方法依赖于完全注释的参考数据集。为此，我们介绍了creDBle数据库（http://creDBle.wustl.edu），其中包含准确的质量，保留时间和MS / MS碎片数据以及所有凭据功能的注释。