Metabolite identification is a crucial step in mass spectrometry (MS)-based metabolomics. However, it is still challenging to assess the confidence of assigned metabolites. We report a novel method for estimating the false discovery rate (FDR) of metabolite assignment with a target-decoy strategy, in which the decoys are generated through violating the octet rule of chemistry by adding small odd numbers of hydrogen atoms. The target-decoy strategy was integrated into JUMPm, an automated metabolite identification pipeline for large-scale MS analysis and was also evaluated with two other metabolomics tools, mzMatch and MZmine 2. The reliability of FDR calculation was examined by false data sets, which were simulated by altering MS1 or MS2 spectra. Finally, we used the JUMPm pipeline coupled to the target-decoy strategy to process unlabeled and stable-isotope-labeled metabolomic data sets. The results demonstrate that the target-decoy strategy is a simple and effective method for evaluating the confidence of high-throughput metabolite identification.

中文翻译：

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基于目标诱饵的错误发现率估计用于大规模代谢物鉴定

代谢物鉴定是基于质谱（MS）的代谢组学中的关键步骤。但是，评估分配的代谢物的置信度仍然具有挑战性。我们报告了一种新的方法，用于估计具有目标诱饵策略的代谢物分配的错误发现率（FDR），其中诱饵是通过添加少量奇数个氢原子而违反化学八位位组规则而产生的。目标诱饵策略已集成到JUMPm（用于大规模MS分析的自动代谢物识别管道）中，并已使用其他两种代谢组学工具mzMatch和MZmine 2进行了评估。FDR计算的可靠性通过错误的数据集进行了检查，这些数据集通过更改MS1或MS2光谱进行模拟。最后，我们将JUMPm管线与目标诱饵策略结合使用来处理未标记和稳定同位素标记的代谢组学数据集。结果表明，目标诱饵策略是一种简单有效的评估高通量代谢物鉴定可信度的方法。