Chair for Bioinformatics, Friedrich Schiller University Jena, Jena, 07743, Germany.
RNA Bioinformatics and High Throughput Analysis, Friedrich Schiller University Jena, Jena, 07743, Germany.
Nat Commun. 2017 Nov 14;8(1):1494. doi: 10.1038/s41467-017-01318-5.
The annotation of small molecules in untargeted mass spectrometry relies on the matching of fragment spectra to reference library spectra. While various spectrum-spectrum match scores exist, the field lacks statistical methods for estimating the false discovery rates (FDR) of these annotations. We present empirical Bayes and target-decoy based methods to estimate the false discovery rate (FDR) for 70 public metabolomics data sets. We show that the spectral matching settings need to be adjusted for each project. By adjusting the scoring parameters and thresholds, the number of annotations rose, on average, by +139% (ranging from -92 up to +5705%) when compared with a default parameter set available at GNPS. The FDR estimation methods presented will enable a user to assess the scoring criteria for large scale analysis of mass spectrometry based metabolomics data that has been essential in the advancement of proteomics, transcriptomics, and genomics science.
在非靶向质谱中，小分子的注释依赖于碎片谱与参考文库谱的匹配。虽然存在各种谱-谱匹配评分，但该领域缺乏估计这些注释的假发现率 (FDR) 的统计方法。我们提出了经验贝叶斯和基于靶标诱饵的方法来估计 70 个公共代谢组学数据集的假发现率 (FDR)。我们表明，需要针对每个项目调整光谱匹配设置。通过调整评分参数和阈值，与 GNPS 提供的默认参数集相比，注释数量平均增加了 +139%（范围从-92 到+5705%）。所提出的 FDR 估计方法将使用户能够评估基于质谱的代谢组学数据的大规模分析的评分标准，这对于蛋白质组学、转录组学和基因组学科学的发展至关重要。
