Unknown features in untargeted metabolomics and non-targeted analysis (NTA) are identified using fragment ions from MS/MS spectra to predict the structures of the unknown compounds. The precursor ion selected for fragmentation is commonly performed using data dependent acquisition (DDA) strategies or following statistical analysis using targeted MS/MS approaches. However, the selected precursor ions from DDA only cover a biased subset of the peaks or features found in full scan data. In addition, different statistical analysis can select different precursor ions for MS/MS analysis, which make the post-hoc validation of ions selected following a secondary analysis impossible for precursor ions selected by the original statistical method. Here we propose an automated, exhaustive, statistical model-free workflow: paired mass distance-dependent analysis (PMDDA), for reproducible untargeted mass spectrometry MS2 fragment ion collection of unknown compounds found in MS1 full scan. Our workflow first removes redundant peaks from MS1 data and then exports a list of precursor ions for pseudo-targeted MS/MS analysis on independent peaks. This workflow provides comprehensive coverage of MS2 collection on unknown compounds found in full scan analysis using a “one peak for one compound” workflow without a priori redundant peak information. We compared pseudo-spectra formation and the number of MS2 spectra linked to MS1 data using the PMDDA workflow to that obtained using CAMERA and RAMclustR algorithms. More annotated compounds, molecular networks, and unique MS/MS spectra were found using PMDDA compared with CAMERA and RAMClustR. In addition, PMDDA can generate a preferred ion list for iterative DDA to enhance coverage of compounds when instruments support such functions. Finally, compounds with signals in both positive and negative modes can be identified by the PMDDA workflow, to further reduce redundancies. The whole workflow is fully reproducible as a docker image xcmsrocker with both the original data and the data processing template.

中文翻译：

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可重现的非靶向代谢组学工作流程，用于对 MS1 特征进行详尽的 MS2 数据采集

使用 MS/MS 光谱中的碎片离子识别非靶向代谢组学和非靶向分析 (NTA) 中的未知特征，以预测未知化合物的结构。选择用于碎裂的母离子通常使用数据相关采集 (DDA) 策略或使用靶向 MS/MS 方法进行统计分析。然而，从 DDA 中选择的前体离子仅涵盖在全扫描数据中发现的峰或特征的偏差子集。此外，不同的统计分析可以选择不同的母离子进行MS/MS分析，这使得通过原始统计方法选择的母离子无法对二次分析后选择的离子进行事后验证。在这里，我们提出了一个自动化的、详尽的、无统计模型的工作流程：配对质量距离相关分析 (PMDDA)，用于可重现的非靶向质谱 MS2 碎片离子收集在 MS1 全扫描中发现的未知化合物。我们的工作流程首先从 MS1 数据中删除冗余峰，然后导出母离子列表，以对独立峰进行伪靶向 MS/MS 分析。此工作流程使用“一个化合物一个峰”工作流程提供了对在全扫描分析中发现的未知化合物的 MS2 收集的全面覆盖，而无需先验冗余峰信息。我们将使用 PMDDA 工作流程与使用 CAMERA 和 RAMclustR 算法获得的伪光谱形成和与 MS1 数据相关联的 MS2 光谱的数量进行了比较。与 CAMERA 和 RAMClustR 相比，使用 PMDDA 发现了更多注释化合物、分子网络和独特的 MS/MS 光谱。此外，PMDDA 可以为迭代 DDA 生成首选离子列表，以在仪器支持此类功能时增强化合物的覆盖范围。最后，可以通过 PMDDA 工作流程识别具有正模式和负模式信号的化合物，以进一步减少冗余。整个工作流程完全可重现为 docker 图像 xcmsrocker，同时包含原始数据和数据处理模板。