Harwood Thomas V, Wang Mingxun, Northen Trent R, Bowen Benjamin P
Joint Genome Institute, Lawrence Berkeley National Laboratory, One Cyclotron Road, Berkeley, California 94720, United States.
Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory, One Cyclotron Road, Berkeley, California 94720, United States.
Anal Chem. 2025 Sep 9;97(35):18860-18866. doi: 10.1021/acs.analchem.5c02591. Epub 2025 Aug 25.
A significant bottleneck in metabolomics data interpretation is the effective use of domain knowledge to assign structural information based on fragmentation patterns. The mass spectrometry query language (MassQL) aims to make this process accessible and applicable across multiple analysis platforms. While advanced computational methods are capable of predicting compound structures from fragmentation data, AI/ML approaches often rely on complex, opaque criteria that are difficult to interpret or modify. As a result, their predictive patterns cannot be readily translated into human-readable rules, such as those used in MassQL. In this study, we introduce ChemEcho, a machine learning embedding method that converts tandem mass spectrometry data into sparse feature vectors containing peak and neutral mass subformulae to enhance explainable AI/ML-based methods. An advantage of this approach is that decision trees trained using these feature vectors can be directly translated to MassQL. Using a battery of decision trees trained using ChemEcho embeddings to predict molecular attributes, we generated over 1500 MassQL queries for 765 molecular features and evaluated their precision and recall. From these queries, the 50 highest-performing queries were integrated into the MassQL compendium. This set of generated MassQL queries included environmentally and biologically relevant classes such as PFAS and molecules containing phosphate or sulfate substructures. To illustrate the impact these queries would have on a typical metabolomics experiment, these MassQL queries were applied to a public metabolomics data set─resulting in a marked increase in the structural information derived from tandem mass spectra. Access and reuse of these queries is expected to enhance structural annotation in untargeted experiments, leading to more specific claims and advancing many applications in metabolomics.
代谢组学数据解读中的一个重大瓶颈是有效利用领域知识，根据碎片模式来分配结构信息。质谱查询语言（MassQL）旨在使这一过程在多个分析平台上易于实现和应用。虽然先进的计算方法能够从碎片数据预测化合物结构，但人工智能/机器学习方法通常依赖于复杂、不透明的标准，难以解释或修改。因此，它们的预测模式无法轻易转化为人类可读的规则，比如MassQL中使用的规则。在本研究中，我们引入了ChemEcho，一种机器学习嵌入方法，它将串联质谱数据转换为包含峰和中性质量子公式的稀疏特征向量，以增强基于人工智能/机器学习的可解释方法。这种方法的一个优点是，使用这些特征向量训练的决策树可以直接转化为MassQL。我们使用一系列基于ChemEcho嵌入训练的决策树来预测分子属性，针对765个分子特征生成了1500多个MassQL查询，并评估了它们的精确率和召回率。从这些查询中，50个性能最佳的查询被整合到MassQL汇编中。这组生成的MassQL查询包括与环境和生物相关的类别，如全氟和多氟烷基物质以及含有磷酸盐或硫酸盐子结构的分子。为了说明这些查询对典型代谢组学实验的影响，我们将这些MassQL查询应用于一个公共代谢组学数据集，结果串联质谱得出的结构信息显著增加。预计这些查询的获取和重用将增强非靶向实验中的结构注释，从而得出更具体的结论，并推动代谢组学中的许多应用。
