Untargeted mass spectrometry is employed to detect small molecules in complex biospecimens, generating data that are difficult to interpret. We developed Qemistree, a data exploration strategy based on the hierarchical organization of molecular fingerprints predicted from fragmentation spectra. Qemistree allows mass spectrometry data to be represented in the context of sample metadata and chemical ontologies. By expressing molecular relationships as a tree, we can apply ecological tools that are designed to analyze and visualize the relatedness of DNA sequences to metabolomics data. Here we demonstrate the use of tree-guided data exploration tools to compare metabolomics samples across different experimental conditions such as chromatographic shifts. Additionally, we leverage a tree representation to visualize chemical diversity in a heterogeneous collection of samples. The Qemistree software pipeline is freely available to the microbiome and metabolomics communities in the form of a QIIME2 plugin, and a global natural products social molecular networking workflow.

非靶向质谱法用于检测复杂生物样本中的小分子，生成难以解释的数据。我们开发了 Qemistree，这是一种基于从碎片光谱预测的分子指纹的分层组织的数据探索策略。Qemistree 允许在样本元数据和化学本体的上下文中表示质谱数据。通过将分子关系表示为树，我们可以应用旨在分析和可视化 DNA 序列与代谢组学数据的相关性的生态工具。在这里，我们演示了使用树引导数据探索工具来比较不同实验条件（如色谱位移）的代谢组学样本。此外，我们利用树表示来可视化异构样本集合中的化学多样性。Qemistree 软件管道以 QIIME2 插件和全球天然产物社交分子网络工作流程的形式免费提供给微生物组和代谢组学社区。