代谢组学
计算机科学
工作流程
元数据
背景(考古学)
计算生物学
管道(软件)
数据挖掘
杠杆(统计)
数据科学
生物信息学
生物
数据库
人工智能
万维网
古生物学
程序设计语言
作者
Anupriya Tripathi,Yoshiki Vázquez-Baeza,Julia M. Gauglitz,Mingxun Wang,Kai Dührkop,Mélissa Nothias-Esposito,Deepa Acharya,Madeleine Ernst,Justin J. J. van der Hooft,Qiyun Zhu,Daniel McDonald,Asker Brejnrod,Antonio González,Jo Handelsman,Markus Fleischauer,Marcus Ludwig,Sebastian Böcker,Louis Félix Nothias,Rob Knight,Pieter C. Dorrestein
标识
DOI:10.1038/s41589-020-00677-3
摘要
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.
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