代谢组
代谢组学
可视化
计算生物学
鉴定(生物学)
注释
数据采集
质谱法
化学
生物系统
计算机科学
生物
数据挖掘
色谱法
生物信息学
植物
操作系统
作者
Roger Giné,Jordi Capellades,Josep M. Badia,Dennis Vughs,Michaela Schwaiger-Haber,Theodore Alexandrov,María Vinaixa,Andrea M. Brunner,Gary J. Patti,Óscar Yanes
出处
期刊:Nature Methods
[Springer Nature]
日期:2021-11-01
卷期号:18 (11): 1370-1376
被引量:20
标识
DOI:10.1038/s41592-021-01307-z
摘要
Comprehensive metabolome analyses are essential for biomedical, environmental, and biotechnological research. However, current MS1- and MS2-based acquisition and data analysis strategies in untargeted metabolomics result in low identification rates of metabolites. Here we present HERMES, a molecular-formula-oriented and peak-detection-free method that uses raw LC/MS1 information to optimize MS2 acquisition. Investigating environmental water, Escherichia coli, and human plasma extracts with HERMES, we achieved an increased biological specificity of MS2 scans, leading to improved mass spectral similarity scoring and identification rates when compared with a state-of-the-art data-dependent acquisition (DDA) approach. Thus, HERMES improves sensitivity, selectivity, and annotation of metabolites. HERMES is available as an R package with a user-friendly graphical interface for data analysis and visualization.
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