MetEx: A Targeted Extraction Strategy for Improving the Coverage and Accuracy of Metabolite Annotation in Liquid Chromatography-High-Resolution Mass Spectrometry Data.
Fujian ZhengLei YouWangshu QinRunze OuyangWangjie LvLei GuoXin LuEnyou LiXinjie ZhaoGuo-Wang XuPublished in: Analytical chemistry (2022)
Liquid chromatography-high-resolution mass spectrometry (LC-HRMS) is the most popular platform for untargeted metabolomics studies, but compound annotation is a challenge. In this work, we developed a new LC-HRMS data-targeted extraction method called MetEx for metabolite annotation. MetEx contains the retention time ( t R ), MS1, and MS2 information of 30 620 metabolites from freely available spectral databases, including MoNA and KEGG. The t R values of 95.4% of the compounds in our database were calculated by the GNN-RT model. The MS2 spectra of 39.4% compounds were also predicted using CFM-ID. MetEx was initially examined on a mixture of 634 standards, considering chemical coverage and accurate metabolite assignment, and later applied to human plasma (NIST SRM 1950), human urine, HepG2 cells, mouse liver tissue, and mouse feces. MetEx correctly assigned 252 out of 253 standards detected in our instruments. The platform also provided 8.0-44.2% more compounds in the biological samples compared to XCMS, MS-DIAL, and MZmine 2. MetEx is implemented and visualized in R and freely available at http://www.metaboex.cn/MetEx.
Keyphrases
- high resolution mass spectrometry
- liquid chromatography
- mass spectrometry
- ultra high performance liquid chromatography
- tandem mass spectrometry
- gas chromatography
- simultaneous determination
- high resolution
- ms ms
- big data
- endothelial cells
- rna seq
- multiple sclerosis
- high throughput
- emergency department
- optical coherence tomography
- lymph node metastasis
- magnetic resonance
- affordable care act
- density functional theory
- adverse drug
- machine learning