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Updated: Jan 31, 2026
05:35
An Integrated Workflow of Identification and Quantification on FDR Control-Based Untargeted Metabolome
Published on: September 20, 2022
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Peak Annotation and Verification Engine for Untargeted LC-MS Metabolomics.
Lin Wang1,2, Xi Xing1,2, Li Chen1,2
1Lewis Sigler Institute for Integrative Genomics , Princeton University , Princeton , New Jersey 08544 , United States.
Analytical Chemistry
|December 27, 2018
Summary
Untargeted metabolomics often yields thousands of peaks, but identifying true metabolites is challenging. The Peak Annotation and Verification Engine (PAVE) systematically annotates these peaks, revealing that most are contaminants or artifacts, with only a small fraction being actual metabolites.
Area of Science:
- Metabolomics
- Analytical Chemistry
- Bioinformatics
Background:
- Untargeted metabolomics via LC-MS generates numerous peaks, complicating metabolite identification.
- Accurate annotation of these peaks is crucial for understanding biological systems.
Purpose of the Study:
- To introduce a novel workflow, the Peak Annotation and Verification Engine (PAVE), for systematic annotation of untargeted microbial metabolomics data.
- To improve the accuracy of metabolite identification from complex LC-MS data.
Main Methods:
- Utilized 13C and 15N isotope-labeled media to determine carbon and nitrogen atom counts of biological compounds.
- Developed algorithms for improved deisotoping and deadducting integrating multiple data modes.
- Employed in-source collision-induced dissociation to differentiate metabolites from fragments.
- Assigned molecular formulas using m/z, C/N counts, and database searching.
Main Results:
- Over 80% of detected peaks in microbial samples were identified as environmental contaminants.
- More than 70% of biological peaks were non-metabolite entities (isotopic variants, adducts, fragments, artifacts).
- PAVE successfully annotated approximately 4% of total peaks as apparent metabolites, with 220 structures confirmed by MS/MS and/or retention time matching.
Conclusions:
- PAVE provides a robust method for systematic annotation of untargeted metabolomics data.
- The majority of peaks in untargeted LC-MS are not true metabolites, highlighting the need for rigorous verification.
- This approach significantly enhances the reliability of metabolite identification in microbial studies.
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