An Annotation Consistency Checker for Evidence-Based Evaluation of Untargeted LC-MS/MS Metabolite Annotations
10 affiliations
- 1Skaggs School of Pharmacy and Pharmaceutical Sciences
- 2University of California San Diego
- 3Équipe VirPath, INSERM U1111, CNRS UMR 5308
- 4Centre International de Recherche en Infectiologie Laboratoire de Virologie et Pathologie Humaine
- 5Laboratoire TIMC Recherche Translationnelle et Innovation en Médecine et Complexité - Équipe TrEE
- 6Univ Grenoble Alpes - CNRS UMR 5525 - Vetagro Sup - Grenoble INP
- 7Plateforme de Métabolomique GEMELI-GExiM, Institut de Biologie et Pathologie
- 8CHU Grenoble - Université Grenoble Alpes
- 9Department of Computer Science and Engineering
- 10University of California
Abstract
Untargeted LC-MS/MS metabolomics routinely produces complex annotation landscapes in which individual spectra can yield multiple candidate metabolite annotations. Although spectral similarity provides an essential foundation for library-based annotation, determining whether a candidate annotation is consistent with the available evidence and whether alternative explanations remain plausible requires integration of multiple lines of evidence. In current practice, this evaluation is largely embedded in expert judgment, making annotation review difficult to teach, standardize, and reproduce. Here, we introduce an annotation consistency checker that aids this expert review process by generating a user-inspectable report for spectral library matches. The framework integrates complementary evidence dimensions, including spectral agreement, structural relationships among candidate annotations, and competing library matches to highlight annotations that are supported by, or inconsistent with, the available data. Biological context is provided separately to aid interpretation of metabolite occurrence across organisms, tissues, and sample types but is not incorporated into the annotation score. By enabling systematic evaluation of multiple evidence streams, this approach provides a transparent workflow that helps users critically assess metabolite annotations, reduce overinterpretation of spectral matches, and make informed decisions beyond only accepting the top-ranked library match.
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BibTeX
@misc{nunes2026annotation,
title = {{An Annotation Consistency Checker for Evidence-Based Evaluation of Untargeted LC-MS/MS Metabolite Annotations}},
author = {Wilhan D. G. Nunes and Edward J. Mathias and Charles Terra and Audrey Le Gouellec and Mingxun Wang and Pieter C. Dorrestein and Helena Mannochio-Russo},
howpublished = {ChemRxiv},
year = {2026},
month = oct,
doi = {10.26434/chemrxiv.15010158/v1},
url = {https://chemrxiv.org/doi/full/10.26434/chemrxiv.15010158/v1},
note = {CC BY-NC},
}
RIS
TY - UNPB
TI - An Annotation Consistency Checker for Evidence-Based Evaluation of Untargeted LC-MS/MS Metabolite Annotations
AU - Wilhan D. G. Nunes
AU - Edward J. Mathias
AU - Charles Terra
AU - Audrey Le Gouellec
AU - Mingxun Wang
AU - Pieter C. Dorrestein
AU - Helena Mannochio-Russo
PY - 2026
DA - 2026/10/09
PB - ChemRxiv
DO - 10.26434/chemrxiv.15010158/v1
UR - https://chemrxiv.org/doi/full/10.26434/chemrxiv.15010158/v1
AB - Untargeted LC-MS/MS metabolomics routinely produces complex annotation landscapes in which individual spectra can yield multiple candidate metabolite annotations. Although spectral similarity provides an essential foundation for library-based annotation, determining whether a candidate annotation is consistent with the available evidence and whether alternative explanations remain plausible requires integration of multiple lines of evidence. In current practice, this evaluation is largely embedded in expert judgment, making annotation review difficult to teach, standardize, and reproduce. Here, we introduce an annotation consistency checker that aids this expert review process by generating a user-inspectable report for spectral library matches. The framework integrates complementary evidence dimensions, including spectral agreement, structural relationships among candidate annotations, and competing library matches to highlight annotations that are supported by, or inconsistent with, the available data. Biological context is provided separately to aid interpretation of metabolite occurrence across organisms, tissues, and sample types but is not incorporated into the annotation score. By enabling systematic evaluation of multiple evidence streams, this approach provides a transparent workflow that helps users critically assess metabolite annotations, reduce overinterpretation of spectral matches, and make informed decisions beyond only accepting the top-ranked library match.
ER -