ArticleAnalytical chemistry2022
Automated Annotation of Untargeted All-Ion Fragmentation LC-MS Metabolomics Data with MetaboAnnotatoR.
Article in Analytical chemistry, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
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Who cites it
23 citing papers in PubMed.
- MALDI Mass Spectrometry Imaging in Alzheimer's Disease Lipidomics: Matrix Selection, Spatial Lipid Pathology and Emerging Analytical Strategies.International journal of molecular sciences · 2026Review
- Activity-guided substructure prioritization accelerates discovery of gut microbiota-derived immune-regulating metabolites.Nature communications · 2026Article
- The chemical interaction between plants and the rhizosphere microbiome.Trends in plant science · 2025Review
- Broadband Collision-Induced Dissociation Mass Spectrometry Imaging.Journal of the American Society for Mass Spectrometry · 2025Article
- Synthetic Biology in Natural Product Biosynthesis.Chemical reviews · 2025Review
- Structural annotation of full-scan MS data: A unified solution for LC-MS and MS imaging analyses.bioRxiv : the preprint server for biology · 2025Article
- Phytochemistry, quality control and biosynthesis in ginseng research from 2021 to 2023: A state-of-the-art review concerning advances and challenges.Chinese herbal medicines · 2024Review
- NPAnalytical chemistry · 2024Article
- MetaboAnalystR 4.0: a unified LC-MS workflow for global metabolomics.Nature communications · 2024Article
- LC-MS/DIA-based strategy for comprehensive flavonoid profiling: anRSC advances · 2024Article
- Enhanced efficiency of MS/MS all-ion fragmentation for non-targeted analysis of trace contaminants in surface water using multivariate curve resolution and data fusion.Analytical and bioanalytical chemistry · 2024Article
- Reactivity-based identification of oxygen containing functional groups of chemicals applied as potential classifier in non-target analysis.Scientific reports · 2023Article
- Cumulative Neutral Loss Model for Fragment Deconvolution in Electrospray Ionization High-Resolution Mass Spectrometry Data.Analytical chemistry · 2023Article
- IDSL.CSA: Composite Spectra Analysis for Chemical Annotation of Untargeted Metabolomics Datasets.Analytical chemistry · 2023Article
- Recent advances in mass spectrometry-based computational metabolomics.Current opinion in chemical biology · 2023Review
- IDSL.CSA: Composite Spectra Analysis for Chemical Annotation of Untargeted Metabolomics Datasets.bioRxiv : the preprint server for biology · 2023Article
- Oligonucleotide Catabolism-Derived Gluconucleosides inJournal of the American Chemical Society · 2023Article
- MAW: the reproducible Metabolome Annotation Workflow for untargeted tandem mass spectrometry.Journal of cheminformatics · 2023Article
- Unveiling Microbial Chemical Interactions Based on Metabolomics Approaches.Advances in experimental medicine and biology · 2023Article
- Simulated-to-real benchmarking of acquisition methods in untargeted metabolomics.Frontiers in molecular biosciences · 2023Article
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8 authors.
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Abstract
Untargeted metabolomics and lipidomics LC-MS experiments produce complex datasets, usually containing tens of thousands of features from thousands of metabolites whose annotation requires additional MS/MS experiments and expert knowledge. All-ion fragmentation (AIF) LC-MS/MS acquisition provides fragmentation data at no additional experimental time cost. However, analysis of such datasets requires reconstruction of parent-fragment relationships and annotation of the resulting pseudo-MS/MS spectra. Here, we propose a novel approach for automated annotation of isotopologues, adducts, and in-source fragments from AIF LC-MS datasets by combining correlation-based parent-fragment linking with molecular fragment matching. Our workflow focuses on a subset of features rather than trying to annotate the full dataset, saving time and simplifying the process. We demonstrate the workflow in three human serum datasets containing 599 features manually annotated by experts. Precision and recall values of 82-92% and 82-85%, respectively, were obtained for features found in the highest-rank scores (1-5). These results equal or outperform those obtained using MS-DIAL software, the current state of the art for AIF data annotation. Further validation for other biological matrices and different instrument types showed variable precision (60-89%) and recall (10-88%) particularly for datasets dominated by nonlipid metabolites. The workflow is freely available as an open-source R package, MetaboAnnotatoR, together with the fragment libraries from Github (https://github.com/gggraca/MetaboAnnotatoR).
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