ArticleJournal of cheminformatics2017
Comprehensive comparison of in silico MS/MS fragmentation tools of the CASMI contest: database boosting is needed to achieve 93% accuracy.
Article in Journal of cheminformatics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 50 papers.
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Who cites it
50 citing papers in PubMed.
- Structural determination of natural products at speeds and scale of high-throughput screening.Natural product reports · 2026Review
- Evaluation of MassFrontier, MetFrag, MS-FINDER, and SIRIUS for Metabolite Annotation Using an Experimental LC-HRMS Dataset.Biomedicines · 2026Article
- FIDDLE: a deep learning method for chemical formulas prediction from tandem mass spectra.Nature communications · 2025Article
- ModiFinder: Tandem Mass Spectral Alignment Enables Structural Modification Site Localization.Journal of the American Society for Mass Spectrometry · 2024Article
- High-Resolution Mass Spectrometry for Human Exposomics: Expanding Chemical Space Coverage.Environmental science & technology · 2024Review
- A spectroscopic test suggests that fragment ion structure annotations in MS/MS libraries are frequently incorrect.Communications chemistry · 2024Article
- Deep Learning-Enabled MS/MS Spectrum Prediction Facilitates Automated Identification Of Novel Psychoactive Substances.Analytical chemistry · 2023Article
- Comparison of Compound Identification Tools Using Data Dependent and Data Independent High-Resolution Mass Spectrometry Spectra.Metabolites · 2023Article
- Good practices and recommendations for using and benchmarking computational metabolomics metabolite annotation tools.Metabolomics : Official journal of the Metabolomic Society · 2022Review
- Review
- How Well Can We Predict Mass Spectra from Structures? Benchmarking Competitive Fragmentation Modeling for Metabolite Identification on Untrained Tandem Mass Spectra.Journal of chemical information and modeling · 2022Article
- Autonomous Chemical Experiments: Challenges and Perspectives on Establishing a Self-Driving Lab.Accounts of chemical research · 2022Article
- High-confidence structural annotation of metabolites absent from spectral libraries.Nature biotechnology · 2022Article
- Evaluating the Accuracy of the QCEIMS Approach for Computational Prediction of Electron Ionization Mass Spectra of Purines and Pyrimidines.Metabolites · 2022Article
- Sunflower Bark Extract as a Biostimulant Suppresses Reactive Oxygen Species in Salt-Stressed Arabidopsis.Frontiers in plant science · 2022Article
- Untargeted Metabolomic Profiling of Fungal Species Populations.Methods in molecular biology (Clifton, N.J.) · 2022Article
- Strategies for structure elucidation of small molecules based on LC-MS/MS data from complex biological samples.Computational and structural biotechnology journal · 2022Review
- MassGenie: A Transformer-Based Deep Learning Method for Identifying Small Molecules from Their Mass Spectra.Biomolecules · 2021Article
- New Advances in Tissue Metabolomics: A Review.Metabolites · 2021Review
- Large-Scale Implementation and Flaw Investigation of Human Serum Suspect Screening Analysis for Industrial Chemicals.Journal of the American Society for Mass Spectrometry · 2021Article
Corrections and comments
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Authors and funding
12 authors.
Funding
Abstract
In mass spectrometry-based untargeted metabolomics, rarely more than 30% of the compounds are identified. Without the true identity of these molecules it is impossible to draw conclusions about the biological mechanisms, pathway relationships and provenance of compounds. The only way at present to address this discrepancy is to use in silico fragmentation software to identify unknown compounds by comparing and ranking theoretical MS/MS fragmentations from target structures to experimental tandem mass spectra (MS/MS). We compared the performance of four publicly available in silico fragmentation algorithms (MetFragCL, CFM-ID, MAGMa+ and MS-FINDER) that participated in the 2016 CASMI challenge. We found that optimizing the use of metadata, weighting factors and the manner of combining different tools eventually defined the ultimate outcomes of each method. We comprehensively analysed how outcomes of different tools could be combined and reached a final success rate of 93% for the training data, and 87% for the challenge data, using a combination of MAGMa+, CFM-ID and compound importance information along with MS/MS matching. Matching MS/MS spectra against the MS/MS libraries without using any in silico tool yielded 60% correct hits, showing that the use of in silico methods is still important.
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Registered trials
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