ArticleMetabolites2018
Mind the Gap: Mapping Mass Spectral Databases in Genome-Scale Metabolic Networks Reveals Poorly Covered Areas.
Article in Metabolites, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 38 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
38 citing papers in PubMed, 76 citations in OpenAlex.
- The MassBank contributions of the mFam collaboration.Metabolomics : Official journal of the Metabolomic Society · 2026Article
- Comprehensive Curation and Harmonization of Small-Molecule MS/MS Libraries in Spectraverse.Analytical chemistry · 2026Article
- ChemEmbed: a deep learning framework for metabolite identification using enhanced MS/MS data and multidimensional molecular embeddings.Briefings in bioinformatics · 2026Article
- Integrating metagenome-scale metabolic models and metabolomics to explore candidate biochemical interactions in cultivatedISME communications · 2026Article
- Assessing the Impact of Measurement Precision on Metabolite Identification Probability in Multidimensional Mass Spectrometry-Based, Reference-Free Metabolomics.Analytical chemistry · 2025Article
- SingleFrag: a deep learning tool for MS/MS fragment and spectral prediction and metabolite annotation.Briefings in bioinformatics · 2025Article
- Introducing "Identification Probability" for Automated and Transferable Assessment of Metabolite Identification Confidence in Metabolomics and Related Studies.Analytical chemistry · 2025Review
- Navigating common pitfalls in metabolite identification and metabolomics bioinformatics.Metabolomics : Official journal of the Metabolomic Society · 2024Review
- Introducing 'identification probability' for automated and transferable assessment of metabolite identification confidence in metabolomics and related studies.bioRxiv : the preprint server for biology · 2024Article
- Review
- Characterization of Electrospray Ionization Complexity in Untargeted Metabolomic Studies.Analytical chemistry · 2024Article
- Genome scale metabolic network modelling for metabolic profile predictions.PLoS computational biology · 2024Article
- Multiplatform Metabolomics Characterization Reveals Novel Metabolites and Phospholipid Compositional Rules ofInternational journal of molecular sciences · 2023Article
- The human microbial exposome: expanding the Exposome-Explorer database with gut microbial metabolites.Scientific reports · 2023Article
- Current State and Future Perspectives on Personalized Metabolomics.Metabolites · 2023Review
- Reconciliation and evolution of Penicillium rubens genome-scale metabolic networks-What about specialised metabolism?PloS one · 2023Article
- Good practices and recommendations for using and benchmarking computational metabolomics metabolite annotation tools.Metabolomics : Official journal of the Metabolomic Society · 2022Review
- Metabolite discovery: Biochemistry's scientific driver.Cell metabolism · 2022Review
- Networks and Graphs Discovery in Metabolomics Data Analysis and Interpretation.Frontiers in molecular biosciences · 2022Review
- Metabolomics for personalized medicine: the input of analytical chemistry from biomarker discovery to point-of-care tests.Analytical and bioanalytical chemistry · 2022Review
Corrections and comments
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Authors and funding
7 authors at 5 institutions in 5 countries.
Funding
Abstract
The use of mass spectrometry-based metabolomics to study human, plant and microbial biochemistry and their interactions with the environment largely depends on the ability to annotate metabolite structures by matching mass spectral features of the measured metabolites to curated spectra of reference standards. While reference databases for metabolomics now provide information for hundreds of thousands of compounds, barely 5% of these known small molecules have experimental data from pure standards. Remarkably, it is still unknown how well existing mass spectral libraries cover the biochemical landscape of prokaryotic and eukaryotic organisms. To address this issue, we have investigated the coverage of 38 genome-scale metabolic networks by public and commercial mass spectral databases, and found that on average only 40% of nodes in metabolic networks could be mapped by mass spectral information from standards. Next, we deciphered computationally which parts of the human metabolic network are poorly covered by mass spectral libraries, revealing gaps in the eicosanoids, vitamins and bile acid metabolism. Finally, our network topology analysis based on the betweenness centrality of metabolites revealed the top 20 most important metabolites that, if added to MS databases, may facilitate human metabolome characterization in the future.
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.