ArticleBioinformatics (Oxford, England)2019
CliqueMS: a computational tool for annotating in-source metabolite ions from LC-MS untargeted metabolomics data based on a coelution similarity network.
Article in Bioinformatics (Oxford, England), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 39 papers.
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Who cites it
39 citing papers in PubMed.
- Integrated metabolomics data analysis to generate mechanistic hypotheses with MetaProViz.Molecular systems biology · 2026Article
- IMFrag: A Tool to Recognize In-Source Fragmentation in Ion Mobility-Enabled Data-Independent Acquisition Workflows.Analytical chemistry · 2026Article
- CorrelandJournal of the American Society for Mass Spectrometry · 2026Article
- Review
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- A guide to reverse metabolomics-a framework for big data discovery strategy.Nature protocols · 2025Review
- MS1FA: Shiny app for the annotation of redundant features in untargeted metabolomics datasets.Bioinformatics (Oxford, England) · 2025Article
- Structural annotation of full-scan MS data: A unified solution for LC-MS and MS imaging analyses.bioRxiv : the preprint server for biology · 2025Article
- Annotation of Metabolites in Stable Isotope Tracing Untargeted Metabolomics via Khipu-web.Journal of the American Society for Mass Spectrometry · 2024Article
- An assessment of AcquireX and Compound Discoverer software 3.3 for non-targeted metabolomics.Scientific reports · 2024Article
- Combination of Structure Databases, In Silico Fragmentation, and MS/MS Libraries for Untargeted Screening of Non-Volatile Migrants from Recycled High-Density Polyethylene Milk Bottles.Analytical chemistry · 2023Article
- Generalized Tree Structure to Annotate Untargeted Metabolomics and Stable Isotope Tracing Data.Analytical chemistry · 2023Article
- Systematic analysis of in-source modifications of primary metabolites during flow-injection time-of-flight mass spectrometry.Analytical biochemistry · 2023Article
- Generalized tree structure to annotate untargeted metabolomics and stable isotope tracing data.bioRxiv : the preprint server for biology · 2023Article
- Metabolite annotation from knowns to unknowns through knowledge-guided multi-layer metabolic networking.Nature communications · 2022Article
- IDSL.UFA Assigns High-Confidence Molecular Formula Annotations for Untargeted LC/HRMS Data Sets in Metabolomics and Exposomics.Analytical chemistry · 2022Article
- Using MetaboAnalyst 5.0 for LC-HRMS spectra processing, multi-omics integration and covariate adjustment of global metabolomics data.Nature protocols · 2022Review
- Recent advances in proteomics and metabolomics in plants.Molecular horticulture · 2022Review
- Relationship between lung function and exhaled volatile organic compounds in healthy infants.Pediatric pulmonology · 2022Article
- Strategies for structure elucidation of small molecules based on LC-MS/MS data from complex biological samples.Computational and structural biotechnology journal · 2022Review
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
motivationThe analysis of biological samples in untargeted metabolomic studies using LC-MS yields tens of thousands of ion signals. Annotating these features is of the utmost importance for answering questions as fundamental as, e.g. how many metabolites are there in a given sample.
resultsHere, we introduce CliqueMS, a new algorithm for annotating in-source LC-MS1 data. CliqueMS is based on the similarity between coelution profiles and therefore, as opposed to most methods, allows for the annotation of a single spectrum. Furthermore, CliqueMS improves upon the state of the art in several dimensions: (i) it uses a more discriminatory feature similarity metric; (ii) it treats the similarities between features in a transparent way by means of a simple generative model; (iii) it uses a well-grounded maximum likelihood inference approach to group features; (iv) it uses empirical adduct frequencies to identify the parental mass and (v) it deals more flexibly with the identification of the parental mass by proposing and ranking alternative annotations. We validate our approach with simple mixtures of standards and with real complex biological samples. CliqueMS reduces the thousands of features typically obtained in complex samples to hundreds of metabolites, and it is able to correctly annotate more metabolites and adducts from a single spectrum than available tools. AVAILABILITY AND IMPLEMENTATION: https://CRAN.R-project.org/package=cliqueMS and https://github.com/osenan/cliqueMS. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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