ArticleMolecular & cellular proteomics : MCP2014
Automated label-free quantification of metabolites from liquid chromatography-mass spectrometry data.
Article in Molecular & cellular proteomics : MCP, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 41 papers.
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Who cites it
41 citing papers in PubMed.
- LC-MS Data Processing Using OpenMS.Methods in molecular biology (Clifton, N.J.) · 2026Article
- Not One Method to Rule Them All: A Comparative Study of Chromatographic Platforms (RP-LC-, HILIC-, SFC-, and IC-HRMS) for Water Analysis.Analytical chemistry · 2025Article
- A stochastic approach for parameter optimization of feature detection algorithms for non-target screening in mass spectrometry.Analytical and bioanalytical chemistry · 2025Article
- Fully Automated Deep Learning Enabled Miniature Mass Spectrometry System for Psychoactive Therapeutic Drug Monitoring.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Article
- Comprehensive Blood Metabolome and Exposome Analysis, Annotation, and Interpretation in E-Waste Workers.Metabolites · 2024Article
- FLASHQuant: A Fast Algorithm for Proteoform Quantification in Top-Down Proteomics.Analytical chemistry · 2024Article
- The association of infant urinary adrenal steroids with the risk of childhood asthma development.Annals of allergy, asthma & immunology : official publication of the American College of Allergy, Asthma, & Immunology · 2024Article
- PFΔScreen - an open-source tool for automated PFAS feature prioritization in non-target HRMS data.Analytical and bioanalytical chemistry · 2024Article
- Autologous hematopoietic stem cell transplantation significantly alters circulating ceramides in peripheral blood of relapsing-remitting multiple sclerosis patients.Lipids in health and disease · 2023Article
- Disease phenotype prediction in multiple sclerosis.iScience · 2023Article
- TopFD: A Proteoform Feature Detection Tool for Top-Down Proteomics.Analytical chemistry · 2023Article
- UmetaFlow: an untargeted metabolomics workflow for high-throughput data processing and analysis.Journal of cheminformatics · 2023Article
- Predicting RP-LC retention indices of structurally unknown chemicals from mass spectrometry data.Journal of cheminformatics · 2023Article
- Sensitive Detection of Pharmaceutical Drugs and Metabolites in Serum Using Data-Independent Acquisition Mass Spectrometry and Open-Access Data Acquisition Tools.Pharmaceuticals (Basel, Switzerland) · 2022Article
- MetHoS: a platform for large-scale processing, storage and analysis of metabolomics data.BMC bioinformatics · 2022Article
- Power of mzRAPP-Based Performance Assessments in MS1-Based Nontargeted Feature Detection.Analytical chemistry · 2022Article
- DIAMetAlyzer allows automated false-discovery rate-controlled analysis for data-independent acquisition in metabolomics.Nature communications · 2022Article
- An anchored experimental design and meta-analysis approach to address batch effects in large-scale metabolomics.Frontiers in molecular biosciences · 2022Article
- Mass Deconvolution of Top-Down Mass Spectrometry Datasets by FLASHDeconv.Methods in molecular biology (Clifton, N.J.) · 2022Article
Corrections and comments
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Liquid chromatography coupled to mass spectrometry (LC-MS) has become a standard technology in metabolomics. In particular, label-free quantification based on LC-MS is easily amenable to large-scale studies and thus well suited to clinical metabolomics. Large-scale studies, however, require automated processing of the large and complex LC-MS datasets. We present a novel algorithm for the detection of mass traces and their aggregation into features (i.e. all signals caused by the same analyte species) that is computationally efficient and sensitive and that leads to reproducible quantification results. The algorithm is based on a sensitive detection of mass traces, which are then assembled into features based on mass-to-charge spacing, co-elution information, and a support vector machine-based classifier able to identify potential metabolite isotope patterns. The algorithm is not limited to metabolites but is applicable to a wide range of small molecules (e.g. lipidomics, peptidomics), as well as to other separation technologies. We assessed the algorithm's robustness with regard to varying noise levels on synthetic data and then validated the approach on experimental data investigating human plasma samples. We obtained excellent results in a fully automated data-processing pipeline with respect to both accuracy and reproducibility. Relative to state-of-the art algorithms, ours demonstrated increased precision and recall of the method. The algorithm is available as part of the open-source software package OpenMS and runs on all major operating systems.
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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.