CorrectOverloadedPeaks (R package to correct APCI-HR-MS peak data)
- Metabolomics, the analysis of potentially all small molecules within a biological system, has become a valuable tool for biomarker identification and the elucidation of biological processes. While metabolites are often present in complex mixtures at extremely different concentrations, the dynamic range of available analytical methods to capture this variance is generally limited. Here, we show that gas chromatography coupled to atmospheric pressure chemical ionization mass spectrometry (GC-APCI-MS), a state of the art analytical technology applied in metabolomics analyses, shows an average linear range (LR) of 2.39 orders of magnitude for a set of 62 metabolites from a representative compound mixture. We further developed a computational tool to extend this dynamic range on average by more than 1 order of magnitude, demonstrated with a dilution series of the compound mixture, using robust and automatic reconstruction of intensity values exceeding the detection limit. The tool is freelyMetabolomics, the analysis of potentially all small molecules within a biological system, has become a valuable tool for biomarker identification and the elucidation of biological processes. While metabolites are often present in complex mixtures at extremely different concentrations, the dynamic range of available analytical methods to capture this variance is generally limited. Here, we show that gas chromatography coupled to atmospheric pressure chemical ionization mass spectrometry (GC-APCI-MS), a state of the art analytical technology applied in metabolomics analyses, shows an average linear range (LR) of 2.39 orders of magnitude for a set of 62 metabolites from a representative compound mixture. We further developed a computational tool to extend this dynamic range on average by more than 1 order of magnitude, demonstrated with a dilution series of the compound mixture, using robust and automatic reconstruction of intensity values exceeding the detection limit. The tool is freely available as an R package (CorrectOverloadedPeaks) from CRAN (https://cran.r-project.org/) and can be incorporated in a metabolomics data processing pipeline facilitating large screening assays.…
Autor*innen: | Jan LisecORCiD |
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Dokumenttyp: | Forschungsdatensatz |
Veröffentlichungsform: | Graue Literatur |
Sprache: | Englisch |
Jahr der Erstveröffentlichung: | 2019 |
Organisationseinheit der BAM: | 1 Analytische Chemie; Referenzmaterialien |
1 Analytische Chemie; Referenzmaterialien / 1.7 Organische Spuren- und Lebensmittelanalytik | |
Herausgeber (Institution): | Bundesanstalt für Materialforschung und -prüfung (BAM) |
Verlag: | GitHub |
Verlagsort: | San Francisco, CA, USA |
DDC-Klassifikation: | Naturwissenschaften und Mathematik / Chemie / Analytische Chemie |
Freie Schlagwörter: | Data processing; Metabolomics; R package; Software |
Themenfelder/Aktivitätsfelder der BAM: | Chemie und Prozesstechnik |
Art der Ressource: | Software |
URL: | https://github.com/cran/CorrectOverloadedPeaks/ |
Zugehöriger Identifikator: | https://pubs.acs.org/doi/full/10.1021/acs.analchem.6b02515 |
Verfügbarkeit des Dokuments: | Datei für die Öffentlichkeit verfügbar ("Open Access") |
Lizenz (Deutsch): | Creative Commons - CC BY - Namensnennung 4.0 International |
Datum der Freischaltung: | 06.07.2023 |
Referierte Publikation: | Nein |
Schriftenreihen ohne Nummerierung: | Forschungsdatensätze der BAM |