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RESEARCH PRODUCT
Comparing Targeted vs. Untargeted MS2 Data-Dependent Acquisition for Peak Annotation in LC-MS Metabolomics
Teresa Martínez-senaMarta Moreno-torresJuan Daniel Sanjuan-herráezMáximo VentoGuillermo QuintásIsabel Ten-doménechAnna Parra-llorcaJulia KuligowskiJosé V. CastellJosé V. Castellsubject
0301 basic medicineBioquímicaBiologiaComputer scienceEndocrinology Diabetes and Metabolismlcsh:QR1-50201 natural sciencesBiochemistryliquid chromatography–mass spectrometryArticlelcsh:Microbiology03 medical and health sciencesAnnotationMetabolomicsLiquid chromatography–mass spectrometrypeak annotationMolecular BiologyData dependentliquid chromatography-mass spectrometrydata dependent acquisitionbusiness.industry010401 analytical chemistryhuman milkPattern recognition0104 chemical sciencesWorking range030104 developmental biologyFeature (computer vision)Reference databaseArtificial intelligencebusinessMETABOLIC FEATURESdescription
One of the most widely used strategies for metabolite annotation in untargeted LCMS is based on the analysis of MSn spectra acquired using data-dependent acquisition (DDA), where precursor ions are sequentially selected from MS scans based on user-selected criteria. However, the number of MSn spectra that can be acquired during a chromatogram is limited and a trade-off between analytical speed, sensitivity and coverage must be ensured. In this research, we compare four different strategies for automated MS2 DDA, which can be easily implemented in the frame of standard QA/QC workflows for untargeted LC&ndash
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2020-03-01 |