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• W3210617360 abstract "Glycerophospholipids (GPs) have a wide variety and complex structure, which makes their identification challenging. Our software affords a novel tool for the automated identification of non-target GPs in biological mixtures. Here, we explored the multi-stage fragmentation processes of GPs in positive and negative ion modes, and then constructed multi-stage fragment ion databases. This database includes 8214 simulated GP molecules from a random combination of fatty acids corresponding to 42,439 self-built predicted multi-stage fragment ions in positive ion mode and 31,487 self-built predicted multi-stage fragment ions in negative ion mode (MS ≤ 3). The automatic GP identification (AGPI) software can screen out GP candidates utilizing the MS1 accurate mass. The isomers of fatty acid chains and the phosphoryl head group can be distinguished using the MS2 and MS3 fragment spectra in positive-ion and negative-ion modes. All of the selected 45 GP standards were putatively identified using AGPI software; however, there were false positives because the software cannot distinguish positional isomers of fatty acids. Therefore, the AGPI software could be applied to identify GPs in samples, such as cancer cells; we successfully identified 41 GPs in cancer cells." @default.
• W3210617360 created "2021-11-08" @default.
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• W3210617360 date "2022-02-01" @default.
• W3210617360 modified "2023-09-27" @default.
• W3210617360 title "Identification of glycerophospholipids using self-built recognition software based on positive and negative ion high-resolution mass spectrometric fragmentation experiments" @default.
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• W3210617360 doi "https://doi.org/10.1016/j.talanta.2021.123006" @default.
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