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• W2056000843 abstract "Phosphopeptide/protein identification using tandem mass spectrometry (MS/MS) is a challenging issue in proteomics research. In particular, phosphopeptides typically exhibit low intensity peaks of b and y ions in spectra when serine or threonine is phosphorylated. Consequently, the existing algorithms for peptide and protein identification generate a high false discovery rate when coping with phosphopeptide spectra. In order to increase the number of correct phosphopeptide identifications using database search, a new data mining approach for spectra preprocessing is proposed. A support vector machine classifier is used to calculate the probability of each peak representing a b or y ion. Next, low-probability peaks are removed from spectra, while remaining peaks have their intensities enhanced. As a result, a huge increase in signal-to-noise ratio is provided and the chances of detecting important peaks are significantly advanced. Experiments using MASCOT and SEQUEST along with Peptide/ProteinProphet and a decoy database approach showed a significant improvement in the sensitivity of phosphopeptide identification without compromising specificity, demonstrating that our new strategy for MS/MS spectra preprocessing is a powerful proteomics tool for enhancing phosphopeptide identifications." @default.
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• W2056000843 date "2009-01-01" @default.
• W2056000843 modified "2023-09-25" @default.
• W2056000843 title "Improving Phosphopeptide/Protein Identification Using a New Data Mining Framework for MS/MS Spectra Preprocessing" @default.
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• W2056000843 doi "https://doi.org/10.4172/jpb.1000072" @default.
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