2010
DOI: 10.1093/bioinformatics/btq341
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Prophossi: automating expert validation of phosphopeptide–spectrum matches from tandem mass spectrometry

Abstract: Motivation: Complex patterns of protein phosphorylation mediate many cellular processes. Tandem mass spectrometry (MS/MS) is a powerful tool for identifying these post-translational modifications. In high-throughput experiments, mass spectrometry database search engines, such as MASCOT provide a ranked list of peptide identifications based on hundreds of thousands of MS/MS spectra obtained in a mass spectrometry experiment. These search results are not in themselves sufficient for confident assignment of phosp… Show more

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Cited by 17 publications
(16 citation statements)
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“…Although no rules have been established for the fragmentation of dimethylated and tagged 3AT-containing peptide sequences, several can be adapted from those applied to automated expert validation of phosphopeptide MS/MS-spectrum matches (ProPhosSI) 55 :…”
Section: Figurementioning
confidence: 99%
“…Although no rules have been established for the fragmentation of dimethylated and tagged 3AT-containing peptide sequences, several can be adapted from those applied to automated expert validation of phosphopeptide MS/MS-spectrum matches (ProPhosSI) 55 :…”
Section: Figurementioning
confidence: 99%
“…The first type is "automating expert validation" based methods, which simulate the approach for manual interpretation of phosphopeptide MS 2 spectra. 44 The basic concept of these methods is the selection of appropriate features and corresponding criteria. Since most of the phosphopeptides could generate neutral loss during CID fragmentation, the phosphate neutral loss in MS 2 spectrum can be used as a unique feature to confirm the presence of the phosphopeptides.…”
Section: Perspectivementioning
confidence: 99%
“…More recently, a hybrid Support Vector Machine (SVM)/Dynamic Bayes Network (DBN) approach was used to classify MS/MS data, and was shown to increase positive identifications in 1% FDR search results [8]. To circumvent the need for large amounts of training data for classification methods, another approach is to create a rule-based framework where prominent fragments are predicted based on expert criteria [9], although codifying experiential human knowledge still limits results to those peptides that match prescribed criteria, therefore hindering generalization to peptides with different PTM’s. The “expect” score in MASCOT provides another, peptide sequence specific, alternative to the FDR.…”
Section: Introductionmentioning
confidence: 99%