Learning Score Function Parameters for Improved Spectrum Identification in Tandem Mass Spectrometry Experiments

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چکیده

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Learning score function parameters for improved spectrum identification in tandem mass spectrometry experiments.

The identification of proteins from spectra derived from a tandem mass spectrometry experiment involves several challenges: matching each observed spectrum to a peptide sequence, ranking the resulting collection of peptide-spectrum matches, assigning statistical confidence estimates to the matches, and identifying the proteins. The present work addresses algorithms to rank peptide-spectrum matc...

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We present a peptide-spectrum alignment strategy that employs a dynamic Bayesian network (DBN) for the identification of spectra produced by tandem mass spectrometry (MS/MS). Our method is fundamentally generative in that it models peptide fragmentation in MS/MS as a physical process. The model traverses an observed MS/MS spectrum and a peptide-based theoretical spectrum to calculate the best a...

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Accurate assignment of peptide sequences to observed fragmentation spectra is hindered by the large number of hypotheses that must be considered for each observed spectrum. A high score assigned to a particular peptide-spectrum match (PSM) may not end up being statistically significant after multiple testing correction. Researchers can mitigate this problem by controlling the hypothesis space i...

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ژورنال

عنوان ژورنال: Journal of Proteome Research

سال: 2012

ISSN: 1535-3893,1535-3907

DOI: 10.1021/pr300234m