Open Access
December 2012 A likelihood-based scoring method for peptide identification using mass spectrometry
Qunhua Li, Jimmy K. Eng, Matthew Stephens
Ann. Appl. Stat. 6(4): 1775-1794 (December 2012). DOI: 10.1214/12-AOAS568


Mass spectrometry provides a high-throughput approach to identify proteins in biological samples. A key step in the analysis of mass spectrometry data is to identify the peptide sequence that, most probably, gave rise to each observed spectrum. This is often tackled using a database search: each observed spectrum is compared against a large number of theoretical “expected” spectra predicted from candidate peptide sequences in a database, and the best match is identified using some heuristic scoring criterion. Here we provide a more principled, likelihood-based, scoring criterion for this problem. Specifically, we introduce a probabilistic model that allows one to assess, for each theoretical spectrum, the probability that it would produce the observed spectrum. This probabilistic model takes account of peak locations and intensities, in both observed and theoretical spectra, which enables incorporation of detailed knowledge of chemical plausibility in peptide identification. Besides placing peptide scoring on a sounder theoretical footing, the likelihood-based score also has important practical benefits: it provides natural measures for assessing the uncertainty of each identification, and in comparisons on benchmark data it produced more accurate peptide identifications than other methods, including SEQUEST. Although we focus here on peptide identification, our scoring rule could easily be integrated into any downstream analyses that require peptide-spectrum match scores.


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Qunhua Li. Jimmy K. Eng. Matthew Stephens. "A likelihood-based scoring method for peptide identification using mass spectrometry." Ann. Appl. Stat. 6 (4) 1775 - 1794, December 2012.


Published: December 2012
First available in Project Euclid: 27 December 2012

zbMATH: 1257.62106
MathSciNet: MR3058683
Digital Object Identifier: 10.1214/12-AOAS568

Keywords: Generative model , maximum likelihood , peptide identification , proteomics

Rights: Copyright © 2012 Institute of Mathematical Statistics

Vol.6 • No. 4 • December 2012
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