The Annals of Applied Statistics

A likelihood-based scoring method for peptide identification using mass spectrometry

Qunhua Li, Jimmy K. Eng, and Matthew Stephens

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Abstract

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.

Article information

Source
Ann. Appl. Stat. Volume 6, Number 4 (2012), 1775-1794.

Dates
First available in Project Euclid: 27 December 2012

Permanent link to this document
http://projecteuclid.org/euclid.aoas/1356629059

Digital Object Identifier
doi:10.1214/12-AOAS568

Mathematical Reviews number (MathSciNet)
MR3058683

Zentralblatt MATH identifier
1257.62106

Keywords
Generative model maximum likelihood peptide identification proteomics

Citation

Li, Qunhua; Eng, Jimmy K.; Stephens, Matthew. A likelihood-based scoring method for peptide identification using mass spectrometry. Ann. Appl. Stat. 6 (2012), no. 4, 1775--1794. doi:10.1214/12-AOAS568. http://projecteuclid.org/euclid.aoas/1356629059.


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Supplemental materials

  • Supplementary material: Preprocessing procedure. We describe the preprocessing steps in this supplement.
    Digital Object Identifier: doi:10.1214/12-AOAS568SUPP
    Supplemental files available for subscribers.

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