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Bioinformatics Advance Access originally published online on January 10, 2008
Bioinformatics 2008 24(5):674-681; doi:10.1093/bioinformatics/btn011
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© 2008 The Author(s)
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.0/uk/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

Incorporating sequence information into the scoring function: a hidden Markov model for improved peptide identification

Jainab Khatun 1, Eric Hamlett 1 and Morgan C. Giddings 1,2,3,*

1Department of Microbiology and Immunology, 2Department of Biomedical Engineering and 3Department of Computer Science, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA

*To whom correspondence should be addressed.


  Abstract

Motivation: The identification of peptides by tandem mass spectrometry (MS/MS) is a central method of proteomics research, but due to the complexity of MS/MS data and the large databases searched, the accuracy of peptide identification algorithms remains limited. To improve the accuracy of identification we applied a machine-learning approach using a hidden Markov model (HMM) to capture the complex and often subtle links between a peptide sequence and its MS/MS spectrum.

Model: Our model, HMM_Score, represents ion types as HMM states and calculates the maximum joint probability for a peptide/spectrum pair using emission probabilities from three factors: the amino acids adjacent to each fragmentation site, the mass dependence of ion types and the intensity dependence of ion types. The Viterbi algorithm is used to calculate the most probable assignment between ion types in a spectrum and a peptide sequence, then a correction factor is added to account for the propensity of the model to favor longer peptides. An expectation value is calculated based on the model score to assess the significance of each peptide/spectrum match.

Results: We trained and tested HMM_Score on three data sets generated by two different mass spectrometer types. For a reference data set recently reported in the literature and validated using seven identification algorithms, HMM_Score produced 43% more positive identification results at a 1% false positive rate than the best of two other commonly used algorithms, Mascot and X!Tandem. HMM_Score is a highly accurate platform for peptide identification that works well for a variety of mass spectrometer and biological sample types.

Availability: The program is freely available on ProteomeCommons via an OpenSource license. See http://bioinfo.unc.edu/downloads/ for the download link.

Contact: giddings{at}unc.edu, giddings{at}med.unc.edu

Supplementary information: Supplementary data are available at Bioinformatics online.

Associate Editor: Chris Stoeckert

Received on July 27, 2007; revised on January 4, 2008; accepted on January 4, 2008

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