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Bioinformatics Advance Access published online on November 17, 2007
Bioinformatics, doi:10.1093/bioinformatics/btm555
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© The Author (2007). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

A Hierarchical Statistical Model to Assess the Confidence of Peptides and Proteins Inferred from Tandem Mass Spectrometry

Changyu Shen *,1, Zhiping Wang 1, Ganesh Shankar 1, Xiang Zhang 2 and Lang Li 1

1Division of Biostatistics, Department of Medicine, Indiana University School of Medicine, Indianapolis, IN 46202, USA
2Bindley Bioscience Center, Purdue University, West Lafayette, IN 47907, USA

*To whom correspondence should be addressed. Dr. Changyu Shen, E-mail: chashen{at}iupui.edu


  Abstract

Motivation: Statistical evaluation of the confidence of peptide and protein identifications made by tandem mass spectrometry is a critical component for appropriately interpreting the experimental data and conducting downstream analysis. Although many approaches have been developed to assign confidence measure from different perspectives, a unified statistical framework that integrates the uncertainty of peptides and proteins is still missing.

Results: We developed a hierarchical statistical model (HSM) that jointly models the uncertainty of the identified peptides and proteins and can be applied to any scoring system. With data sets of a standard mixture and the yeast proteome, we demonstrate that the HSM offers a reliable or at least conservative false discovery rate (FDR) estimate for peptide and protein identifications. The probability measure of HSM also offers a powerful discriminating score for peptide identification.

Availability: The algorithm is available upon request from the authors

Contact: chashen{at}iupui.edu

Associate Editor: Dr. Olga Troyanskaya

Received on June 25, 2007; revised on October 4, 2007; accepted on November 1, 2007

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