This article is part of the supplement: Statistical mass spectrometry-based proteomics
A statistical model-building perspective to identification of MS/MS spectra with PeptideProphet
1 Department of Statistics, Purdue University, 250 N. University Street, West Lafayette, Indiana, USA
2 Department of Computer Science, Purdue University, 305 N. University Street, West Lafayette, Indiana, USA
3 Department of Pathology, University of Michigan, 4237 Medical Science I, Ann Arbor, Michigan, USA
Citation and License
BMC Bioinformatics 2012, 13(Suppl 16):S1 doi:10.1186/1471-2105-13-S16-S1Published: 5 November 2012
PeptideProphet is a post-processing algorithm designed to evaluate the confidence in identifications of MS/MS spectra returned by a database search. In this manuscript we describe the "what and how" of PeptideProphet in a manner aimed at statisticians and life scientists who would like to gain a more in-depth understanding of the underlying statistical modeling. The theory and rationale behind the mixture-modeling approach taken by PeptideProphet is discussed from a statistical model-building perspective followed by a description of how a model can be used to express confidence in the identification of individual peptides or sets of peptides. We also demonstrate how to evaluate the quality of model fit and select an appropriate model from several available alternatives. We illustrate the use of PeptideProphet in association with the Trans-Proteomic Pipeline, a free suite of software used for protein identification.