A statistical model-building perspective to identification of MS/MS spectra with PeptideProphet

BMC Bioinformatics. 2012;13 Suppl 16(Suppl 16):S1. doi: 10.1186/1471-2105-13-S16-S1. Epub 2012 Nov 5.

Abstract

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.

Publication types

  • Research Support, N.I.H., Extramural
  • Research Support, U.S. Gov't, Non-P.H.S.

MeSH terms

  • Algorithms
  • Humans
  • Models, Statistical*
  • Peptides / chemistry*
  • Proteomics / statistics & numerical data*
  • Software*
  • Tandem Mass Spectrometry / statistics & numerical data*

Substances

  • Peptides