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

BMC Bioinformatics

Research

Alexey I Nesvizhskii¹  Kelvin Ma²  Olga Vitek³ 

[1] Department of Pathology, University of Michigan, 4237 Medical Science I, Ann Arbor, Michigan, USA;Department of Statistics, Purdue University, 250 N. University Street, West Lafayette, Indiana, USA;Department of Statistics, Purdue University, 250 N. University Street, West Lafayette, Indiana, USA;Department of Computer Science, Purdue University, 305 N. University Street, West Lafayette, Indiana, USA;

关键词: False Discovery Rate;    Discriminant Function;    Gumbel Distribution;    Spectrum Identification;    Incorrect Identification;   

DOI  :  10.1186/1471-2105-13-S16-S1

来源: Springer

【 摘 要 】

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.

【 授权许可】

Unknown   
© Ma et al.; licensee BioMed Central Ltd. 2012. This article is published under license to BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.