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Statistical validation of peptide identifications in large-scale proteomics using the target-decoy database search strategy and flexible mixture modeling.

Abstract：

:Reliable statistical validation of peptide and protein identifications is a top priority in large-scale mass spectrometry based proteomics. PeptideProphet is one of the computational tools commonly used for assessing the statistical confidence in peptide assignments to tandem mass spectra obtained using database search programs such as SEQUEST, MASCOT, or X! TANDEM. We present two flexible methods, the variable component mixture model and the semiparametric mixture model, that remove the restrictive parametric assumptions in the mixture modeling approach of PeptideProphet. Using a control protein mixture data set generated on an linear ion trap Fourier transform (LTQ-FT) mass spectrometer, we demonstrate that both methods improve parametric models in terms of the accuracy of probability estimates and the power to detect correct identifications controlling the false discovery rate to the same degree. The statistical approaches presented here require that the data set contain a sufficient number of decoy (known to be incorrect) peptide identifications, which can be obtained using the target-decoy database search strategy.

journal_name

J Proteome Res

journal_title

Journal of proteome research

authors

Choi H,Ghosh D,Nesvizhskii AI

doi

10.1021/pr7006818

subject

Has Abstract

pub_date

2008-01-01 00:00:00

pages

286-92

issue

1

eissn

1535-3893

issn

1535-3907

journal_volume

7

pub_type

杂志文章

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