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Bioinformatics. 2008 Jan 15;24(2):202-8. Epub 2007 Nov 17.

A hierarchical statistical model to assess the confidence of peptides and proteins inferred from tandem mass spectrometry.

Author information

1
Division of Biostatistics, Department of Medicine, Indiana University School of Medicine, Indianapolis, IN 46202, USA. chashen@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.

PMID:
18024968
DOI:
10.1093/bioinformatics/btm555
[Indexed for MEDLINE]

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