A Statistical Method for Assessing Peptide Identification Confidence in Accurate Mass and Time Tag Proteomics

Current algorithms for quantifying peptide identification confidence in the accurate mass and time (AMT) tag approach assume that the AMT tags themselves have been correctly identified. However, there is uncertainty in the identification of AMT tags, because this is based on matching LC-MS/MS fragme...

Full description

Saved in:
Bibliographic Details
Published in:Analytical chemistry (Washington) 2011-08, Vol.83 (16), p.6135-6140
Main Authors: Stanley, Jeffrey R, Adkins, Joshua N, Slysz, Gordon W, Monroe, Matthew E, Purvine, Samuel O, Karpievitch, Yuliya V, Anderson, Gordon A, Smith, Richard D, Dabney, Alan R
Format: Article
Language:English
Subjects:
Algorithms
Analytical chemistry
Biological and medical sciences
Chemistry
Chromatographic methods and physical methods associated with chromatography
Chromatography, Liquid - standards
Chromatography, Liquid - statistics & numerical data
Databases, Protein
Diverse techniques
Exact sciences and technology
Fundamental and applied biological sciences. Psychology
Models, Statistical
Molecular and cellular biology
Other chromatographic methods
Peptides
Peptides - analysis
Peptides - chemistry
Probability
Proteomics
Proteomics - methods
Proteomics - statistics & numerical data
Software
Spectrometric and optical methods
Statistical analysis
Tandem Mass Spectrometry - standards
Tandem Mass Spectrometry - statistics & numerical data
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Current algorithms for quantifying peptide identification confidence in the accurate mass and time (AMT) tag approach assume that the AMT tags themselves have been correctly identified. However, there is uncertainty in the identification of AMT tags, because this is based on matching LC-MS/MS fragmentation spectra to peptide sequences. In this paper, we incorporate confidence measures for the AMT tag identifications into the calculation of probabilities for correct matches to an AMT tag database, resulting in a more accurate overall measure of identification confidence for the AMT tag approach. The method is referenced as Statistical Tools for AMT Tag Confidence (STAC). STAC additionally provides a uniqueness probability (UP) to help distinguish between multiple matches to an AMT tag and a method to calculate an overall false discovery rate (FDR). STAC is freely available for download, as both a command line and a Windows graphical application.
ISSN:0003-2700
1520-6882
DOI:10.1021/ac2009806