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Title: A Comparative Analysis of Computational Approaches to Relative Protein Quantification Using Peptide Peak Intensities in Label-free LC-MS Proteomics Experiments

Abstract

Liquid chromatography coupled with mass spectrometry (LC-MS) is widely used to identify and quantify peptides in complex biological samples. In particular, label-free shotgun proteomics is highly effective for the identification of peptides and subsequently obtaining a global protein profile of a sample. As a result, this approach is widely used for discovery studies. Typically, the objective of these discovery studies is to identify proteins that are affected by some condition of interest (e.g. disease, exposure). However, for complex biological samples, label-free LC-MS proteomics experiments measure peptides and do not directly yield protein quantities. Thus, protein quantification must be inferred from one or more measured peptides. In recent years, many computational approaches to relative protein quantification of label-free LC-MS data have been published. In this review, we examine the most commonly employed quantification approaches to relative protein abundance from peak intensity values, evaluate their individual merits, and discuss challenges in the use of the various computational approaches.

Authors:
; ; ; ; ; ; ; ; ; ;
Publication Date:
Research Org.:
Pacific Northwest National Laboratory (PNNL), Richland, WA (US), Environmental Molecular Sciences Laboratory (EMSL)
Sponsoring Org.:
USDOE
OSTI Identifier:
1072870
Report Number(s):
PNNL-SA-88866
16303; 16303a
DOE Contract Number:  
AC05-76RL01830
Resource Type:
Journal Article
Resource Relation:
Journal Name: Proteomics, 13(3-4):493-503
Country of Publication:
United States
Language:
English
Subject:
Label-free; Peak intensity; Protein quantification; Relative; Environmental Molecular Sciences Laboratory

Citation Formats

Matzke, Melissa M., Brown, Joseph N., Gritsenko, Marina A., Metz, Thomas O., Pounds, Joel G., Rodland, Karin D., Shukla, Anil K., Smith, Richard D., Waters, Katrina M., McDermott, Jason E., and Webb-Robertson, Bobbie-Jo M.. A Comparative Analysis of Computational Approaches to Relative Protein Quantification Using Peptide Peak Intensities in Label-free LC-MS Proteomics Experiments. United States: N. p., 2013. Web. doi:10.1002/pmic.201200269.
Matzke, Melissa M., Brown, Joseph N., Gritsenko, Marina A., Metz, Thomas O., Pounds, Joel G., Rodland, Karin D., Shukla, Anil K., Smith, Richard D., Waters, Katrina M., McDermott, Jason E., & Webb-Robertson, Bobbie-Jo M.. A Comparative Analysis of Computational Approaches to Relative Protein Quantification Using Peptide Peak Intensities in Label-free LC-MS Proteomics Experiments. United States. doi:10.1002/pmic.201200269.
Matzke, Melissa M., Brown, Joseph N., Gritsenko, Marina A., Metz, Thomas O., Pounds, Joel G., Rodland, Karin D., Shukla, Anil K., Smith, Richard D., Waters, Katrina M., McDermott, Jason E., and Webb-Robertson, Bobbie-Jo M.. Fri . "A Comparative Analysis of Computational Approaches to Relative Protein Quantification Using Peptide Peak Intensities in Label-free LC-MS Proteomics Experiments". United States. doi:10.1002/pmic.201200269.
@article{osti_1072870,
title = {A Comparative Analysis of Computational Approaches to Relative Protein Quantification Using Peptide Peak Intensities in Label-free LC-MS Proteomics Experiments},
author = {Matzke, Melissa M. and Brown, Joseph N. and Gritsenko, Marina A. and Metz, Thomas O. and Pounds, Joel G. and Rodland, Karin D. and Shukla, Anil K. and Smith, Richard D. and Waters, Katrina M. and McDermott, Jason E. and Webb-Robertson, Bobbie-Jo M.},
abstractNote = {Liquid chromatography coupled with mass spectrometry (LC-MS) is widely used to identify and quantify peptides in complex biological samples. In particular, label-free shotgun proteomics is highly effective for the identification of peptides and subsequently obtaining a global protein profile of a sample. As a result, this approach is widely used for discovery studies. Typically, the objective of these discovery studies is to identify proteins that are affected by some condition of interest (e.g. disease, exposure). However, for complex biological samples, label-free LC-MS proteomics experiments measure peptides and do not directly yield protein quantities. Thus, protein quantification must be inferred from one or more measured peptides. In recent years, many computational approaches to relative protein quantification of label-free LC-MS data have been published. In this review, we examine the most commonly employed quantification approaches to relative protein abundance from peak intensity values, evaluate their individual merits, and discuss challenges in the use of the various computational approaches.},
doi = {10.1002/pmic.201200269},
journal = {Proteomics, 13(3-4):493-503},
number = ,
volume = ,
place = {United States},
year = {Fri Feb 01 00:00:00 EST 2013},
month = {Fri Feb 01 00:00:00 EST 2013}
}