A statistical framework for protein quantitation in bottom-up MS-based proteomics

Motivation: Quantitative mass spectrometry-based proteomics requires protein-level estimates and associated confidence measures. Challenges include the presence of low quality or incorrectly identified peptides and informative missingness. Furthermore, models are required for rolling peptide-level i...

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Published in:Bioinformatics 2009-08, Vol.25 (16), p.2028-2034
Main Authors: Karpievitch, Yuliya, Stanley, Jeff, Taverner, Thomas, Huang, Jianhua, Adkins, Joshua N., Ansong, Charles, Heffron, Fred, Metz, Thomas O., Qian, Wei-Jun, Yoon, Hyunjin, Smith, Richard D., Dabney, Alan R.
Format: Article
Language:English
Subjects:
Biological and medical sciences
Databases, Protein
Fundamental and applied biological sciences. Psychology
General aspects
Mass Spectrometry - methods
Mathematics in biology. Statistical analysis. Models. Metrology. Data processing in biology (general aspects)
Models, Statistical
Original Papers
Proteins - analysis
Proteome - analysis
Proteomics - methods
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cited_by cdi_FETCH-LOGICAL-c634t-85ecf7e151638138db20aeea2ae18269dcb6f121bd0a13e87c85a4ddeaf67ee83
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container_end_page 2034
container_issue 16
container_start_page 2028
container_title Bioinformatics
container_volume 25
creator Karpievitch, Yuliya
Stanley, Jeff
Taverner, Thomas
Huang, Jianhua
Adkins, Joshua N.
Ansong, Charles
Heffron, Fred
Metz, Thomas O.
Qian, Wei-Jun
Yoon, Hyunjin
Smith, Richard D.
Dabney, Alan R.
description Motivation: Quantitative mass spectrometry-based proteomics requires protein-level estimates and associated confidence measures. Challenges include the presence of low quality or incorrectly identified peptides and informative missingness. Furthermore, models are required for rolling peptide-level information up to the protein level. Results: We present a statistical model that carefully accounts for informative missingness in peak intensities and allows unbiased, model-based, protein-level estimation and inference. The model is applicable to both label-based and label-free quantitation experiments. We also provide automated, model-based, algorithms for filtering of proteins and peptides as well as imputation of missing values. Two LC/MS datasets are used to illustrate the methods. In simulation studies, our methods are shown to achieve substantially more discoveries than standard alternatives. Availability: The software has been made available in the open-source proteomics platform DAnTE (http://omics.pnl.gov/software/). Contact: adabney@stat.tamu.edu Supplementary information: Supplementary data are available at Bioinformatics online.
doi_str_mv 10.1093/bioinformatics/btp362
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subjects Biological and medical sciences
Databases, Protein
Fundamental and applied biological sciences. Psychology
General aspects
Mass Spectrometry - methods
Mathematics in biology. Statistical analysis. Models. Metrology. Data processing in biology (general aspects)
Models, Statistical
Original Papers
Proteins - analysis
Proteome - analysis
Proteomics - methods
title A statistical framework for protein quantitation in bottom-up MS-based proteomics
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