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Bioinformatics Advance Access originally published online on June 17, 2009
Bioinformatics 2009 25(16):2028-2034; doi:10.1093/bioinformatics/btp362
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© The Author 2009. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

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

Yuliya Karpievitch 1, Jeff Stanley 1, Thomas Taverner 2, Jianhua Huang 1, Joshua N. Adkins 2, Charles Ansong 2, Fred Heffron 3, Thomas O. Metz 2, Wei-Jun Qian 2, Hyunjin Yoon 3, Richard D. Smith 2 and Alan R. Dabney 1,*

1Department of Statistics, Texas A&M University, 3143 TAMU, College Station, TX 77843, 2Pacific Northwest National Laboratory, P.O. Box 999, Richland, WA 99352 and 3Molecular Microbiology and Immunology, Oregon Health and Science University, Mail Code L220, Portland, OR 97201, USA

*To whom correspondence should be addressed.


  Abstract

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{at}stat.tamu.edu

Supplementary information: Supplementary data are available at Bioinformatics online.

Associate Editor: Trey Ideker

Received on December 29, 2008; revised on May 31, 2009; accepted on June 9, 2009

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