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Bioinformatics. 2009 Aug 15;25(16):2028-34. doi: 10.1093/bioinformatics/btp362. Epub 2009 Jun 17.

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

Author information

1
Department of Statistics, Texas A&M University, 3143 TAMU, College Station, TX 77843, USA.

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/).

PMID:
19535538
PMCID:
PMC2723007
DOI:
10.1093/bioinformatics/btp362
[Indexed for MEDLINE]
Free PMC Article

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