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

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 met

SUBMITTER: Karpievitch Y 

PROVIDER: S-EPMC2723007 | biostudies-literature | 2009 Aug

REPOSITORIES: biostudies-literature