This chapter describes how to improve quantitative accuracy and precision in shotgun proteomics by PQPQ (protein quantification by peptide quality control). The method is based on the assumption that the quantitative pattern of peptides derived from one protein will correlate over several samples. Dissonant patterns are assumed to arise either from mismatched peptides or due to the presence of different protein species. PQPQ identifies and excludes outliers and detects the existence of different protein species by correlation analysis. Alternative protein species can then be quantified separately. PQPQ can handle shotgun proteomics data from several MS instruments, data from different kinds of labeling, and label-free data.
We have previously shown that data processing by PQPQ improves the information output from shotgun proteomics by validating the algorithm on seven datasets related to different cancer studies (Forshed et al., Mol Cell Proteomics 10(10):M111.010264, 2011). Data from two labeling procedures and three different instrumental platforms was included in the evaluation. With this unique method using both peptide sequence data and quantitative data, we can improve the quantitative accuracy and precision on the protein level and detect different protein species (Forshed et al., Mol Cell Proteomics 10(10):M111.010264, 2011).
Protein quantification Data curation Quantitative accuracy Quantitative precision Quantitative shotgun proteomics
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