AI Article Synopsis
- - The tool, called protein quantification by peptide quality control (PQPQ), aims to enhance the accuracy and precision of protein measurements in mass spectrometry through shotgun proteomics by analyzing peptide patterns across multiple samples.
- - PQPQ identifies and excludes outlier peptides and distinguishes between different protein species by performing correlation analysis on peptide patterns.
- - Validation using seven cancer study data sets demonstrates that PQPQ significantly improves data processing and output from shotgun proteomics, even across various labeling techniques and instrumental platforms.
Article Abstract
We present a tool to improve quantitative accuracy and precision in mass spectrometry based on shotgun proteomics: protein quantification by peptide quality control, PQPQ. The method is based on the assumption that the quantitative pattern of peptides derived from one protein will correlate over several samples. Dissonant patterns arise either from outlier peptides or because of the presence of different protein species. By correlation analysis, protein quantification by peptide quality control identifies and excludes outliers and detects the existence of different protein species. Alternative protein species are then quantified separately. By validating the algorithm on seven data sets related to different cancer studies we show that data processing by protein quantification by peptide quality control improves the information output from shotgun proteomics. 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.
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| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3205873 | PMC |
| http://dx.doi.org/10.1074/mcp.M111.010264 | DOI Listing |
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