A Correlation Algorithm for the Automated Quantitative Analysis of Shotgun Proteomics Data

Quantitative shotgun proteomic analyses are facilitated using chemical tags such as ICAT and metabolic labeling strategies with stable isotopes. The rapid high-throughput production of quantitative ”shotgun” proteomic data necessitates the development of software to automatically convert mass spectr...

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Bibliographic Details
Published in:Analytical chemistry (Washington) 2003-12, Vol.75 (24), p.6912-6921
Main Authors: MacCoss, Michael J, Wu, Christine C, Liu, Hongbin, Sadygov, Rovshan, Yates, John R
Format: Article
Language:English
Subjects:
Algorithms
Amino Acid Sequence
Analytical chemistry
Analytical, structural and metabolic biochemistry
Automation
Biological and medical sciences
Chemistry
Chromatographic methods and physical methods associated with chromatography
Correlation analysis
Databases as Topic
Exact sciences and technology
Fundamental and applied biological sciences. Psychology
General aspects, investigation methods
Mass Spectrometry - methods
Molecular Sequence Data
Peptides - analysis
Peptides - chemistry
Proteins
Proteins - analysis
Proteins - chemistry
Proteomics - methods
Saccharomyces cerevisiae
Saccharomyces cerevisiae - chemistry
Software
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Summary:Quantitative shotgun proteomic analyses are facilitated using chemical tags such as ICAT and metabolic labeling strategies with stable isotopes. The rapid high-throughput production of quantitative ”shotgun” proteomic data necessitates the development of software to automatically convert mass spectrometry-derived data of peptides into relative protein abundances. We describe a computer program called RelEx, which uses a least-squares regression for the calculation of the peptide ion current ratios from the mass spectrometry-derived ion chromatograms. RelEx is tolerant of poor signal-to-noise data and can automatically discard nonusable chromatograms and outlier ratios. We apply a simple correction for systematic errors that improves the accuracy of the quantitative measurement by 32 ± 4%. Our automated approach was validated using labeled mixtures composed of known molar ratios and demonstrated in a real sample by measuring the effect of osmotic stress on protein expression in Saccharomyces cerevisiae.
ISSN:0003-2700
1520-6882
DOI:10.1021/ac034790h