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

• Published in: Analytical chemistry (Washington) Vol. 75; no. 24; pp. 6912 - 6921
• Main Authors: MacCoss, Michael J, Wu, Christine C, Liu, Hongbin, Sadygov, Rovshan, Yates, John R
• Format: Journal Article
• Language: English
• Published: Washington, DC American Chemical Society 15.12.2003
• 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; Mass Spectrometry - methods; Molecular Sequence Data; Peptides; Peptides - analysis; Peptides - chemistry; Proteins; Proteins - analysis; Proteins - chemistry; Proteomics - methods; Saccharomyces cerevisiae; Saccharomyces cerevisiae - chemistry; Software; Stable isotopes; Peptides; Algorithm; Protein; Fungi; Ion chromatography; Computer program; Least squares method; Proteomics; Ascomycetes; Error correction; Automatic analysis; Saccharomyces cerevisiae; Thallophyta; Quantitative analysis
• ISSN: 0003-2700; 1520-6882
• DOI: 10.1021/ac034790h

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.