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 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.