Efficient analysis of very large amounts of raw data for peptide identification and
protein quantification is a principal challenge in mass spectrometry (MS)-based proteomics.
Here we describe MaxQuant, an integrated suite of algorithms specifically developed
for high-resolution, quantitative MS data. Using correlation analysis and graph theory,
MaxQuant detects peaks, isotope clusters and stable amino acid isotope-labeled (SILAC)
peptide pairs as three-dimensional objects in m/z, elution time and signal intensity
space. By integrating multiple mass measurements and correcting for linear and nonlinear
mass offsets, we achieve mass accuracy in the p.p.b. range, a sixfold increase over
standard techniques. We increase the proportion of identified fragmentation spectra
to 73% for SILAC peptide pairs via unambiguous assignment of isotope and missed-cleavage
state and individual mass precision. MaxQuant automatically quantifies several hundred
thousand peptides per SILAC-proteome experiment and allows statistically robust identification
and quantification of >4,000 proteins in mammalian cell lysates.