MaxQuant enables high peptide identification rates, individualized p.p.b. range mass accuracies and proteome wide protein quantification
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.
Nature Biotechnology