MaxQuant enables high peptide identification rates, individualized ppb-range mass accuracies and proteome-wide protein quantification

J Cox, M Mann - Nature biotechnology, 2008 - nature.com
Nature biotechnology, 2008nature.com
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 …
Abstract
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.
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