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

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      Nature Biotechnology
      Springer Science and Business Media LLC

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          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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          Most cited references1

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          Monte Carlo Methods in Statistical Physics: Foundations and New Algorithms

          AD Sokal (1996)
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            Author and article information

            Journal
            Nature Biotechnology
            Nat Biotechnol
            Springer Science and Business Media LLC
            1087-0156
            1546-1696
            December 2008
            November 30 2008
            December 2008
            : 26
            : 12
            : 1367-1372
            Article
            10.1038/nbt.1511
            19029910
            2448fe31-e287-4c7a-959c-67d253915dc2
            © 2008

            http://www.springer.com/tdm

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