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

@article{citeulike:3703187, 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.}, author = {Cox, J\"{u}rgen and Mann, Matthias}, citeulike-article-id = {3703187}, citeulike-linkout-0 = {http://dx.doi.org/10.1038/nbt.1511}, citeulike-linkout-1 = {http://dx.doi.org/10.1038/nbt.1511}, citeulike-linkout-2 = {http://view.ncbi.nlm.nih.gov/pubmed/19029910}, citeulike-linkout-3 = {http://www.hubmed.org/display.cgi?uids=19029910}, day = {30}, doi = {10.1038/nbt.1511}, issn = {1546-1696}, journal = {Nature biotechnology}, keywords = {kinaxo, maxquant, proteomics, quantitative}, month = {December}, number = {12}, pages = {1367--1372}, posted-at = {2009-09-09 21:45:32}, priority = {2}, title = {MaxQuant enables high peptide identification rates, individualized p.p.b.-range mass accuracies and proteome-wide protein quantification.}, url = {http://dx.doi.org/10.1038/nbt.1511}, volume = {26}, year = {2008} }

TY - JOUR ID - citeulike:3703187 L3 - citeulike-article-id:3703187 N2 - 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. IS - 12 JF - Nature biotechnology SN - 1546-1696 EP - 1372 TI - MaxQuant enables high peptide identification rates, individualized p.p.b.-range mass accuracies and proteome-wide protein quantification. VL - 26 SP - 1367 KW - kinaxo KW - maxquant KW - proteomics KW - quantitative AU - Cox, Jürgen AU - Mann, Matthias PY - 2008/12/30/ UR - http://dx.doi.org/10.1038/nbt.1511 ER -