MaxQuant is a quantitative proteomics software package designed for analyzing large-scale mass-spectrometric data sets. It supports all main labeling techniques like SILAC, Di-methyl, TMT and iTRAQ as well as label-free quantification. Also measured spectra of various vendors - Thermo Fisher Scientific, Bruker Daltonics, AB Sciex and Agilent Technologies - can be processed using MaxQuant. The software is easy to handle and thus enables analyses of complex data sets on desktop machines by any researcher wishing to employ proteomics data.

The MaxQuant suite is a set of algorithms, which includes peak detection and scoring of peptides, it performs mass calibration and database searches for protein identification, it quantifies identified proteins, and provides summary statistics. First, MaxQuant corrects for systematic inaccuracies of measured peptide masses and corresponding retention times of extracted peptides from the raw data. Then for peptide identification, mass and intensity of the peptide peaks in a mass spectrometry (MS) spectra are detected and assembled into three-dimensional (3D) peak hills over the m/z retention time plane, which are filtered by applying graph theory algorithms to identify isotope patterns. High mass accuracy is achieved by weighted averaging and through mass recalibration by subtracting the determined systematic mass error from the measured mass of each MS isotope pattern. Peptide and fragment masses (in case of an MS/MS spectra) are searched in an organism specific sequence database, and are then scored by a probability-based approach termed peptide score. For limiting a certain number of peak matches by chance a target-decoy-based false discovery rate (FDR) approach is utilized. The FDR is determined using statistical methods that account for multiple hypotheses testing. Also the organism specific database search includes not only the target sequences, but also their reverse counterparts and contaminants, which helps to determine a statistical cutoff for acceptable spectral matches. The assembly of peptide hits into protein hits to identify proteins is the next step, in which each identified peptide of a protein contributes to the overall identification accuracy. Also, an FDR-controlled algorithm called matching between runs is incorporated, which enables the MS/MS free identification of MS features in the complete data set for each single measurement. This leads to an increase in the number of quantified proteins per sample.

The software is written in C# and freely available (see the download and installation guide). The download includes the Andromeda search engine as well as the Viewer module. The search engine contains a vast amount of preconfigured modifications, labels, proteases and search databases, but sometimes for specialized studies additional configurations are necessary. Therefore it is possible to configure the search engine according to special needs, if necessary. The Viewer module can be used for inspection of unprocessed and processed raw data. For statistical analyses of the MaxQuant output we offer the Perseus framework.

 

Publications:

Cox, J. and Mann, M. MaxQuant enables high peptide identification rates, individualized p.p.b.-range mass accuracies and proteome-wide protein quantification. Nat Biotechnol, 2008, 26, pp 1367-72.
Note that the paper has a large supplement containing in-depth descriptions of algorithms.

Cox, J and Mann, M., Computational principles of determining and improving mass precision and accuracy for proteome measurements in an Orbitrap. J Am Soc Mass Spectrom., 2009, 20, pp 1477-85.

Cox, J., Michalski, A., and Mann, M., Software Lock Mass by Two-Dimensional Minimization of Peptide Mass Errors. J Am Soc Mass Spectrom., 2011, 22, pp 1373–1380.

Schaab C., Geiger T., Stoehr G., Cox J., and Mann M. Analysis of High Accuracy, Quantitative Proteomics Data in the MaxQB Database. Mol Cell Proteomics, 2012, 11, M111.014068.

Cox J., Hein M. Y., Luber C. A., Paron I., Nagaraj N., and Mann M., Accurate Proteome-wide Label-free Quantification by Delayed Normalization and Maximal Peptide Ratio Extraction, Termed MaxLFQ. Mol Cell Proteomics, 2014, 13, pp 2513–2526.

Tyanova, S., Temu, T., Carlson, A., Sinitcyn, P., Mann, M. and Cox, J., Visualization of LC-MS/MS proteomics data in MaxQuant, Proteomics, 2015, 15, pp 1453–1456