Mass Spectrometry-based Proteomics

We develop algorithms and tools for analyzing the vast amounts of spectral data that are produced in modern proteomics experiments. The MaxQuant software that we develop is one of the most widely used platforms in computational proteomics. Many labs world-wide benefit from its precise protein and peptide quantification algorithms. It is freely available for academic and non-academic researchers at Link

J. Cox joined the field of proteomics in 2006 in order to find solutions to the computational problems induced by the increasing volumes of proteomics raw data produced by high-resolution mass spectrometers. Semi-manual data analysis, which was common at the time, became infeasible, and a reliable method for automated identification and quantification of peptides and proteins was urgently needed. It was not long before a solution for SILAC data was found in form of the first MaxQuant prototype. Soon afterwards MaxQuant was made available to everyone interested. 

Today MaxQuant supports nearly all types of shotgun proteomics data, including MS1-level based labeling data, like SILAC and di-methyl, isobaric labeling data, like iTRAQ and TMT, and label-free data. Supported mass spectrometry vendors include Thermo Fisher Scientific, Bruker Daltonics and AB Sciex. The peptide search engine Andromeda, which uses a probability-based scoring of peptide-spectrum matches, is integrated into MaxQuant. Sophisticated mass recalibration algorithms dramatically improve the accuracy of the mass measurements of peptides and contribute to their reliable identification.

We collaborate with the group of Prof. Mann on several projects that have the goal of improving proteomics technologies, and we provide computational tools to enable the analysis of data acquired on new and emerging hardware platforms. Furthermore we participate in efforts to streamline proteomics workflows with the goal of bringing them to the clinic.


Selected publications:

Cox, J. and Mann, M. (2008). MaxQuant enables high peptide identification rates, individualized p.p.b.-range mass accuracies and proteome-wide protein quantification. Nature Biotech 26, 1367-72.

Cox, J., Hein, M.Y., Luber, C.A., Paron, I., Nagaraj, N. and Mann, M. (2014) MaxLFQ allows accurate proteome-wide label-free quantification by delayed normalization and maximal peptide ratio estimation. Mol Cell Proteomics.

Cox, J., Neuhauser, N., Michalski, A., Scheltema, R.A., Olsen, J.V. and Mann, M. (2011) Andromeda – a peptide search engine integrated into the MaxQuant environment. J Proteome Res 10, 1794-1805.

Cox, J., Michalski, A. and Mann, M. (2011). Software lock mass by two-dimensional minimization of peptide mass errors. J Am Soc Mass Spectrom 22, 1373-80.

Cox, J. and Mann, M. (2011). Quantitative, high resolution proteomics for data-driven systems biology. Annual Review of Biochemistry 80, 273-299.

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