A main bottleneck in proteomics is the downstream biological analysis of highly multivariate quantitative protein abundance data generated using massspectrometry- based analysis. We developed the Perseus software platform (https://maxquant.net/perseus/) to support biological and biomedical researchers in interpreting protein quantification, interaction and post-translational modification data. Perseus contains a comprehensive portfolio of statistical tools for high-dimensional omics data analysis covering normalization, pattern recognition, time-series analysis, cross-omics comparisons and multiplehypothesis testing. A machine learning module supports the classification and validation of patient groups for diagnosis and prognosis, and it also detects predictive protein signatures. Central to Perseus is a user-friendly, interactive workflow environment that provides complete documentation of computational methods used in a publication. All activities in Perseus are realized as plugins, and users can extend the software by programming their own, which can be shared through a plugin store. We anticipate that Perseus’s arsenal of algorithms and its intuitive usability will empower interdisciplinary analysis of complex large data sets.

Publications:

Tyanova, S., Temu, T., Sinitcyn, P., Carlson, A., Hein, M., Geiger, T., Mann, M. and Cox, J. The Perseus computational platform for comprehensive analysis of (prote)omics data. Nature Methods, 2016.

Cox, J and Mann, M., 1D and 2D annotation enrichment: A statistical method integrating quantitative proteomics with complementary high-throughput data. BMC Bioinformatics, 2012, 13 Suppl 16:S12.