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 ( http://www.perseus-framework.org) 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.
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.