The Perseus software platform supports 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 multiple-hypothesis 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.
Perseus is freely available to commercial and academic users, just follow the download and installation guide.
If you use Perseus in your project, please cite:
The Perseus computational platform for comprehensive analysis of (prote)omics data Nat. Methods 2016.
Network analysis, co-expression and PluginInterop:
A network module for the Perseus software for computational proteomics facilitates proteome interaction graph analysis BioRxiv 2018.
Find a predictive protein signature using SVMs:
Proteomic maps of breast cancer subtypes Nat. Comm. 2016.
Perform 1D or 2D annotation enrichment:
1D and 2D annotation enrichment: a statistical method integrating quantitative proteomics with complementary high-throughput data BMC Bioinformatics 2012.
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