The Perseus platform is a comprehensive computational tool for analyzing (prote)omics data, offering a wide range of statistical and machine learning methods for high-dimensional data analysis. It supports the interpretation of protein quantification, interaction, and post-translational modification (PTM) data, with a user-friendly workflow environment that allows for complete documentation of computational methods. Perseus is built on a plugin architecture, enabling users to extend its functionality through custom plugins, which can be shared via a plugin store. The platform integrates various omics data types, including proteomics, transcriptomics, and metabolomics, and provides tools for data normalization, pattern recognition, time series analysis, and cross-omics comparisons. It also includes machine learning modules for classification and validation of patient groups, as well as for detecting predictive protein signatures. Perseus supports the analysis of large-scale datasets, including those involving multiple conditions, time series, and interaction networks. It offers advanced tools for post-translational modification analysis, including enrichment analysis of biological processes and pathways, and for interaction proteomics, enabling the identification of protein interactions and their functional implications. The platform also includes time series analysis tools for detecting periodic expression patterns and cross-omics data analysis for comparing proteomics data with other omics dimensions. Perseus is designed to be intuitive and accessible to biomedical domain experts, even those without formal computational training, and provides detailed documentation of all analysis steps and parameters. The platform is continuously developed and maintained, with a focus on improving its functionality and supporting future data types. Perseus is freely available and can be used for a wide range of applications in life sciences, including fundamental biology, drug discovery, and medical research. It aims to bridge the gap between computational and biological research, enabling more effective translation of omics technologies into biological and medical discoveries.The Perseus platform is a comprehensive computational tool for analyzing (prote)omics data, offering a wide range of statistical and machine learning methods for high-dimensional data analysis. It supports the interpretation of protein quantification, interaction, and post-translational modification (PTM) data, with a user-friendly workflow environment that allows for complete documentation of computational methods. Perseus is built on a plugin architecture, enabling users to extend its functionality through custom plugins, which can be shared via a plugin store. The platform integrates various omics data types, including proteomics, transcriptomics, and metabolomics, and provides tools for data normalization, pattern recognition, time series analysis, and cross-omics comparisons. It also includes machine learning modules for classification and validation of patient groups, as well as for detecting predictive protein signatures. Perseus supports the analysis of large-scale datasets, including those involving multiple conditions, time series, and interaction networks. It offers advanced tools for post-translational modification analysis, including enrichment analysis of biological processes and pathways, and for interaction proteomics, enabling the identification of protein interactions and their functional implications. The platform also includes time series analysis tools for detecting periodic expression patterns and cross-omics data analysis for comparing proteomics data with other omics dimensions. Perseus is designed to be intuitive and accessible to biomedical domain experts, even those without formal computational training, and provides detailed documentation of all analysis steps and parameters. The platform is continuously developed and maintained, with a focus on improving its functionality and supporting future data types. Perseus is freely available and can be used for a wide range of applications in life sciences, including fundamental biology, drug discovery, and medical research. It aims to bridge the gap between computational and biological research, enabling more effective translation of omics technologies into biological and medical discoveries.