Expanding the Perseus Software for Omics Data Analysis With Custom Plugins

Area of Science:

• Bioinformatics
• Computational Biology
• Proteomics

Background:

• Quantitative proteomics generates large-scale data requiring flexible analysis tools.
• Existing software often lacks the adaptability for user-defined computational methods.
• Integrating diverse omics data necessitates extensible analytical frameworks.

Purpose of the Study:

• To introduce new functionality in Perseus for integrating user-created plugins.
• To enable seamless incorporation of custom C#, R, and Python scripts into the Perseus workflow.
• To expand the analytical capabilities of Perseus for complex biological data.

Main Methods:

• Development of a plugin architecture within Perseus for C#, R, and Python.
• Integration of user-written code as custom activities in the Perseus data analysis workflow.
• Facilitation of access to external R and Python libraries (CRAN, Bioconductor, PyPI, Anaconda).

Main Results:

• Successful integration of user-developed plugins into Perseus.
• Demonstration of custom activity creation for R, Python, and C#.
• Establishment of a plugin repository for community sharing and distribution.
• Examples provided for UMAP visualization using R and Python plugins.

Conclusions:

• Perseus now offers enhanced flexibility for statistical analysis of quantitative proteomics and multi-omics data.
• User-created plugins significantly broaden the scope of computational methods applicable within Perseus.
• The plugin system and repository foster community collaboration and advance data analysis in biological research.