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Expanding the Perseus Software for Omics Data Analysis With Custom Plugins.

Sung-Huan Yu1, Daniela Ferretti1, Julia P Schessner2

  • 1Computational Systems Biochemistry Research Group, Max-Planck Institute of Biochemistry, Martinsried, Germany.

Current Protocols in Bioinformatics
|September 15, 2020
PubMed
Summary
This summary is machine-generated.

Perseus software now integrates custom plugins in C#, R, and Python, enhancing quantitative proteomics and multi-omics data analysis. This allows users to incorporate personalized computational methods and access diverse libraries for advanced biological studies.

Keywords:
MaxQuantPerseusomics data analysisplugin developmentquantitative proteomics

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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.