Sung-Huan Yu1, Daniela Ferretti1, Julia P Schessner2, Jan Daniel Rudolph1,3, Georg H H Borner2, Jürgen Cox1,4. 1. Computational Systems Biochemistry Research Group, Max-Planck Institute of Biochemistry, Martinsried, Germany. 2. Systems Biology of Membrane Trafficking Research Group, Max-Planck Institute of Biochemistry, Martinsried, Germany. 3. Bosch Center for Artificial Intelligence, Robert-Bosch-Campus 1, Renningen, Germany. 4. Department of Biological and Medical Psychology, University of Bergen, Bergen, Norway.
Abstract
The Perseus software provides a comprehensive framework for the statistical analysis of large-scale quantitative proteomics data, also in combination with other omics dimensions. Rapid developments in proteomics technology and the ever-growing diversity of biological studies increasingly require the flexibility to incorporate computational methods designed by the user. Here, we present the new functionality of Perseus to integrate self-made plugins written in C#, R, or Python. The user-written codes will be fully integrated into the Perseus data analysis workflow as custom activities. This also makes language-specific R and Python libraries from CRAN (cran.r-project.org), Bioconductor (bioconductor.org), PyPI (pypi.org), and Anaconda (anaconda.org) accessible in Perseus. The different available approaches are explained in detail in this article. To facilitate the distribution of user-developed plugins among users, we have created a plugin repository for community sharing and filled it with the examples provided in this article and a collection of already existing and more extensive plugins.
The Perseus software provides a comprehensive framework for the statistical analysis of large-scale quantitative proteomics data, also in combination with other omics dimensions. Rapid developments in proteomics technology and the ever-growing diversity of biological studies increasingly require the flexibility to incorporate computational methods designed by the user. Here, we present the new functionality of Perseus to integrate self-made plugins written in C#, R, or Python. The user-written codes will be fully integrated into the Perseus data analysis workflow as custom activities. This also makes language-specific R and Python libraries from CRAN (cran.r-project.org), Bioconductor (bioconductor.org), PyPI (pypi.org), and Anaconda (anaconda.org) accessible in Perseus. The different available approaches are explained in detail in this article. To facilitate the distribution of user-developed plugins among users, we have created a plugin repository for community sharing and filled it with the examples provided in this article and a collection of already existing and more extensive plugins.
Authors: Brooke L Brauer; Kwame Wiredu; Sierra Mitchell; Greg B Moorhead; Scott A Gerber; Arminja N Kettenbach Journal: Nat Protoc Date: 2021-09-13 Impact factor: 13.491