PaDuA: A Python Library for High-Throughput (Phospho)proteomics Data Analysis

J Proteome Res. 2019 Feb 1;18(2):576-584. doi: 10.1021/acs.jproteome.8b00576. Epub 2018 Dec 21.


The increased speed and sensitivity in mass spectrometry-based proteomics has encouraged its use in biomedical research in recent years. Large-scale detection of proteins in cells, tissues, and whole organisms yields highly complex quantitative data, the analysis of which poses significant challenges. Standardized proteomic workflows are necessary to ensure automated, sharable, and reproducible proteomics analysis. Likewise, standardized data processing workflows are also essential for the overall reproducibility of results. To this purpose, we developed PaDuA, a Python package optimized for the processing and analysis of (phospho)proteomics data. PaDuA provides a collection of tools that can be used to build scripted workflows within Jupyter Notebooks to facilitate bioinformatics analysis by both end-users and developers.

Keywords: data analysis; high-throughput; proteomics; python library.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Computational Biology / methods
  • Data Analysis*
  • Databases, Genetic
  • Phosphoproteins / analysis*
  • Proteomics / methods*
  • Reference Standards
  • Software*
  • Workflow


  • Phosphoproteins