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PaDuA: A Python Library for High-Throughput (Phospho)proteomics Data Analysis
Author(s) -
Anna Ressa,
Martin Fitzpatrick,
Henk van den Toorn,
Albert J. R. Heck,
Maarten Altelaar
Publication year - 2018
Publication title -
journal of proteome research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.644
H-Index - 161
eISSN - 1535-3907
pISSN - 1535-3893
DOI - 10.1021/acs.jproteome.8b00576
Subject(s) - python (programming language) , workflow , proteomics , computer science , quantitative proteomics , computational biology , data science , bioinformatics , database , chemistry , programming language , biology , biochemistry , gene
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.

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