pyOpenMS-viz: Streamlining Mass Spectrometry Data Visualization with pandas

Mass spectrometry data visualization is essential for a wide range of applications, such as validation of workflows and results, benchmarking new algorithms, and creating comprehensive quality control reports. Python offers a popular and powerful framework for analyzing and visualizing multidimensio...

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Published inJournal of proteome research Vol. 24; no. 4; pp. 2152 - 2158
Main Authors Sing, Justin Cyril, Charkow, Joshua, Walter, Axel, Gao, Mingxuan, Müller, Tom David, Bittremieux, Wout, Sachsenberg, Timo, Röst, Hannes Luc
Format Journal Article
LanguageEnglish
Published United States American Chemical Society 04.04.2025
Subjects
Algorithms
Data Visualization
mass spectrometry
Mass Spectrometry - methods
proteome
Proteomics - methods
Python
quality control
Software
mass-spectrometry
visualization
python
quality control
validation
Online AccessGet full text
ISSN1535-3893
1535-3907
1535-3907
DOI10.1021/acs.jproteome.4c00873

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Summary:Mass spectrometry data visualization is essential for a wide range of applications, such as validation of workflows and results, benchmarking new algorithms, and creating comprehensive quality control reports. Python offers a popular and powerful framework for analyzing and visualizing multidimensional data; however, generating commonly used mass spectrometry plots in Python can be cumbersome. Here we present pyOpenMS-viz, a versatile, unified framework for generating mass spectrometry plots. pyOpenMS-viz directly extends pandas DataFrame plotting for generating figures in a single line of code. This implementation enables easy integration across various Python-based mass spectrometry tools that already use pandas DataFrames to store MS data. pyOpenMS-viz is open-source under a BSD 3-Clause license and freely available at https://github.com/OpenMS/pyopenms_viz.
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ISSN:1535-3893
1535-3907
1535-3907
DOI:10.1021/acs.jproteome.4c00873