OpenMS WebApps: Building User-Friendly Solutions for MS Analysis

Area of Science:

• Life sciences
• Analytical chemistry
• Bioinformatics

Background:

• Liquid chromatography-mass spectrometry (LC-MS) is crucial for proteomics and metabolomics.
• OpenMS is a powerful but complex open-source software for MS data analysis.
• A need exists for more accessible MS data analysis tools for non-expert users.

Purpose of the Study:

• To develop a framework for creating user-friendly web applications for MS data analysis.
• To simplify the use of OpenMS functionalities through an intuitive interface.
• To facilitate the development and deployment of custom MS analysis tools.

Main Methods:

• Development of OpenMS WebApps using the Streamlit Python package.
• Implementation of features like workspace management, automatic widget generation, and parallel tool execution.
• Creation of interactive result visualizations and support for local/online execution.

Main Results:

• OpenMS WebApps provides an intuitive graphical user interface for MS data analysis.
• The framework enables high-performance, ready-to-use solutions for both local and online deployment.
• Several applications demonstrate the framework's utility across diverse MS-related fields.

Conclusions:

• OpenMS WebApps significantly lowers the barrier to entry for MS data analysis.
• The framework empowers researchers to focus on scientific discovery, not software complexity.
• It fosters rapid development and distribution of novel MS analysis tools, benefiting the wider scientific community.