OpenMS WebApps: Building User-Friendly Solutions for MS Analysis

IF 3.8 2区 生物学 Q1 BIOCHEMICAL RESEARCH METHODS Journal of Proteome Research Pub Date : 2025-01-29 DOI:10.1021/acs.jproteome.4c0087210.1021/acs.jproteome.4c00872
Tom David Müller, Arslan Siraj, Axel Walter, Jihyung Kim, Samuel Wein, Johannes von Kleist, Ayesha Feroz, Matteo Pilz, Kyowon Jeong, Justin Cyril Sing, Joshua Charkow, Hannes Luc Röst* and Timo Sachsenberg*, 
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引用次数: 0

Abstract

Liquid chromatography–mass spectrometry (LC-MS) is an indispensable analytical technique in proteomics, metabolomics, and other life sciences. While OpenMS provides advanced open-source software for MS data analysis, its complexity can be challenging for nonexperts. To address this, we have developed OpenMS WebApps, a framework for creating user-friendly MS web applications based on the Streamlit Python package. OpenMS WebApps simplifies MS data analysis through an intuitive graphical user interface, interactive result visualizations, and support for both local and online execution. Key features include workspace management, automatic generation of input widgets, and parallel execution of tools, resulting in high performance and ready-to-use solutions for online and local deployment. This framework benefits both researchers and developers: scientists can focus on their research without the burden of complex software setups, and developers can rapidly create and distribute custom WebApps with novel algorithms. Several applications built on the OpenMS WebApps template demonstrate its utility across diverse MS-related fields, enhancing the OpenMS ecosystem for developers and a wider range of users. Furthermore, it integrates seamlessly with third-party software, extending its benefits to developers beyond the OpenMS community.

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来源期刊
Journal of Proteome Research
Journal of Proteome Research 生物-生化研究方法
CiteScore
9.00
自引率
4.50%
发文量
251
审稿时长
3 months
期刊介绍: Journal of Proteome Research publishes content encompassing all aspects of global protein analysis and function, including the dynamic aspects of genomics, spatio-temporal proteomics, metabonomics and metabolomics, clinical and agricultural proteomics, as well as advances in methodology including bioinformatics. The theme and emphasis is on a multidisciplinary approach to the life sciences through the synergy between the different types of "omics".
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