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Updated: May 31, 2025
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A Streamlined Approach for Mass Spectrometry-Based Proteomics Using Selected Tissue Regions
Published on: April 18, 2025
339
prolfquapp ─ A User-Friendly Command-Line Tool Simplifying Differential Expression Analysis in Quantitative
Witold E Wolski1,2, Jonas Grossmann1,2, Leonardo Schwarz1,2
1Functional Genomics Center Zurich (FGCZ) - University of Zurich/ETH Zurich, Winterthurerstrasse 190, CH-8057 Zurich, Switzerland.
Journal of Proteome Research
|January 24, 2025
Summary
Mass spectrometry is key for quantitative proteomics. The prolfquapp tool simplifies differential expression analysis (DEA) for complex experiments, offering accessible, integrated data processing and visualization for researchers.
Area of Science:
- Proteomics
- Bioinformatics
- Computational Biology
Background:
- Mass spectrometry is fundamental for quantitative proteomics, enabling relative protein quantification and differential expression analysis (DEA).
- Interactive DEA tools become impractical for complex experiments with numerous samples, groups, and identified proteins.
Purpose of the Study:
- To develop a command-line interface tool, prolfquapp, that simplifies DEA for large-scale quantitative proteomics.
- To enable nonprogrammers to perform DEA and integrate it into workflow management systems.
- To streamline data processing and result visualization for complex proteomics experiments.
Main Methods:
- Prolfquapp provides a command-line interface for DEA.
- It generates dynamic HTML reports for exploring differential expression results.
- It leverages advanced statistical models from the prolfqua R package.
Main Results:
- Prolfquapp simplifies DEA, making it accessible to nonprogrammers.
- Dynamic HTML reports facilitate the exploration of complex experimental results, including repeated measurements and multiple explanatory variables.
- Supports multiple output formats (XLSX, SummarizedExperiment, rank files) for further analysis.
Conclusions:
- Prolfquapp offers a user-friendly, integrated solution for large-scale quantitative proteomics.
- It combines efficient data processing with insightful, publication-ready outputs.
- Facilitates further interactive analysis using spreadsheet software, Shiny applications, or gene set enrichment analysis tools.
