MaxQuant and MSstats in Galaxy Enable Reproducible Cloud-Based Analysis of Quantitative Proteomics Experiments for...

Area of Science:

• Proteomics
• Bioinformatics
• Computational Biology

Background:

• Quantitative mass spectrometry-based proteomics is a high-throughput method for protein identification and quantification.
• MaxQuant and MSstats are widely used tools for analyzing differential protein abundance in complex samples.

Purpose of the Study:

• To integrate MaxQuant, PTXQC, MSstats, and MSstatsTMT into the open-source Galaxy framework.
• To enable accessible and reproducible quantitative proteomics analyses in a cloud environment.

Main Methods:

• Integration of MaxQuant (including TMTpro 16/18plex), PTXQC, MSstats, and MSstatsTMT into the Galaxy framework.
• Web-based analysis of label-free and isobaric labeling proteomics experiments using Galaxy's graphical user interface on public clouds.
• Leveraging Galaxy's workflow capabilities for standardized, shareable, and reproducible analyses.

Main Results:

• Enabled web-based, cloud-accessible quantitative proteomics analysis.
• Facilitated integration with existing Galaxy tools and creation of standardized workflows.
• Ensured reproducibility and transparency through Galaxy's metadata tracking and history sharing features.

Conclusions:

• The integration provides a foundation for high-throughput proteomics data science accessible to a wider audience.
• Galaxy framework enhances the usability and reproducibility of MaxQuant and MSstats for proteomics research.
• Accessible cloud infrastructure and training materials increase the adoption of advanced proteomics analysis techniques.