Democratizing data-independent acquisition proteomics analysis on public cloud infrastructures via the Galaxy...

Area of Science:

• Proteomics
• Mass Spectrometry
• Bioinformatics

Background:

• Data-independent acquisition (DIA) offers deep proteomic insights but faces analysis challenges.
• Complex DIA data requires specialized software, programming skills, and significant computing resources.
• Current open-source tools are fragmented, limiting usability and reproducibility.

Purpose of the Study:

• To simplify and enhance the reproducibility of DIA data analysis.
• To integrate a suite of open-source DIA tools within a user-friendly platform.
• To improve accessibility of advanced proteomic data processing for the research community.

Main Methods:

• Integrated OpenSwath, PyProphet, diapysef, and swath2stats into the Galaxy framework.
• Developed functional Galaxy pipelines with a web-based graphical user interface for DIA processing.
• Provided extensive training materials to support user adoption.

Main Results:

• Demonstrated a unified, reproducible, and version-controlled DIA data processing workflow.
• Enabled seamless sharing of workflows, configurations, raw data, and results.
• Validated the pipeline's usability with a spike-in case study.

Conclusions:

• The Galaxy-integrated DIA suite empowers researchers with reproducible and transparent data analysis.
• User-friendly web interface and comprehensive training lower barriers to advanced proteomics.
• Facilitates broader community engagement in complex mass spectrometry-based proteomic studies.