ProteoGyver: a fast, user-friendly tool for routine QC and analysis of MS-based proteomics data

Area of Science:

• Proteomics
• Bioinformatics
• Computational Biology

Background:

• Mass spectrometry-based proteomics generates vast datasets.
• Current software for quality control (QC) and preliminary analysis often requires specialized expertise.
• This limits the routine adoption of essential QC practices in proteomics research.

Purpose of the Study:

• To develop an accessible and lightweight software solution for rapid QC and preliminary analysis of proteomics data.
• To lower the barrier for routine QC practices in proteomics.
• To provide streamlined workflows for whole-proteome and interactomics datasets.

Main Methods:

• Development of ProteoGyver (PG), a software tool for automated QC metrics and graphical reporting.
• Integration of tools like MS Inspector for chromatogram inspection and Colocalizer for microscopy data.
• Deployment options include Docker containers or standalone Python installations for ease of use.

Main Results:

• ProteoGyver (PG) provides automated QC metrics and intuitive graphical reports for proteomics data.
• Streamlined workflows facilitate preliminary analysis of whole-proteome and interactomics datasets.
• Additional tools enhance data inspection capabilities.

Conclusions:

• ProteoGyver (PG) significantly lowers the barrier to regular QC practices in mass spectrometry-based proteomics.
• The software offers an accessible and efficient solution for preliminary data analysis.
• PG is open-source and freely available, promoting wider adoption in the research community.