MS-PyCloud: A Cloud Computing-Based Pipeline for Proteomic and Glycoproteomic Data Analyses.

Area of Science:

• Proteomics and Glycoproteomics
• Computational Biology
• Bioinformatics

Background:

• Mass spectrometry-based glycoproteomic technologies enable large-scale protein and glycosylation studies.
• Data storage, management, and analysis pose significant challenges due to software costs and processing times.
• Explorative and discovery analyses require flexible data processing settings.

Purpose of the Study:

• To develop an open-source, cloud computing-based pipeline for proteomic and glycoproteomic data analysis.
• To address the computational challenges faced by individual laboratories in handling large-scale datasets.
• To ensure transparency and reproducibility in data analysis.

Main Methods:

• Developed MS-PyCloud, an open-source pipeline with a graphical user interface (GUI).
• Integrated components for data validation, MS/MS database search, FDR estimation, protein inference, and quantitation.
• Utilized Amazon Web Services (AWS) for scalable cloud computing infrastructure.

Main Results:

• MS-PyCloud facilitates analysis of global protein levels and specific glycopeptides.
• The pipeline supports analysis of other post-translational modifications like phosphorylation, acetylation, and ubiquitination.
• Demonstrated effectiveness and high performance on large-scale LC-MS/MS datasets.

Conclusions:

• MS-PyCloud provides an efficient, scalable, and reproducible solution for proteomic and glycoproteomic data analysis.
• The open-source nature and cloud-based architecture lower barriers for researchers.
• The pipeline enhances the ability to perform explorative and discovery-driven analyses.