Using PSEA-Quant for Protein Set Enrichment Analysis of Quantitative Mass Spectrometry-Based Proteomics

Area of Science:

• Proteomics
• Bioinformatics
• Systems Biology

Background:

• Quantitative mass spectrometry-based proteomics generates large datasets.
• Identifying biological pathways and functions from these datasets is crucial.
• Existing tools may have limitations in analyzing reproducibly quantified proteins.

Purpose of the Study:

• To introduce PSEA-Quant, a novel tool for analyzing quantitative proteomics data.
• To enable the identification of significantly enriched annotations in high-abundance proteins.
• To provide a user-friendly protocol for PSEA-Quant usage and interpretation.

Main Methods:

• PSEA-Quant analyzes quantitative proteomics data.
• It identifies enrichment of annotations from Gene Ontology and Molecular Signature databases.
• The tool is compatible with label-free and isotopic labeling quantitative proteomics methods.

Main Results:

• PSEA-Quant successfully identifies significantly enriched annotations for reproducibly quantified proteins.
• The tool provides a web interface and a command-line option for accessibility.
• The protocol details parameter importance and troubleshooting for effective use.

Conclusions:

• PSEA-Quant is a versatile tool for the analysis of quantitative proteomics datasets.
• It facilitates the discovery of biological insights through annotation enrichment analysis.
• The tool supports various quantitative proteomics strategies and offers flexible access options.