QuickProt: A bioinformatics and visualization tool for DIA and PRM mass spectrometry-based proteomics datasets.

Area of Science:

• Proteomics
• Biotechnology
• Computational Biology

Background:

• Mass spectrometry (MS)-based proteomics is crucial for identifying and quantifying biological samples.
• Existing software platforms handle raw data processing, but downstream analysis, quality control, and interpretation remain challenging.
• Integrated tools for comprehensive proteomics data analysis are lacking.

Purpose of the Study:

• To develop an integrated, user-friendly tool for analyzing data-independent acquisition (DIA) and parallel reaction monitoring (PRM) proteomics datasets.
• To provide open-source Python-based Google Colab notebooks for streamlined proteomics data analysis.
• To facilitate downstream analysis, including quality control, visualization, and interpretation of proteomics results.

Main Methods:

• Development of QuickProt, a series of Python-based Google Colab notebooks.
• Utilized notebooks for analyzing in-house DIA and stable isotope dilution (SID)-PRM MS proteomics datasets.
• Applied notebooks to a time-course study of human erythropoiesis.

Main Results:

• QuickProt successfully analyzed DIA and SID-PRM MS proteomics datasets.
• Generated annotated tables and publication-ready figures.
• Revealed dynamic proteome rearrangement during erythroid differentiation, with increased abundance of proteins in gene regulation, metabolic, and chromatin remodeling pathways.

Conclusions:

• QuickProt automates and streamlines DIA and PRM-MS proteomics data analysis.
• The tool enhances efficiency and reduces analysis time for researchers.
• QuickProt empowers users with limited coding expertise to perform complex proteomics data analysis.