A Bioconductor workflow for processing, evaluating, and interpreting expression proteomics data

Area of Science:

• Proteomics
• Bioinformatics
• Computational Biology

Background:

• Expression proteomics globally evaluates protein abundances.
• Differential expression analysis identifies protein changes after system perturbations.
• Quantitative mass spectrometry is key to modern proteomics.

Purpose of the Study:

• To provide a comprehensive, step-by-step workflow for quantitative proteomics data analysis.
• To guide users through processing, analysis, and interpretation using open-source R packages.
• To demonstrate the workflow with a practical example using HEK293 cell treatment data.

Main Methods:

• Utilized open-source R software packages from Bioconductor.
• Developed a workflow for quantitative mass spectrometry-based expression proteomics.
• Applied the workflow to both tandem mass tag (TMT) labeled cellular proteins and label-free quantitation (LFQ) of secreted proteins.
• Included data import, pre-processing, quality control, statistical differential expression analysis, and gene ontology enrichment analysis.

Main Results:

• The workflow details software infrastructure, data import, pre-processing, and quality control for TMT and LFQ datasets.
• Demonstrated statistical differential expression analysis and interpretation through gene ontology enrichment.
• Successfully applied the workflow to experimental data from HEK293 cells.

Conclusions:

• A comprehensive and accessible workflow for expression proteomics data analysis is presented.
• The workflow is a valuable resource for the proteomics community, especially beginners familiar with R.
• Enables users to make data-driven decisions in their proteomics analyses.