Simple, Fast, and Reliable Analysis of Label-Free Proteomics Data With the Proteomics Eye (ProtE)

Area of Science:

• Proteomics
• Bioinformatics
• Computational Biology

Background:

• Label-free mass spectrometry generates complex proteome tables requiring extensive post-quantification analysis.
• Current analysis often involves challenging programmatic pipelines, limiting accessibility for researchers.

Purpose of the Study:

• To introduce Proteomics Eye (ProtE), a user-friendly R package designed to streamline the analysis of label-free mass spectrometry data.
• To provide a single-function solution for data processing, quality testing, statistical analysis, and visualization of proteome tables.

Main Methods:

• Developed ProtE as a single-function R package compatible with data from DIA-NN, ProteomeDiscoverer, and MaxQuant.
• Integrated features for data processing, preparation, statistical testing (group-wide and pairwise comparisons), gene set enrichment analysis, and visualization.
• Implemented traditional statistical tests and linear models for differential expression analysis.

Main Results:

• ProtE successfully streamlines the analysis of large-scale label-free DDA and DIA datasets.
• The package offers comprehensive options for data quality assessment and biological interpretation.
• Facilitates both group-wide and pairwise statistical comparisons across multiple experimental groups.

Conclusions:

• Proteomics Eye (ProtE) enhances accessibility to advanced proteomic data analysis for both novice and experienced researchers.
• The R package simplifies complex workflows, enabling efficient interpretation of proteomic signals.
• ProtE supports robust statistical analysis and visualization, crucial for biological discovery in proteomics.