ProtExA: A tool for post-processing proteomics data providing differential expression metrics, co-expression networks...

Area of Science:

• Bioinformatics
• Computational Biology
• Systems Biology

Background:

• Protein and gene expression datasets are crucial for understanding biological systems.
• Analyzing these large datasets often requires specialized bioinformatics expertise and computational resources.
• Existing tools may lack comprehensive post-processing workflows for integrated analysis.

Purpose of the Study:

• To introduce ProTExA, a novel web-tool designed for the post-processing analysis of protein and gene expression data.
• To provide a user-friendly platform for differential expression, co-expression network, and pathway analysis.
• To facilitate data interpretation for researchers lacking extensive bioinformatics expertise or local computational infrastructure.

Main Methods:

• ProTExA employs network-based bioinformatics approaches for data analysis.
• It integrates statistical analysis, filtering, and enrichment analysis to identify key biological features.
• The tool generates various network types (pathway-to-pathway, pathway-to-gene, co-expression) and applies clustering methodologies.

Main Results:

• ProTExA successfully performs differential expression analysis to highlight significant proteins and genes.
• It identifies top-scored pathways through enrichment analysis.
• The tool generates comprehensive protein-gene co-expression networks and identifies relevant sub-networks via clustering.

Conclusions:

• ProTExA offers a simplified yet informative workflow for protein and gene expression data analysis.
• It empowers researchers to explore and exploit complex biological datasets effectively.
• The web-tool supports collaborative research by enabling accessible and reproducible data analysis.