ProCoNA

ProCoNA constructs protein co-expression networks from peptide-level quantitative proteomics data by adapting weighted gene co-expression network analysis (WGCNA) principles to model complex biological systems and support biomarker and disease-etiology investigations.

Key Features:

• WGCNA adaptation: Applies principles of weighted gene co-expression network analysis specifically adapted for peptide-level proteomic data.
• Network construction: Constructs approximately scale-free peptide co-expression networks and identifies statistically significant modules from quantitative proteomics experiments, including mouse lung and human plasma datasets.
• Topological and abundance analysis: Evaluates topological overlap and abundance concordance among peptides originating from the same protein to inform protein-level inference.
• Eigenpeptides: Identifies eigenpeptides as module representatives and assesses their correlations with biological phenotypes.
• Functional enrichment: Performs enrichment analysis to link network modules to gene ontology terms and protein-protein interactions.
• Degenerate mapping resolution and applications: Provides de novo approaches for resolving degenerate peptide-to-protein mappings and supports quality control and biomarker discovery.

Scientific Applications:

• Systems biology: Models complex biological systems at the peptide level to reveal coordinated protein behavior.
• Biomarker discovery: Identifies module-associated eigenpeptides and modules as candidate biomarkers correlated with phenotypes.
• Quality control: Uses peptide concordance and network topology to assess data consistency and reliability.
• Peptide-to-protein mapping: Aids in resolving degenerate peptide-protein mappings to improve protein abundance inference.
• Disease mechanism elucidation: Correlates modules with biological phenotypes and functional annotations to support investigation of disease etiology.

Methodology:

Adapts WGCNA for proteomics to construct approximately scale-free peptide co-expression networks, detects network modules, computes topological overlap and peptide abundance concordance within proteins, derives eigenpeptides for modules and correlates them with phenotypes, and performs functional enrichment analysis linking modules to gene ontology terms and protein-protein interactions.