VAN: an R package for identifying biologically perturbed networks via differential variability analysis

Area of Science:

• Bioinformatics
• Systems Biology
• Computational Biology

Background:

• Complex diseases involve network-level perturbations, not just individual gene changes.
• Existing methods for gene expression analysis are unsuitable for network-level studies.
• Bioinformatics approaches integrating transcriptomics and interaction networks are crucial for identifying perturbed networks.

Purpose of the Study:

• To present the Variability Analysis in Networks (VAN) R package.
• To streamline the bioinformatics analysis of molecular interaction networks and transcriptomics data.
• To facilitate the identification of disease-associated network modules.

Main Methods:

• VAN identifies network hubs and extracts modules (hub and interaction partners).
• It uses functions like identifySignificantHubs and summarizeHubData to detect dysregulated modules.
• The package includes tools for ID mapping, microRNA-gene network generation, and data visualization in R and Cytoscape.

Main Results:

• VAN identifies network-level perturbations across biological states.
• It can convert protein identifiers to gene identifiers and generate microRNA-gene networks.
• The software aids in identifying cancer-associated hubs and visualizing network module changes.

Conclusions:

• VAN offers a user-friendly platform for integrative analysis of omics data.
• It enables the identification of disease-associated network modules.
• The approach is relevant for understanding phenotypic changes driven by network regulation shifts.