GWENA

GWENA performs integrated analysis of gene co-expression networks to identify modules, characterize their topology, detect hub genes, integrate gene set enrichment and phenotypic associations, and assess differential co-expression across conditions.

Key Features:

• Network construction and module characterization: Constructs gene co-expression networks and identifies and characterizes network modules from expression data.
• Gene set enrichment analysis: Integrates gene set enrichment analysis to annotate module biological functions.
• Phenotypic associations: Associates network modules with phenotypic data to link modules to traits or conditions.
• Hub gene detection: Detects hub genes within modules to prioritize centrally connected genes.
• Topological metric computation: Computes topological metrics and supports advanced topological analysis to study network structural properties.
• Module comparison across conditions: Compares network modules across different conditions (e.g., wild-type vs. mutant) to identify significant variations.
• Differential co-expression analysis: Performs differential co-expression analysis to identify changes in gene interactions across conditions or phenotypes.
• Support for enrichment databases: Supports additional enrichment databases to enhance biological interpretation of modules.
• Visualization tools: Provides visualization tools for graphical representation of networks and module components.

Scientific Applications:

• GTEx skeletal muscle analysis: Applied to skeletal muscle datasets from young and old patients in the GTEx study to prioritize genes with previously unknown roles in muscle development and growth.
• Age-related co-expression changes: Revealed age-related changes in co-expression, including loss of connectivity and reorganization of gene interactions associated with aging.

Methodology:

Constructs gene co-expression networks from expression data; identifies and characterizes modules using enrichment analysis, phenotypic associations, and hub gene detection; computes topological metrics to assess network structure; and performs differential co-expression analysis to detect condition-specific changes.