FGNet

FGNet constructs gene networks from functional enrichment analysis results in R/Bioconductor to reveal shared functional annotations, gene modules, and multifunctional genes.

Key Features:

• Network Generation: Transforms enriched gene sets from FEA into networks that expose gene modules and genes associated with multiple biological roles.
• Annotation Clustering: Clusters functional annotations and links genes through shared annotations to reveal functional relationships.
• Similarity Analysis: Computes similarities or distances among gene groups and produces a distance heatmap representing these relationships.
• Bipartite Network Visualization: Constructs bipartite networks connecting genes and functional terms to represent functional overlaps.
• Integration with External Tools: Interfaces with DAVID, GeneTerm Linker, TopGO, and GAGE to import or query functional enrichment analysis results.

Scientific Applications:

• Functional enrichment interpretation: Clarifies and contextualizes FEA results by mapping enriched terms onto gene networks.
• Genomics: Identifies and characterizes functional gene modules within genomic datasets.
• Systems biology: Maps functional interactions and modules for systems-level analysis of biological processes.
• Personalized medicine: Highlights multifunctional genes and overlapping pathways that may inform disease mechanisms or individual-specific profiles.

Methodology:

Transforms functional enrichment analysis results into network representations, clusters annotations, establishes links between genes based on shared functional attributes, computes pairwise group similarities to produce a distance heatmap, and constructs bipartite gene–term networks; interfaces with DAVID, GeneTerm Linker, TopGO, and GAGE for FEA queries.