PyGNA

PyGNA performs statistical network analysis of gene and protein interaction data derived from high-throughput functional genomics experiments to identify networks associated with complex diseases and phenotypes.

Key Features:

• Statistical Framework: Implements an integrated statistical framework to test network properties of single and multiple genesets under various interaction models.
• High-Performance Computing: Uses multi-core processing to generate calibrated null distributions for network tests on large datasets.
• Integration into Analysis Pipelines: Integrates functional genomics data with molecular interaction information to enable network-aware geneset analyses.
• Visualization and Reporting: Generates figures and reports that summarize network analysis results.

Scientific Applications:

• RNA sequencing and high-throughput genomics: Applies to RNA sequencing and other high-throughput functional genomics datasets for geneset network analysis.
• Disease and phenotype network identification: Identifies networks associated with complex diseases and phenotypes by combining geneset and interaction data.
• Large-scale omic analysis: Supports analysis of population-scale omic datasets for comprehensive network investigations.

Methodology:

Integrates high-throughput functional genomics data with gene and protein interaction information, tests network properties across single and multiple genesets under various interaction models, and generates calibrated null distributions using multi-core computation.