PhenoGeneRanker

PhenoGeneRanker implements Random Walk with Restart on Multiplex Heterogeneous Networks (RWR-MH) to prioritize candidate genes and diseases by propagating signals on integrated multi-layer gene and disease networks and thereby uncover genotype-phenotype relationships.

Key Features:

• Network propagation (RWR-MH): Uses Random Walk with Restart on Multiplex Heterogeneous Networks to diffuse phenotype signals across gene and disease layers.
• Multi-layer network integration: Integrates multi-layer gene and disease networks to represent diverse biological interactions and relationships across data types.
• Empirical p-value calculation: Computes empirical p-values for gene rankings via random stratified sampling based on node connectivity degree to assess statistical robustness and control for degree bias.
• Application to multi-omics datasets: Applied to multi-omics data, including analyses of cold tolerance in rice, to prioritize genes associated with specific phenotypes.
• Enrichment validation: Validates prioritized genes by Gene Ontology enrichment, with top-ranked genes enriched for phenotype-specific GO terms and lower-ranked genes associated with general GO terms.

Scientific Applications:

• Computational genomics: Prioritizes candidate genes and diseases from large-scale genomics and multi-omics datasets using network-based propagation.
• Systems biology: Identifies functionally coherent gene sets and disease associations within integrated interaction networks.
• Personalized medicine: Supports linking genetic variants to phenotypes for potential translational and clinical research applications.
• Crop improvement: Prioritizes genes underlying agronomic traits such as cold tolerance in rice for functional follow-up.
• Functional annotation of genetic variants: Aids interpretation of variant effects by ranking genes connected to phenotype-relevant network neighborhoods.

Methodology:

Integrates multi-layer gene and disease networks and implements Random Walk with Restart on Multiplex Heterogeneous Networks (RWR-MH); calculates empirical p-values via random stratified sampling by network degree; and validates rankings using Gene Ontology enrichment analysis on multi-omics datasets.