PPNet

PPNet infers functional gene association networks from prokaryotic genome data to elucidate gene interactions associated with phenotypic traits.

Key Features:

• Phylogenetic Profiling: Combines genome information and phylogenetic profiles to derive gene association networks by analyzing binary similarity and distance measures within bacterial species.
• Binary Similarity Measures: Implements 81 binary similarity and dissimilarity measures that are systematically evaluated and categorized into four groups for network construction.
• Performance Metrics: Evaluates network predictive power using the area under the receiver operating characteristic (AUROC) and the area under the precision-recall (AUPR) curves.
• Experimental Validation: Enables validation of predicted associations using bacterial two-hybrid experiments.
• Uniqueness Assignment: Assigns strain uniqueness based on average nucleotide identity (ANI) and average nucleotide coverage (ANC).

Scientific Applications:

• Functional Network Inference: Interpreting complex gene interaction patterns across bacterial strains.
• Pathogenicity Analysis: Elucidating molecular mechanisms underlying pathogenicity.
• Control Strategy Support: Supporting formulation of targeted control measures against bacterial pathogens.

Methodology:

Collect genome-scale data from publicly available prokaryotic genomes; apply phylogenetic profiling using a comprehensive set of binary similarity and distance measures to derive functional association networks; evaluate networks using AUROC and AUPR metrics; validate selected associations with bacterial two-hybrid experiments.