RevEcoR

RevEcoR infers ecological interactions and reconstructs metabolic networks from microbial genomes and high-throughput metagenomic sequencing data to characterize microbe-microbe and microbe-environment interfaces.

Key Features:

• Graph-theoretical algorithm: Employs a graph-theory-based algorithm to analyze metabolic networks across species within microbial communities.
• Microbe–microbe interaction inference: Determines potential interactions among microbial species by examining metabolic interfaces and network connectivity.
• Metabolic network reconstruction: Reconstructs and examines metabolic networks to predict how species interact with each other and their environment.
• High-throughput metagenomic data integration: Processes large-scale ecological data derived from high-throughput metagenomic sequencing for community-level analysis.
• Reverse ecology framework: Applies a reverse ecology approach to infer ecological relationships from genomic data without prior assumptions about species roles.

Scientific Applications:

• Microbiome ecology: Characterizes ecological interactions and metabolic interfaces within host-associated and environmental microbiomes.
• Environmental microbiology: Maps microbial metabolic interactions relevant to ecosystem processes and environmental adaptation.
• Community function and diversity analysis: Links reconstructed metabolic networks to microbial diversity, function, and potential resource competition or cooperation.
• Applied ecology and biotechnology: Informs studies in biotechnology and environmental conservation by predicting metabolic interfaces and interaction networks within communities.

Methodology:

Uses a reverse ecology approach with graph-theory-based analysis to construct and examine metabolic networks from genomic and high-throughput metagenomic sequencing data to predict ecological interfaces and species interactions.