AMON

AMON annotates origins of small-molecule metabolites in host-associated samples by integrating microbiome and metabolome data to predict whether compounds are produced by microbial communities or the host.

Key Features:

• Metabolite Origin Prediction: Predicts putative origins of metabolites (microbial versus host) and can perform predictions with or without accompanying metabolomics data.
• KEGG Integration: Leverages KEGG (Kyoto Encyclopedia of Genes and Genomes) databases and KO annotations and includes scripts such as extract_ko_genome_from_organism.py for extracting KO data from KEGG organism files.
• Pathway Enrichment Analysis: Evaluates pathway enrichment by comparing host versus microbial metabolites to identify differential metabolic contributions.
• Visualization of Pathways: Maps compounds onto KEGG pathways to illustrate potential interactions between host and microbial enzymes.

Scientific Applications:

• Untargeted metabolomics of host-associated samples: Annotates metabolite origins to support interpretation of untargeted metabolomics data in host-associated studies.
• Host–microbe metabolic interaction studies: Identifies microbial versus host contributions to small-molecule pools to study microbial influence on host metabolism.
• Disease, diet, and exposure research: Supports investigations of how microbial metabolism relates to disease phenotypes, dietary influences, and environmental exposures.

Methodology:

Extracts KO annotations from KEGG organism files (using scripts such as extract_ko_genome_from_organism.py) to generate lists of KOs, uses KO–KEGG mappings to predict potential metabolite production, compares predicted producers against host or other microbial gene sets and KEGG metabolites to evaluate pathway enrichment between host and microbes, and maps compounds onto KEGG pathways for visualization.