KOMODO

KOMODO detects significantly enriched or depleted KEGG Orthology (KO) groups between a test taxon and a background taxon within biochemical pathways to identify taxon-specific genomic representations.

Key Features:

• KO-group enrichment/depletion detection: Detects significantly enriched or depleted KEGG Orthology (KO) groups between a test taxon and a background taxon.
• Pathway-context analysis: Performs comparisons within specific biochemical pathways to localize differential representation of genomic elements.
• Monophyletic genome analysis: Systematically investigates monophyletic genomes to reveal taxon-level enrichments of homologous genes.
• Large gene-list analysis: Analyzes large gene lists from 'omics' datasets such as transcriptomics and proteomics to identify patterns of gene representation across taxa.
• Metabolic trait identification: Identifies enzymatic activities and traits biased toward specific taxa, exemplified by lactate production in Lactobacillales and complete pyruvate oxidation in Enterobacteriaceae.
• Taxonomic distribution analysis: Detects differences in frequencies of shared genomic elements among taxa to inform evolutionary and functional genomics.

Scientific Applications:

• Metabolic pathway characterization: Characterizes taxon-associated metabolic traits and pathway differences such as central carbon metabolism in Enterobacteriaceae and Lactobacillales.
• Comparative genomics: Compares KO-group representation between taxa to identify taxon-specific genomic signatures.
• Functional and evolutionary genomics: Investigates distribution patterns of shared genomic elements to study functional adaptation and evolutionary divergence.
• Hypothesis generation: Supports formulation of hypotheses about taxonomical distribution of genomic elements based on enrichment patterns.

Methodology:

Computationally compares KO-group counts between a test taxon and a background taxon within biochemical pathways, systematically analyzes monophyletic genomes, and examines large gene lists from 'omics' datasets to identify patterns of gene representation across taxa.