KEGGaNOG: A Lightweight Tool for KEGG Module Profiling From Orthology-Based Annotations.

Area of Science:

• Microbial genomics
• Bioinformatics
• Systems biology

Background:

• Functional interpretation of bacterial genomes and metagenomes is crucial for diverse applications.
• Existing tools may lack scalability or specific features for pathway-level profiling.

Purpose of the Study:

• To introduce KEGGaNOG, a Python tool for pathway-level functional annotation of bacterial genomes and metagenomes.
• To enable reproducible metabolic profiling and comparative analysis.

Main Methods:

• KEGGaNOG translates orthology-based annotations (e.g., from eggNOG-mapper) into KEGG module completeness scores.
• It integrates KEGG-Decoder logic for score calculation.
• Supports individual and multi-sample analyses with various visualization options.

Main Results:

• KEGGaNOG accurately identified core biosynthetic pathways in bacterial genomes.
• It highlighted functional differences across samples, including vitamin biosynthesis and stress-response pathways.
• Demonstrated utility on 11 bacterial genomes, including probiotic strains.

Conclusions:

• KEGGaNOG offers a practical framework for high-throughput functional annotation and comparative metabolic profiling.
• It is suitable for analyzing novel strains, metagenomic data, and supporting probiotic strain selection.