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

Abstract

Functional interpretation of bacterial genomes and metagenomes is essential for applications ranging from microbial ecology to probiotic development. KEGGaNOG is a lightweight and scalable Python tool that enables pathway-level profiling by translating orthology-based annotations into KEGG module completeness scores. KEGGaNOG accepts input from eggNOG-mapper annotations and supports both individual genome and multi-sample analyses. It calculates completeness scores for KEGG modules using internally integrated KEGG-Decoder logic and offers a suite of visualization options, including heatmaps, grouped summaries, barplots, radar plots, and correlation networks. We demonstrate its use on 11 well-characterized bacterial genomes, including several probiotic strains. KEGGaNOG accurately captured core biosynthetic capabilities and highlighted functionally informative differences across samples, such as vitamin biosynthesis, stress-response pathways, and transport systems. KEGGaNOG provides a practical framework for high-throughput functional annotation and comparative metabolic profiling in bacterial genomics and microbiome research. It is particularly well suited for preliminary analysis of novel or uncharacterized strains and is applicable to both isolate and metagenome-derived data. In the context of probiotic research, KEGGaNOG supports mechanistic exploration and strain selection by linking genomic content to functional capacity in a reproducible and interpretable manner.