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

Ilia V Popov1, Michael L Chikindas2,3, Koen Venema4,5

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KEGGaNOG is a Python tool for bacterial genome analysis. It interprets orthology data to assess KEGG module completeness, aiding microbial ecology and probiotic research.

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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.