Treffer: KEGGaNOG: A Lightweight Tool for KEGG Module Profiling From Orthology-Based Annotations.
Title:
KEGGaNOG: A Lightweight Tool for KEGG Module Profiling From Orthology-Based Annotations.
Authors:
Popov IV; Faculty 'Bioengineering and Veterinary Medicine', Don State Technical University, Rostov-on-Don, Russian Federation., Chikindas ML; Health Promoting Naturals Laboratory, School of Environmental and Biological Sciences, Rutgers, the State University of New Jersey, New Brunswick, New Jersey, USA.; Department of General Hygiene, I.M. Sechenov First Moscow State Medical University, Moscow, Russian Federation., Venema K; Beneficial Microbes Consultancy, Wageningen, the Netherlands.; Wageningen Food & Biobased Research, Wageningen University & Research, Wageningen, the Netherlands., Ermakov AM; Faculty 'Bioengineering and Veterinary Medicine', Don State Technical University, Rostov-on-Don, Russian Federation., Popov IV; Faculty 'Bioengineering and Veterinary Medicine', Don State Technical University, Rostov-on-Don, Russian Federation.
Source:
Molecular nutrition & food research [Mol Nutr Food Res] 2026 Jan; Vol. 70 (1), pp. e70269. Date of Electronic Publication: 2025 Sep 18.
Publication Type:
Journal Article
Language:
English
Journal Info:
Publisher: Wiley-VCH Country of Publication: Germany NLM ID: 101231818 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1613-4133 (Electronic) Linking ISSN: 16134125 NLM ISO Abbreviation: Mol Nutr Food Res Subsets: MEDLINE
Imprint Name(s):
Original Publication: Weinheim, Germany : Wiley-VCH, c2004-
MeSH Terms:
References:
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B. N. Abdul Hakim, N. J. Xuan, and S. N. H. Oslan, “A Comprehensive Review of Bioactive Compounds From Lactic Acid Bacteria: Potential Functions as Functional Food in Dietetics and the Food Industry,” Foods 12 (2023): 2850.
E. D. Graham, J. F. Heidelberg, and B. J. Tully, “Potential for Primary Productivity in a Globally‐Distributed Bacterial Phototroph,” The ISME Journal 12 (2018): 1861–1866.
M. Kanehisa, Y. Sato, and K. Morishima, “BlastKOALA and GhostKOALA: KEGG Tools for Functional Characterization of Genome and Metagenome Sequences,” Journal of Molecular Biology 428 (2016): 726–731.
T. Aramaki, R. Blanc‐Mathieu, H. Endo, et al., “KofamKOALA: KEGG Ortholog Assignment Based on Profile HMM and Adaptive Score Threshold,” Bioinformatics 36 (2020): 2251–2252.
T. C. Dakal, C. Xu, and A. Kumar, “Advanced Computational Tools, Artificial Intelligence and Machine‐Learning Approaches in Gut Microbiota and Biomarker Identification,” Frontiers in Medical Technology 6 (2025): 1434799, https://doi.org/10.3389/fmedt.2024.1434799.
C. P. Cantalapiedra, A. Hernández‐Plaza, I. Letunic, P. Bork, and J. Huerta‐Cepas, “eggNOG‐Mapper v2: Functional Annotation, Orthology Assignments, and Domain Prediction at the Metagenomic Scale,” Molecular Biology and Evolution 38 (2021): 5825–5829.
M. Kanehisa and S. Goto, “KEGG: Kyoto Encyclopedia of Genes and Genomes,” Nucleic Acids Research 28 (2000): 27–30.
The Pandas Development Team. pandas‐dev/pandas: Pandas (v2.3.2). Zenodo (2024), https://doi.org/10.5281/ZENODO.3509134.
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J. D. Hunter, “Matplotlib: A 2D Graphics Environment,” Computing in Science & Engineering 9 (2007): 90–95.
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N. A. O'Leary, M. W. Wright, J. R. Brister, et al., “Reference Sequence (RefSeq) Database at NCBI: Current Status, Taxonomic Expansion, and Functional Annotation,” Nucleic Acids Research 44 (2016): D733–D745.
D. Hyatt, G.‐L. Chen, P. F. Locascio, M. L. Land, F. W. Larimer, and L. J. Hauser, “Prodigal: Prokaryotic Gene Recognition and Translation Initiation Site Identification,” BMC Bioinformatics 11 (2010): 119.
J. M. Lorenzo, P. E. Munekata, R. Dominguez, M. Pateiro, J. A. Saraiva, and D. Franco, in Innovative Technologies for Food Preservation, eds. F. J. Barba, A. S. Sant'Ana, and V. Orlie (Academic Press, 2018), 53–107.
A. Umanets, I. S. Surono, and K. Venema, “I Am Better Than I Look: Genome Based Safety Assessment of the Probiotic Lactiplantibacillus plantarum IS‐10506,” BMC Genomics 24 (2023): 518.
M. Cunningham, M. A. Azcarate‐Peril, A. Barnard, et al., “Shaping the Future of Probiotics and Prebiotics,” Trends in Microbiology 29 (2021): 667–685.
I. V. Popov, M. L. Chikindas, and I. V. Popov, “Genomic Insights Into Streptomyces albidoflavus SM254: Tracing the Putative Signs of Anti‐Pseudogymnoascus Destructans Properties,” Brazilian Journal of Microbiology 56 (2025): 2121–2131.
I. V. Popov, A. D. Manakhov, V. E. Gorobets, et al., “Metagenomic Investigation of Intestinal Microbiota of Insectivorous Synanthropic Bats: Densoviruses, Antibiotic Resistance Genes, and Functional Profiling of Gut Microbial Communities,” International Journal of Molecular Sciences 26 (2025): 5941.
M. Shaffer, M. A. Borton, B. B. McGivern, et al., “DRAM for Distilling Microbial Metabolism to Automate the Curation of Microbiome Function,” Nucleic Acids Research 48 (2020): 8883–8900.
P. D. Karp, S. Paley, M. Krummenacker, A. Kothari, M. J. Wannemuehler, and G. J. Phillips, “Pathway Tools Management of Pathway/Genome Data for Microbial Communities,” Frontiers in Bioinformatics 2 (2022): 869150, https://doi.org/10.3389/fbinf.2022.869150.
Y. Ye and T. G. Doak, “A Parsimony Approach to Biological Pathway Reconstruction/Inference for Genomes and Metagenomes,” PLOS Computational Biology 5 (2009): 1000465.
R. Barrangou, E. P. Briczinski, L. L. Traeger, et al., “Comparison of the Complete Genome Sequences of Bifidobacterium animalis Subsp. Lactis DSM 10140 and Bl‐04,” Journal of Bacteriology 191 (2009): 4144–4151.
S. Fukuda, H. Toh, K. Hase, et al., “Bifidobacteria Can Protect From Enteropathogenic Infection Through Production of Acetate,” Nature 469 (2011): 543–547.
A. Koryszewska‐Baginska, T. Aleksandrzak‐Piekarczyk, and J. Bardowski, “Complete Genome Sequence of the Probiotic Strain Lactobacillus casei (Formerly Lactobacillus paracasei) LOCK919,” Genome Announcements 1 (2013): 00758.
M. A. Mahowald, F. E. Rey, H. Seedorf, et al., “Characterizing a Model Human Gut Microbiota Composed of Members of Its Two Dominant Bacterial Phyla,” Proceedings of the National Academy of Sciences 106 (2009): 5859–5864.
J. Nölling, G. Breton, M. V. Omelchenko, et al., “Genome Sequence and Comparative Analysis of the Solvent‐Producing Bacterium Clostridium acetobutylicum,” Journal of Bacteriology 183 (2001): 4823–4838.
B. J. Tindall, J. Sikorski, S. Lucas, et al., “Complete Genome Sequence of Meiothermus ruber Type Strain (21T),” Standards in Genomic Sciences 3 (2010): 26–36.
E. Belda, R. G. A. van Heck, M. José Lopez‐Sanchez, et al., “The Revisited Genome of Pseudomonas putida KT2440 Enlightens Its Value as a Robust Metabolic Chassis,” Environmental Microbiology 18 (2016): 3403–3424.
K. Han, Z. Li, R. Peng, et al., “Extraordinary Expansion of a Sorangium cellulosum Genome From an Alkaline Milieu,” Scientific Reports 3 (2013): 2101.
R. Rosenstein, C. Nerz, L. Biswas, et al., “Genome Analysis of the Meat Starter Culture Bacterium Staphylococcus carnosus TM300,” Applied and Environmental Microbiology 75 (2009): 811–822.
K. E. Nelson, R. A. Clayton, S. R. Gill, et al., “Evidence for Lateral Gene Transfer Between Archaea and Bacteria From Genome Sequence of Thermotoga maritima,” Nature 399 (1999): 323–329.
B. N. Abdul Hakim, N. J. Xuan, and S. N. H. Oslan, “A Comprehensive Review of Bioactive Compounds From Lactic Acid Bacteria: Potential Functions as Functional Food in Dietetics and the Food Industry,” Foods 12 (2023): 2850.
E. D. Graham, J. F. Heidelberg, and B. J. Tully, “Potential for Primary Productivity in a Globally‐Distributed Bacterial Phototroph,” The ISME Journal 12 (2018): 1861–1866.
M. Kanehisa, Y. Sato, and K. Morishima, “BlastKOALA and GhostKOALA: KEGG Tools for Functional Characterization of Genome and Metagenome Sequences,” Journal of Molecular Biology 428 (2016): 726–731.
T. Aramaki, R. Blanc‐Mathieu, H. Endo, et al., “KofamKOALA: KEGG Ortholog Assignment Based on Profile HMM and Adaptive Score Threshold,” Bioinformatics 36 (2020): 2251–2252.
T. C. Dakal, C. Xu, and A. Kumar, “Advanced Computational Tools, Artificial Intelligence and Machine‐Learning Approaches in Gut Microbiota and Biomarker Identification,” Frontiers in Medical Technology 6 (2025): 1434799, https://doi.org/10.3389/fmedt.2024.1434799.
C. P. Cantalapiedra, A. Hernández‐Plaza, I. Letunic, P. Bork, and J. Huerta‐Cepas, “eggNOG‐Mapper v2: Functional Annotation, Orthology Assignments, and Domain Prediction at the Metagenomic Scale,” Molecular Biology and Evolution 38 (2021): 5825–5829.
M. Kanehisa and S. Goto, “KEGG: Kyoto Encyclopedia of Genes and Genomes,” Nucleic Acids Research 28 (2000): 27–30.
The Pandas Development Team. pandas‐dev/pandas: Pandas (v2.3.2). Zenodo (2024), https://doi.org/10.5281/ZENODO.3509134.
C. R. Harris, K. J. Millman, S. J. Van Der Walt, et al., “Array Programming With NumPy,” Nature 585 (2020): 357–362.
J. D. Hunter, “Matplotlib: A 2D Graphics Environment,” Computing in Science & Engineering 9 (2007): 90–95.
M. Waskom, “Seaborn: Statistical Data Visualization,” Journal of Open Source Software 6 (2021): 3021.
N. A. O'Leary, M. W. Wright, J. R. Brister, et al., “Reference Sequence (RefSeq) Database at NCBI: Current Status, Taxonomic Expansion, and Functional Annotation,” Nucleic Acids Research 44 (2016): D733–D745.
D. Hyatt, G.‐L. Chen, P. F. Locascio, M. L. Land, F. W. Larimer, and L. J. Hauser, “Prodigal: Prokaryotic Gene Recognition and Translation Initiation Site Identification,” BMC Bioinformatics 11 (2010): 119.
J. M. Lorenzo, P. E. Munekata, R. Dominguez, M. Pateiro, J. A. Saraiva, and D. Franco, in Innovative Technologies for Food Preservation, eds. F. J. Barba, A. S. Sant'Ana, and V. Orlie (Academic Press, 2018), 53–107.
A. Umanets, I. S. Surono, and K. Venema, “I Am Better Than I Look: Genome Based Safety Assessment of the Probiotic Lactiplantibacillus plantarum IS‐10506,” BMC Genomics 24 (2023): 518.
M. Cunningham, M. A. Azcarate‐Peril, A. Barnard, et al., “Shaping the Future of Probiotics and Prebiotics,” Trends in Microbiology 29 (2021): 667–685.
I. V. Popov, M. L. Chikindas, and I. V. Popov, “Genomic Insights Into Streptomyces albidoflavus SM254: Tracing the Putative Signs of Anti‐Pseudogymnoascus Destructans Properties,” Brazilian Journal of Microbiology 56 (2025): 2121–2131.
I. V. Popov, A. D. Manakhov, V. E. Gorobets, et al., “Metagenomic Investigation of Intestinal Microbiota of Insectivorous Synanthropic Bats: Densoviruses, Antibiotic Resistance Genes, and Functional Profiling of Gut Microbial Communities,” International Journal of Molecular Sciences 26 (2025): 5941.
M. Shaffer, M. A. Borton, B. B. McGivern, et al., “DRAM for Distilling Microbial Metabolism to Automate the Curation of Microbiome Function,” Nucleic Acids Research 48 (2020): 8883–8900.
P. D. Karp, S. Paley, M. Krummenacker, A. Kothari, M. J. Wannemuehler, and G. J. Phillips, “Pathway Tools Management of Pathway/Genome Data for Microbial Communities,” Frontiers in Bioinformatics 2 (2022): 869150, https://doi.org/10.3389/fbinf.2022.869150.
Y. Ye and T. G. Doak, “A Parsimony Approach to Biological Pathway Reconstruction/Inference for Genomes and Metagenomes,” PLOS Computational Biology 5 (2009): 1000465.
R. Barrangou, E. P. Briczinski, L. L. Traeger, et al., “Comparison of the Complete Genome Sequences of Bifidobacterium animalis Subsp. Lactis DSM 10140 and Bl‐04,” Journal of Bacteriology 191 (2009): 4144–4151.
S. Fukuda, H. Toh, K. Hase, et al., “Bifidobacteria Can Protect From Enteropathogenic Infection Through Production of Acetate,” Nature 469 (2011): 543–547.
A. Koryszewska‐Baginska, T. Aleksandrzak‐Piekarczyk, and J. Bardowski, “Complete Genome Sequence of the Probiotic Strain Lactobacillus casei (Formerly Lactobacillus paracasei) LOCK919,” Genome Announcements 1 (2013): 00758.
M. A. Mahowald, F. E. Rey, H. Seedorf, et al., “Characterizing a Model Human Gut Microbiota Composed of Members of Its Two Dominant Bacterial Phyla,” Proceedings of the National Academy of Sciences 106 (2009): 5859–5864.
J. Nölling, G. Breton, M. V. Omelchenko, et al., “Genome Sequence and Comparative Analysis of the Solvent‐Producing Bacterium Clostridium acetobutylicum,” Journal of Bacteriology 183 (2001): 4823–4838.
B. J. Tindall, J. Sikorski, S. Lucas, et al., “Complete Genome Sequence of Meiothermus ruber Type Strain (21T),” Standards in Genomic Sciences 3 (2010): 26–36.
E. Belda, R. G. A. van Heck, M. José Lopez‐Sanchez, et al., “The Revisited Genome of Pseudomonas putida KT2440 Enlightens Its Value as a Robust Metabolic Chassis,” Environmental Microbiology 18 (2016): 3403–3424.
K. Han, Z. Li, R. Peng, et al., “Extraordinary Expansion of a Sorangium cellulosum Genome From an Alkaline Milieu,” Scientific Reports 3 (2013): 2101.
R. Rosenstein, C. Nerz, L. Biswas, et al., “Genome Analysis of the Meat Starter Culture Bacterium Staphylococcus carnosus TM300,” Applied and Environmental Microbiology 75 (2009): 811–822.
K. E. Nelson, R. A. Clayton, S. R. Gill, et al., “Evidence for Lateral Gene Transfer Between Archaea and Bacteria From Genome Sequence of Thermotoga maritima,” Nature 399 (1999): 323–329.
Grant Information:
23-14-00316 Russian Science Foundation
Entry Date(s):
Date Created: 20250919 Date Completed: 20251226 Latest Revision: 20251226
Update Code:
20251226
DOI:
10.1002/mnfr.70269
PMID:
40968530
Database:
MEDLINE
Weitere Informationen
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.
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