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A human gut metagenome-assembled genome catalogue spanning 41 countries supports genome-scale metabolic models

Nature Microbiology volume 11, pages 317–334 (2026)Cite this article

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Abstract

Understanding the human gut microbiome requires comprehensive genomic catalogues, yet many lack geographic diversity and contain medium-quality metagenome-assembled genomes (MAGs) missing up to 50% of genomic regions, potentially distorting functional insights. Here we describe an enhanced Human Reference Gut Microbiome (HRGM2) resource, a catalogue of near-complete MAGs (≥90% completeness, ≤5% contamination) and isolate genomes. HRGM2 comprises 155,211 non-redundant near-complete genomes from 4,824 prokaryotic species across 41 countries, representing a 66% increase in genome count and a 50% boost in species diversity compared to the Unified Human Gastrointestinal Genome catalogue. It enabled improved DNA-based species profiling, resolution of strain heterogeneity and survey of the human gut resistome. The exclusive use of these genomes improved metabolic capacity assessment, enabling high-confidence, automated genome-scale metabolic models of the entire microbiota and revealing disease-associated microbial metabolic interactions. This resource will facilitate reliable functional insights into gut microbiomes.

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Fig. 1: Overview of HRGM2.
Fig. 2: Benchmarking of taxonomic classification methods based on HRGM2 for human gut microbiota.
Fig. 3: Overview of the functional landscape of the human gut microbiome.
Fig. 4: The limitations of MQ genomes in functional profiling.
Fig. 5: Analysis of metabolic independence and interactions in the human gut microbiota.
Fig. 6: Distinct metabolic interaction patterns among CD- and CRC-associated species.

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Data availability

By accessing the web server, www.decodebiome.org/HRGM2/, users can browse and download all genomes for representative species, their annotations and metadata, including geographical origin, taxonomy, genomic content and genome statistics. The five classes of protein catalogues, 16S rRNA sequences and SNVs are also provided with their functional annotation and taxonomic origin. In addition to publicly available datasets, we incorporated three newly generated datasets (PRJNA1227720, PRJNA1227423 and PRJNA1226738), with basic metadata in Supplementary Table 11 and raw metagenomic sequencing data are available in the NCBI Sequence Read Archive. Metadata for the published datasets and samples used are available in Supplementary Tables 1 and 2. Source data are provided with this paper.

Code availability

The source code utilized for the construction and analysis of HRGM2 is publicly available in GitHub at https://github.com/netbiolab/HRGM2 (ref. 97).

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Acknowledgements

This research was supported by the National Research Foundation funded by the Ministry of Science and ICT (2022M3A9F3016364, 2022R1A2C1092062 to I.L.); by the Technology Innovation Program (20022947) funded by the Ministry of Trade, Industry and Energy (MOTIE, Korea); and in part by the Brain Korea 21(BK21) FOUR program.

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Author notes

  1. These authors contributed equally: Junyeong Ma, Nayeon Kim.

Authors and Affiliations

  1. Department of Biotechnology, College of Life Science and Biotechnology, Yonsei University, Seoul, Republic of Korea

    Junyeong Ma, Nayeon Kim, Jun Hyung Cha, Wonjong Kim, Sunmo Yang & Insuk Lee

  2. Molecular Systems Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany

    Chan Yeong Kim

  3. Department of Internal Medicine, Institute for Endocrine Research, Institute for Innovation in Digital Healthcare (IIDH), Yonsei University College of Medicine, Seoul, Republic of Korea

    Yong-ho Lee

  4. Yonsei Cancer Center, Division of Medical Oncology, Department of Internal Medicine, Graduate School of Medicine, Brain Korea 21 Project, Yonsei University College of Medicine, Seoul, Republic of Korea

    Han Sang Kim

  5. Division of Colorectal Surgery, Department of Surgery, Yonsei University College of Medicine, Seoul, Republic of Korea

    Yoon Dae Han

  6. Department of Laboratory Medicine, Research Institute of Bacterial Resistance, Yonsei University College of Medicine, Seoul, Republic of Korea

    Dongeun Yong

  7. Division of Endocrinology, Internal Medicine, Keimyung University School of Medicine, Daegu, Republic of Korea

    Eugene Han

  8. Department of Dermatology, Center for Aging Research, Chobanian and Avedisian School of Medicine, Boston University, Boston, MA, USA

    Samuel Beck

  9. DECODE BIOME Co., Ltd, Incheon, Republic of Korea

    Insuk Lee

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Contributions

J.M., N.K. and I.L. conceived the study. J.M. and N.K. constructed the catalogue and performed bioinformatics analysis. J.H.C., W.K. and S.B. contributed to bioinformatics analysis. C.Y.K. provided technical and scientific advice. Y.L., H.S.K., Y.D.H., D.Y. and E.H. contributed sequencing data generated from unpublished studies. S.Y. constructed the web server. I.L. supervised the project. J.M., N.K. and I.L. wrote the manuscript. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Insuk Lee.

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I.L. is a founder of and shareholder in DECODE BIOME. The other authors declare no competing interests.

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Nature Microbiology thanks the anonymous reviewers for their contribution to the peer review of this work. Peer reviewer reports are available.

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Extended data

Extended Data Fig. 1 Bioinformatics pipelines for HRGM2 construction.

a, The overall pipeline for HRGM2 construction. b, Pipeline to control genome quality. c, CheckM2 assessment of 10,172 MQ genomes previously assessed by CheckM (completeness-underestimated). The 6,281 genomes that were added to the near-complete (NC) genome set based on assessment using universal bacterial markers and CPR markers are marked with blue dots. The vertical and horizontal dashed lines indicate the 90% completeness and 5% contamination threshold, respectively. The grey area includes genomes that meet the NC genome criteria according to CheckM2. d, Count and proportion of filtered in and out by universal bacterial markers and CPR markers that meet the NC criteria under CheckM2.

Source data

Extended Data Fig. 2 Geographic origin of metagenomic samples in UHGG and HRGM2.

The number of metagenomic samples from which the MAGs originate in UHGG and HRGM2 by continent and country.

Extended Data Fig. 3 Extended comparative analyses supporting the HRGM2 overview.

a, Abundance distributions of phyla Elusimicrobiota (up) and Spirochaetota (down) across Africa and non-Africa datasets. All African datasets used for the construction of HRGM2 are included. For comparison, five non-African datasets were randomly selected. The datasets are represented by study accession and country. In the case of “PRJEB39223, United Kingdom”, 400 samples were randomly selected due to the large total sample size. Relative abundances were batch-corrected using MUPPHin and log10-transformed. A phylum was considered present in a sample only if its relative abundance exceeded 1e-06; values below this threshold were replaced with 1e-06. Thus, a y-axis value of -6 indicates absence of the phylum in that sample. Boxes were sorted from left to right, in descending order by median. b, Comparison between HRGM2 and UHGG for the number of NC member genomes for conspecific pairs. c-d, Comparison of the percentage of classified reads between HRGM2 and UHGG at (c) each taxonomic rank and (d) the genus rank, with datasets stratified by continent. A total of 2,624 metagenomic samples not used in either catalog were utilized, and the number of samples included in each dataset is provided in Supplementary Table 6. For boxplots, box lengths represent the interquartile range of the data, and whiskers extend to the lowest and highest values within 1.5 times the interquartile range from the first and third quartiles, respectively. The center bar represents the median. All the outliers are shown in the plots.

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Extended Data Fig. 4 Summary of HRGM2 species-specific marker gene database.

a, Maximum-likelihood phylogenetic tree with annotations of the number of species-specific marker genes. The color of the strip according to the number of species-specific marker genes is the same as in (b, c). b-c, The number and proportion of species in (b) HRGM2 and (c) Collinsella genus according to the number of species-specific marker genes.

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Extended Data Fig. 5 Analysis of strain-level heterogeneity across geographic regions in the human gut microbiota.

a, Number of species exceeding varying thresholds of near-complete (NC) genomes required for SNV-based analysis. HRGM2 consistently retains more species than UHGG across all thresholds, demonstrating enhanced capacity to resolve subspecies-level genetic heterogeneity. b, Comparison of the number of SNVs identified in species with ≥10 non-redundant NC genomes present in both catalogs. Boxplots show the distribution of SNV detection ratios (HRGM2/UHGG) per species. The number of species for each phylum is as follows: Campylobacterota, n = 6; Fusobacteriota, n = 5; Methanobacteriota, n = 2; Firmicutes, n = 81; Actinobacteriota, n = 47; Proteobacteria, n = 55; Desulfobacterota, n = 5; Bacteroidota, n = 111; Firmicutes_A, n = 295; Firmicutes_C, n = 36; Thermoplasmatota, n = 2; Spirochaetota, n = 5; Elusimicrobiota, n = 2; Verrucomicrobiota, n = 11. Box lengths represent the interquartile range of the data, and whiskers extend to the lowest and highest values within 1.5 times the interquartile range from the first and third quartiles, respectively. The center bar represents the median. All the outliers are shown in the plot. c, Number of species within each phylum exhibiting subspecies-level geographic stratification between Europe/US and Asia (black bars), based on PERMANOVA test (p-value < 0.01 and pseudo-F statistic > 30). The reported pseudo-F and p-value come from a permutation-based upper-tailed (one-sided) test (default 999 permutations in scikit-bio). d, Representative phylogenetic trees constructed from SNVs identified in metagenomic samples for top 20 species with geographic stratification, colored by geographic origin (green: Asia; red: Europe/US). Species names, HRGM2 species identifiers, and the corresponding pseudo-F statistics are shown below each tree.

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Extended Data Fig. 6 Functional landscape of human gut microbiome.

a, Summary of functional prediction pipeline in HRGM2. b, Average copy number of CAZyme families per phylum. GH: Glycoside Hydrolase, GT: Glycosyl Transferase, CE: Carbohydrate Esterase, CBM: Carbohydrate Binding Module, PL: Polysaccharide Lyase, AA: Auxiliary Activity. c, Average copy number of GH CAZyme families for genera with more than 100 GH CAZyme families. d, Explained variance by Western/non-Western categorization for the 10 species with the most distinct CAZyme profiles between Western and non-Western continents. In (c) and (d), the color of the bars represents phylum. e, Comparison of the prevalence and the copy number of each CAZyme family between Faecalibacillus intestinalis genomes from Western and non-Western countries (371 genomes from Western and 417 genomes from non-Western countries). Violin plots display the distribution density, while overlaid box plots denote the median (center line), interquartile range (25th-75th percentiles; box bounds), and the minimum and maximum values (whiskers).

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Extended Data Fig. 7 Summary of genome qualities for UHGG.

a, Number and proportion of genomes with completeness ≥ 90% and contamination ≤ 5% (left pie chart), that passed GUNC (center pie chart), and that met the NC criteria (right pie chart), in UHGG representative genomes (up) and non-redundant genomes (down). b, Distribution of the percentage of genomes that are not completeness ≥ 90% and contamination ≤ 5% (top), that did not pass GUNC (middle), and that did not meet the NC criteria (bottom) for each UHGG species with at least two non-redundant genomes. The distributions are either categorized by the number of non-redundant genomes included in each species (left) or not (right) (2 ≤ # < 10, n = 1,590; 10 ≤ # < 100, n = 877; ≥ 100, n = 319; Total, n = 2,786). Box lengths represent the interquartile range of the data, and whiskers extend to the lowest and highest values within 1.5 times the interquartile range from the first and third quartiles, respectively. The center bar represents the median. All the outliers are shown in the plots.

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Extended Data Fig. 8 Additional analyses for metabolic independence and interaction.

a, Possession percentage of 33 KEGG modules for UHGG (n = 4,644) and HRGM2 (n = 4,824) species. Statistical significance was assessed using a two-sided Mann-Whitney U test (P = 1.74e-42), indicating notable differences in module possession between the two catalogs. b, HMI and LMI species percentage in UHGG and HRGM2. c, d, Representative genome size (c) and the number of countries where each species originated (d) of HMI, other, LMI species. In (c) and (d), the number of HMI/Others/LMI species is 751/3,685/388 and 688/3,533/383, respectively; (d) considers only species with country information. P-value was calculated using a two-sided Mann-Whitney U test (P for HMI-Others/Others-LMI/HMI-LMI = (c) 3.270e-227/1.198e-205/2.297e-168; (d) 6.308e-29/1.839e-05/3.472e-05). e, Number and percentage of species with available isolate genomes, categorized by metabolic independence. The association between metabolic independence and the availability of isolate genomes was assessed by a Chi-squared test (two-sided). f, Comparison of the proportions of non-overlapping metabolites (left), reactions (middle), and gene-associated reactions (right) between 327 conspecific MQ and NC GEMs. Differences were evaluated with two-sided Wilcoxon signed-rank test (P for Metabolite/Reaction/Gene-associated reaction = 3.078e-19/1.203e-26/9.691e-45). g, Violin plot showing distribution of MIP and MRO scores for F18-mix (n = 18) and F13-mix (n = 13) strains of Kp-2H7. P-value was calculated using a one-sided Mann-Whitney U test. For boxplots in Extended Data Fig. 8, box lengths represent the interquartile range of the data, and whiskers extend to the lowest and highest values within 1.5 times the interquartile range from the first and third quartiles, respectively. The center bar represents the median. All the outliers are shown in the plots. ****, P < 1e-04; ns: not significant, P > 0.05.

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Ma, J., Kim, N., Cha, J.H. et al. A human gut metagenome-assembled genome catalogue spanning 41 countries supports genome-scale metabolic models. Nat Microbiol 11, 317–334 (2026). https://doi.org/10.1038/s41564-025-02206-1

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