CheckM2: a rapid, scalable and accurate tool for assessing microbial genome quality using machine learning
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Abstract
Advances in sequencing technologies and bioinformatics tools have dramatically increased the recovery rate of microbial genomes from metagenomic data. Assessing the quality of metagenome-assembled genomes (MAGs) is a critical step before downstream analysis. Here, we present CheckM2, an improved method of predicting genome quality of MAGs using machine learning. Using synthetic and experimental data, we demonstrate that CheckM2 outperforms existing tools in both accuracy and computational speed. In addition, CheckM2's database can be rapidly updated with new high-quality reference genomes, including taxa represented only by a single genome. We also show that CheckM2 accurately predicts genome quality for MAGs from novel lineages, even for those with reduced genome size (for example, Patescibacteria and the DPANN superphylum). CheckM2 provides accurate genome quality predictions across bacterial and archaeal lineages, giving increased confidence when inferring biological conclusions from MAGs.
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The final B. bifidum dataset encompassed 1,351 unique high-quality sequences, including 156 genomes from cultured isolates and 1,195 MAGs.
“After genome quality control and dereplication using CheckM2 and drep [38,39] , the final B. bifidum dataset encompassed 1,351 unique high-quality sequences, including 156 genomes from cultured isolates and 1,195 MAGs [Supplementary Table 1].”
From Genome-scale insights into the metabolic landscape and evolutionary development of Bifidobacterium bifidum (2026) · cites this paper as [38]
The quality of each genome was evaluated using CheckM2, based on completeness and contamination levels; genomes with completeness below 95% and/or contamination above 5% were discarded.
“The quality of each genome was evaluated using CheckM2, based on completeness and contamination levels [38] . Genomes with completeness below 95% and/or contamination above 5% were discarded.”
From Genome-scale insights into the metabolic landscape and evolutionary development of Bifidobacterium bifidum (2026) · cites this paper as [38]
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KEGG: Kyoto Encyclopedia of Genes and Genomes
Kanehisa, M. (2000) · Nucleic Acids Research · doi:10.1093/nar/28.1.27 · Cited by 37,077 · see citing works
KEGG: Kyoto Encyclopedia of Genes and Genomes
Ogata, H., Goto, S., Sato, K., Fujibuchi, W., Bono, H., & Kanehisa, M. (1999) · Nucleic Acids Research · doi:10.1093/nar/27.1.29 · Cited by 31,232 · see citing works
Parks, D. H., Imelfort, M., Skennerton, C. T., Hugenholtz, P., & Tyson, G. W. (2015) · Genome Research · doi:10.1101/gr.186072.114 · Cited by 12,496 · see citing works
Self-citationProdigal: prokaryotic gene recognition and translation initiation site identification
Hyatt, D., Chen, G.-L., LoCascio, P. F., Land, M., Larimer, F. W., & Hauser, L. (2010) · BMC Bioinformatics · doi:10.1186/1471-2105-11-119 · Cited by 12,426 · see citing works
LightGBM: A Highly Efficient Gradient Boosting Decision Tree
(2017) · HAL · Cited by 10,512 · see citing works
KEGG as a reference resource for gene and protein annotation
Kanehisa, M., Sato, Y., Kawashima, M., Furumichi, M., & Tanabe, M. (2016) · Nucleic Acids Research · doi:10.1093/nar/gkv1070 · Cited by 7,387 · see citing works
seaborn: statistical data visualization
Waskom, M. (2021) · Journal of Open Source Software · doi:10.21105/joss.03021 · Cited by 7,310 · see citing works
A standardized bacterial taxonomy based on genome phylogeny substantially revises the tree of life
Parks, D. H., Chuvochina, M., Waite, D. W., Rinke, C., Skarshewski, A., Chaumeil, P.-A., & Hugenholtz, P. (2018) · Nature Biotechnology · doi:10.1038/nbt.4229 · Cited by 3,603 · see citing works
Self-citationThe Genome Standards Consortium, Bowers, R. M., Kyrpides, N. C., Stepanauskas, R., Harmon-Smith, M., Doud, D., Reddy, T. B. K., Schulz, F., Jarett, J., Rivers, A. R., et al. (2017) · Nature Biotechnology · doi:10.1038/nbt.3893 · Cited by 2,624 · see citing works
Self-citationSMARCAD1 ATPase activity is required to silence endogenous retroviruses in embryonic stem cells
Sachs, P., Ding, D., Bergmaier, P., Lamp, B., Schlagheck, C., Finkernagel, F., Nist, A., Stiewe, T., & Mermoud, J. E. (2019) · Nature Communications · doi:10.1038/s41467-019-09078-0 · Cited by 2,388 · see citing works
D’Hulst, G., Soro-Arnaiz, I., Masschelein, E., Veys, K., Fitzgerald, G., Smeuninx, B., Kim, S., Deldicque, L., Blaauw, B., Carmeliet, P., et al. (2020) · Nature Communications · doi:10.1038/s41467-019-13889-6 · Cited by 2,310 · see citing works
BUSCO: Assessing Genome Assembly and Annotation Completeness
Seppey, M., Manni, M., & Zdobnov, E. M. (2019) · Methods in molecular biology · doi:10.1007/978-1-4939-9173-0_14 · Cited by 2,238 · see citing works
Recovery of nearly 8,000 metagenome-assembled genomes substantially expands the tree of life
Parks, D. H., Rinke, C., Chuvochina, M., Chaumeil, P.-A., Woodcroft, B. J., Evans, P. N., Hugenholtz, P., & Tyson, G. W. (2017) · Nature Microbiology · doi:10.1038/s41564-017-0012-7 · Cited by 1,882 · see citing works
Self-citationPerineuronal nets stabilize the grid cell network
Christensen, A. C., Lensjø, K. K., Lepperød, M. E., Dragly, S.-A., Sutterud, H., Blackstad, J. S., Fyhn, M., & Hafting, T. (2021) · Nature Communications · doi:10.1038/s41467-020-20241-w · Cited by 1,865 · see citing works
Pasolli, E., Asnicar, F., Manara, S., Zolfo, M., Karcher, N., Armanini, F., Beghini, F., Manghi, P., Tett, A., Ghensi, P., et al. (2019) · Cell · doi:10.1016/j.cell.2019.01.001 · Cited by 1,591 · see citing works
Unusual biology across a group comprising more than 15% of domain Bacteria
Brown, C. T., Hug, L. A., Thomas, B. C., Sharon, I., Castelle, C. J., Singh, A., Wilkins, M. J., Wrighton, K. C., Williams, K. H., & Banfield, J. F. (2015) · Nature · doi:10.1038/nature14486 · Cited by 1,216 · see citing works
Yang, B.-J., Han, X., Yin, L.-L., Xing, M., Xu, Z. H., & Xue, H. (2016) · Nature Communications · doi:10.1038/ncomms11388 · Cited by 1,203 · see citing works
DRAM for distilling microbial metabolism to automate the curation of microbiome function
Shaffer, M., Borton, M., McGivern, B. B., Zayed, A., Rosa, S. L. L., Solden, L., Liu, P.-F., Narrowe, A. B., Rodríguez-Ramos, J. A., Bolduc, B., et al. (2020) · bioRxiv · doi:10.1093/nar/gkaa621 · Cited by 1,043 · see citing works
Anantharaman, K., Brown, C., Hug, L., Sharon, I., Castelle, C., Probst, A., Thomas, B., Singh, A., Wilkins, M., Karaoz, U., et al. (2016) · Nature Communications · doi:10.1038/ncomms13219 · Cited by 1,040 · see citing works
RefSeq: an update on prokaryotic genome annotation and curation
Haft, D. H., DiCuccio, M. N., Badretdin, A. J., Brover, V. V., Chetvernin, V., O’Neill, K., Li, W., Chitsaz, F., Derbyshire, M. K., Gonzales, N. R., et al. (2017) · Nucleic Acids Research · doi:10.1093/nar/gkx1068 · Cited by 989 · see citing works
scMC learns biological variation through the alignment of multiple single-cell genomics datasets
Zhang, L., & Nie, Q. H. (2021) · Genome biology · doi:10.1186/s13059-020-02238-2 · Cited by 848 · see citing works
Correcting for 16S rRNA gene copy numbers in microbiome surveys remains an unsolved problem
Louca, S., Doebeli, M., & Parfrey, L. W. (2018) · Microbiome · doi:10.1186/s40168-018-0420-9 · Cited by 755 · see citing works
Benson, D. A., Cavanaugh, M. L., Clark, K., Karsch‐Mizrachi, I., Ostell, J., Pruitt, K. D., & Sayers, E. W. (2017) · Nucleic Acids Research · doi:10.1093/nar/gkx1094 · Cited by 752 · see citing works
Detection of gene pathways with predictive power for breast cancer prognosis
Ma, S., & Kosorok, M. R. (2010) · BMC Bioinformatics · doi:10.1186/1471-2105-11-1 · Cited by 691 · see citing works
STRING 7--recent developments in the integration and prediction of protein interactions
von Mering, C., Jensen, L. J., Kuhn, M., Chaffron, S., Doerks, T., Kruger, B., Snel, ., & Peer Bork, B. (2006) · Nucleic Acids Research · doi:10.1093/nar/gkl825 · Cited by 666 · see citing works
Improved metagenome binning and assembly using deep variational autoencoders
Nissen, J. N., Johansen, J., Allesøe, R. L., Sønderby, C. K., Armenteros, J. J. A., Grønbech, C. H., Jensen, L. J., Nielsen, H. B., Petersen, T. N., Winther, O., et al. (2021) · Nature Biotechnology · doi:10.1038/s41587-020-00777-4 · Cited by 634 · see citing works
Major New Microbial Groups Expand Diversity and Alter our Understanding of the Tree of Life
Castelle, C. J., & Banfield, J. F. (2018) · Cell · doi:10.1016/j.cell.2018.02.016 · Cited by 595 · see citing works
eggNOG: automated construction and annotation of orthologous groups of genes
Jensen, L. J., Julien, P., Kuhn, M., von Mering, C., Muller, J., Doerks, T., & Bork, P. (2007) · Nucleic Acids Research · doi:10.1093/nar/gkm796 · Cited by 547 · see citing works
Biosynthetic capacity, metabolic variety and unusual biology in the CPR and DPANN radiations
Castelle, C. J., Brown, C. T., Anantharaman, K., Probst, A. J., Huang, R. H., & Banfield, J. F. (2018) · Nature Reviews Microbiology · doi:10.1038/s41579-018-0076-2 · Cited by 454 · see citing works
Genome-centric view of carbon processing in thawing permafrost
Woodcroft, B. J., Singleton, C. M., Boyd, J. A., Evans, P. N., Emerson, J., Zayed, A. A., Hoelzle, R. D., Lamberton, T. O., Mccalley, C. K., Hodgkins, S. B., et al. (2018) · Nature · doi:10.1038/s41586-018-0338-1 · Cited by 435 · see citing works
Self-citationBowerman, K. L., Rehman, S. F., Vaughan, A., Lachner, N., Budden, K. F., Kim, R., Wood, D., Gellatly, S. L., Shukla, S. D., Wood, L. G., et al. (2020) · Nature Communications · doi:10.1038/s41467-020-19701-0 · Cited by 433 · see citing works
Singleton, C. M., Petriglieri, F., Kristensen, J. M., Kirkegaard, R. H., Michaelsen, T. Y., Andersen, M. H., Kondrotaite, Z., Karst, S. M., Dueholm, M. S., Nielsen, P. H., et al. (2021) · Nature Communications · doi:10.1038/s41467-021-22203-2 · Cited by 346 · see citing works
AlQuraishi, M. (2019) · Bioinformatics · doi:10.1093/bioinformatics/btz422 · Cited by 312 · see citing works
GUNC: detection of chimerism and contamination in prokaryotic genomes
Orakov, A. N., Fullam, A., Coelho, L. P., Khedkar, S., Szklarczyk, D., Mende, D. R., Schmidt, T., & Bork, P. (2021) · Genome biology · doi:10.1186/s13059-021-02393-0 · Cited by 307 · see citing works
Microdiversification in genome-streamlined ubiquitous freshwater Actinobacteria
Neuenschwander, S. M., Ghai, R., Pernthaler, J., & Salcher, M. M. (2017) · The ISME Journal · doi:10.1038/ismej.2017.156 · Cited by 232 · see citing works
Rinke, C., Rubino, F., Messer, L. F., Youssef, N. H., Parks, D. H., Chuvochina, M. S., Brown, M. V., Jeffries, T. C., Tyson, G. W., Seymour, J. R., et al. (2018) · The ISME Journal · doi:10.1038/s41396-018-0282-y · Cited by 178 · see citing works
Self-citationThe distinction of CPR bacteria from other bacteria based on protein family content
Méheust, R., Burstein, D., Castelle, C., & Banfield, J. (2019) · Nature Communications · doi:10.1038/s41467-019-12171-z · Cited by 143 · see citing works
DRAM for distilling microbial metabolism to automate the curation of microbiome functionPreprint
Shaffer, M., .Borton, M. A., McGivern, B. B., et al. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.06.29.177501 · Cited by 75 · see citing works
Yeoh, Y. K., Sekiguchi, Y., Parks, D., & Hugenholtz, P. (2015) · Molecular biology and evolution · doi:10.1093/molbev/msv281 · Cited by 73 · see citing works
Self-citationJarett, J. K., Nayfach, S., Podar, M., Inskeep, W. P., Ivanova, N., Munson-McGee, J. H., Schulz, F., Young, M., Jay, Z. J., Beam, J. P., et al. (2018) · Microbiome · doi:10.1186/s40168-018-0539-8 · Cited by 54 · see citing works
Biases in genome reconstruction from metagenomic data
Nelson, W., Tully, B., & Mobberley, J. M. (2020) · PeerJ · doi:10.7717/peerj.10119 · Cited by 50 · see citing works
A method for achieving complete microbial genomes and improving bins from metagenomics data
Lui, L., Nielsen, T., & Arkin, A. (2020) · bioRxiv · doi:10.1371/journal.pcbi.1008972 · Cited by 37 · see citing works
GUNC: Detection of Chimerism and Contamination in Prokaryotic GenomesPreprint
Orakov, A. N., Fullam, A., Coelho, L. P., Khedkar, S., Szklarczyk, D., Mende, D. R., Schmidt, T., & Peer Bork, B. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.12.16.422776 · Cited by 24 · see citing works
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Research works printed but not linked: 1
- Abadi, M. et al. Tensorflow: A system for large-scale machine learning. in 12th USENIX symposium on operating systems design and implementation (OSDI 16) 265-283 (2016).
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Gunjur, A., Shao, Y., Rozday, T., Klein, O., Mu, A., Haak, B. W., Markman, B., Kee, D., Carlino, M. S., Underhill, C., et al. (2024) · Nature Medicine · doi:10.1038/s41591-024-02823-z · Cited by 165 · see citing works
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Ortigossa, E. S., Gonçalves, T., & Nonato, L. G. (2024) · IEEE Access · doi:10.1109/access.2024.3409843 · Cited by 124 · see citing works
Han, X., Liu, Q., Li, Y., Zhang, M., Liu, K., Kwok, L.-Y., Zhang, H., & Zhang, W. (2025) · Trends in Food Science & Technology · doi:10.1016/j.tifs.2025.104938 · Cited by 64 · see citing works
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Proctor, D. M., Sansom, S. E., Deming, C., Conlan, S., Blaustein, R. A., Atkins, T. K., Program, N. C. S., Mullikin, J., Thomas, J., Young, A., et al. (2025) · Nature · doi:10.1038/s41586-025-08608-9 · Cited by 37 · see citing works
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Li, Q., Huo, J., Ni, G., Zhang, F., Zhang, S., Zhang, X., Wang, R., Jiao, J., Yu, Z., Pu, X., et al. (2025) · Microbiome · doi:10.1186/s40168-024-02018-1 · Cited by 22 · see citing works
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Shoer, S., Reicher, L., Zhao, C., Pollard, K. S., Pilpel, Y., & Segal, E. (2024) · Cell Host & Microbe · doi:10.1016/j.chom.2024.08.017 · Cited by 23 · see citing works
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- Authors
- Chklovski, A., Parks, D. H., Woodcroft, B. J., & Tyson, G. W.
- Affiliations
- Translational Research Institute (AU)
- Corresponding author
- Tyson, Gene W.
- Venue
- Nature Methods
- Published
- 2023-07-27
- Volume
- 20
- Issue
- 8
- Pages
- 1203-1212
- ISSN
- 1548-7091 (ISSN-L)
- 1548-7105
- OpenAlex
- W4385302529
- Cited by
- 1,705
- References
- 43
- Citations by year
- 2026: 810 · 2025: 526 · 2024: 297 · 2023: 64 · 2022: 6 · 2021: 1 · 2020: 1
- FWCI
- 152.73
- Type
- journal article
- Language
- en
- Update notices
- correction (2024-03-21)
- Open access
- green
- doi.org
- Licence
- Locations
- Nature Methods (publishedVersion)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nd)
- PubMed (publishedVersion)
- QUT ePrints (Queensland University of Technology) (submittedVersion)
- Funders
- National Science Foundation (2022070)
- McMaster University
- Australian Government
- Queensland University of Technology
- Australian Research Council (FT170100070, DE160100248)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
- Physical Sciences › Environmental Science › Ecology › Microbial Community Ecology and Physiology
- Physical Sciences › Environmental Science › Ecology › Environmental DNA in Biodiversity Studies
- Keywords
- metagenome-assembled genomes, genome quality assessment, microbial genomes, machine learning, reference genome database, Patescibacteria, novel lineages, archaea, bacterial genomes
- MeSH terms
- Machine Learning, Bacteria, Metagenome, Metagenomics, Genome, Microbial
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