ArticleNature methods2023
CheckM2: a rapid, scalable and accurate tool for assessing microbial genome quality using machine learning.
Article in Nature methods, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 1,037 papers, 1 of them a synthesis that pooled it.
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Who cites it
1,037 citing papers in PubMed, 1 synthesis or guideline pooled it.
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- Genomic global epidemiology and evolution ofMicrobial genomics · 2026Article
- Functional Divergence in Gut Clostridia from Hadza Hunter-Gatherers Reveals Diet-Adapted Metabolic Machinery That Shapes Gut Ecology.bioRxiv : the preprint server for biology · 2026Article
- Integrative genomic and phenotypic analyses reveal broad-spectrum antifungal activity and biocontrol potential in Bacillus ayatagriensis.Functional & integrative genomics · 2026Article
- Fine-scale structural diversification among closely relatedApplied and environmental microbiology · 2026Article
977 more citing papers are in PubMed but not listed here.
Corrections and comments
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- Erratum issued
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
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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Registered trials
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