CheckM2: a rapid, scalable and accurate tool for assessing microbial genome quality using machine learning
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Advances in DNA sequencing and bioinformatics have dramatically increased the rate of recovery of microbial genomes from metagenomic data. Assessing the quality of metagenome-assembled genomes (MAGs) is a critical step prior to downstream analysis. Here, we present CheckM2, an improved method of predicting the completeness and contamination of MAGs using machine learning. We demonstrate the effectiveness of CheckM2 on synthetic and experimental data, and show that it outperforms the original version of CheckM in predicting MAG quality. CheckM2 is substantially faster than CheckM and its database can be rapidly updated with new high-quality reference genomes. We show that CheckM2 accurately predicts genome quality for MAGs from novel lineages, even those with sparse genomic representation, or reduced genome size (e.g. symbionts) such as those found in the Patescibacteria and the DPANN superphylum. CheckM2 provides accurate genome quality predictions across the microbial tree of life, giving increased confidence when inferring novel biological conclusions from MAGs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it