Fast genome-based species delimitation: Enterobacterales and beyond
Bibliographic record
Abstract
Average Nucleotide Identity (ANI) is becoming a standard measure for bacterial species delimitation. However, its calculation can take orders of magnitude longer than fast similarity estimates based on sampling of short nucleotides, compiled into so-called sketches. These estimates are widely used and correlate well with ANI. However, they might not be as accurate. Thus, we compared two sketching programs, mash and dashing, against ANI, in delimiting species among publicly available Esterobacterales genomes. Receiver Operating Characteristic (ROC) curve analysis found all three programs to be highly accurate, with Area Under the Curve (AUC) values of 0.99, indicating almost perfect species discrimination. Subsampling to reduce over-represented species, reduced these AUC values to 0.92. Focused tests with ten genera represented by more than three species, also showed almost identical results for all methods. Shigella showed the lowest AUC values (0.68), followed by Citrobacter (0.80). All other genera, Dickeya, Enterobacter, Escherichia, Klebsiella, Pectobacterium, Proteus, Providencia and Yersinia , produced AUC values above 0.90. The species delimitation thresholds varied, with species distance ranges in a few genera overlapping the genus ranges of other genera. Mash was able to separate the E. coli + Shigella complex into 25 apparent phylogroups. Testing mash for species separation in genera outside Enterobacterales showed AUCs above 0.95, again with different thresholds for species delimitation within each genus. Overall, our results suggest that fast estimates of genome similarity are as good as ANI for species delimitation. Therefore, these fast estimates might suffice for determining the role of genomic similarity in bacterial taxonomy.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".