Testing the Infinitely Many Genes Model for the Evolution of the Bacterial Core Genome and Pangenome
Bibliographic record
Abstract
When groups of related bacterial genomes are compared, the number of core genes found in all genomes is usually much less than the mean genome size, whereas the size of the pangenome (the set of genes found on at least one of the genomes) is much larger than the mean size of one genome. We analyze 172 complete genomes of Bacilli and compare the properties of the pangenomes and core genomes of monophyletic subsets taken from this group. We then assess the capabilities of several evolutionary models to predict these properties. The infinitely many genes (IMG) model is based on the assumption that each new gene can arise only once. The predictions of the model depend on the shape of the evolutionary tree that underlies the divergence of the genomes. We calculate results for coalescent trees, star trees, and arbitrary phylogenetic trees of predefined fixed branch length. On a star tree, the pangenome size increases linearly with the number of genomes, as has been suggested in some previous studies, whereas on a coalescent tree, it increases logarithmically. The coalescent tree gives a better fit to the data, for all the examples we consider. In some cases, a fixed phylogenetic tree proved better than the coalescent tree at reproducing structure in the gene frequency spectrum, but little improvement was gained in predictions of the core and pangenome sizes. Most of the data are well explained by a model with three classes of gene: an essential class that is found in all genomes, a slow class whose rate of origination and deletion is slow compared with the time of divergence of the genomes, and a fast class showing rapid origination and deletion. Although the majority of genes originating in a genome are in the fast class, these genes are not retained for long periods, and the majority of genes present in a genome are in the slow or essential classes. In general, we show that the IMG model is useful for comparison with experimental genome data both for species level and widely divergent taxonomic groups. Software implementing the described formulae is provided at http://github.com/rec3141/pangenome.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".