Birth, Death, and Diversification of Mobile Promoters in Prokaryotes
Bibliographic record
Abstract
A previous study of prokaryotic genomes identified large reservoirs of putative mobile promoters (PMPs), that is, homologous promoter sequences associated with nonhomologous coding sequences. Here we extend this data set to identify the full complement of mobile promoters in sequenced prokaryotic genomes. The expanded search identifies nearly 40,000 PMP sequences, 90% of which occur in noncoding regions of the genome. To gain further insight from this data set, we develop a birth-death-diversification model for mobile genetic elements subject to sequence diversification; applying the model to PMPs we are able to quantify the relative importance of duplication, loss, horizontal gene transfer (HGT), and diversification to the maintenance of the PMP reservoir. The model predicts low rates of HGT relative to the duplication and loss of PMP copies, rapid dynamics of PMP families, and a pool of PMPs that exist as a single copy in a genome at any given time, despite their mobility. We report evidence of these "singletons" at high frequencies in prokaryotic genomes. We also demonstrate that including selection, either for or against PMPs, was not necessary to describe the observed data.
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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".