Wavelet to predict bacterial ori and ter: a tendency towards a physical balance
Bibliographic record
Abstract
BACKGROUND: Chromosomal DNA replication in bacteria starts at the origin (ori) and the two replicores propagate in opposite directions up to the terminus (ter) region. We hypothesize that the two replicores need to reach ter at the same time to maintain a physical balance; DNA insertion would disrupt such a balance, requiring chromosomal rearrangements to restore the balance. To test this hypothesis, we needed to demonstrate that ori and ter are in a physical balance in bacterial chromosomes. Using wavelet analysis, we documented GC skew, AT skew, purine excess and keto excess on the published bacterial genomic sequences to locate the turning (minimum and maximum) points on the curves. Previously, the minimum point had been supposed to correlate with ori and the maximum to correlate with ter. RESULTS: We observed a strong tendency of the bacterial chromosomes towards a physical balance, with the minima and maxima corresponding to the known or putative ori and ter and being about half chromosome separated in most of the bacteria studied. A nonparametric method based on wavelet transformation was employed to perform significance tests for the predicted loci. CONCLUSIONS: The wavelet approach can reliably predict the ori and ter regions and the bacterial chromosomes have a strong tendency towards a physical balance between ori and ter.
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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".