Bibliographic record
Abstract
The genetic stability of tandemly repeated DNAs is affected by repeat sequence, tract length, tract purity, and replication direction. Alterations in DNA methylation status are thought to influence many processes of mutagenesis. By use of bacterial and primate cell systems, we have determined the effect of CpG methylation on the genetic stability of cloned di-, tri-, penta- and minisatellite repeated DNA sequences. Depending on the repeat sequence, methylation can significantly enhance or reduce its genetic stability. This effect was evident when repeat tracts were replicated from either direction. Unexpectedly, methylation of adjacent sequences altered the stability of contiguous repeat sequences void of methylatable sites. Of the seven repeat sequences investigated, methylation stabilized five, destabilized one, and had no effect on another. Thus, although methylation generally stabilized repeat tracts, its influence depended on the sequence of the repeat. The current results lend support to the notion that the biological consequences of CpG methylation may be affected through local alterations of DNA structure as well as through direct protein-DNA interactions. In vivo CpG methylation in bacteria may have technical applications for the isolation and stable propagation of DNA sequences that have been recalcitrant to isolation and/or analyses because of their extreme instability.
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.
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 | 0.000 | 0.001 |
| 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) | 0.001 | 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 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".