Instability of bacterial artificial chromosome (BAC) clones containing tandemly repeated DNA sequences
Bibliographic record
Abstract
The cloning and propagation of large DNA fragments as bacterial artificial chromosomes (BACs) has become a valuable technique in genome research. BAC clones are highly stable in the host, Escherichia coli, a major advantage over yeast artificial chromosomes (YACs) in which recombination-induced instability is a major drawback. Here we report that BAC clones containing tandemly repeated DNA elements are not stable and can undergo drastic deletions during routine library maintenance and DNA preparation. Instability was observed in three BAC clones from sorghum, rice, and potato, each containing distinct tandem repeats. As many as 46% and 74% of the single colonies derived from a rice BAC clone containing 5S ribosomal RNA genes had insert deletions after 24 and 120 h of growth, respectively. We also demonstrated that BAC insert rearrangement can occur in the early stage of library construction and duplication. Thus, a minimum growth approach may not avoid the instability problem of such clones. The impact of BAC instability on genome research is discussed.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".