Insertion site preference of Mu, Tn5, and Tn7 transposons
Bibliographic record
Abstract
BACKGROUND: Transposons, segments of DNA that can mobilize to other locations in a genome, are often used for insertion mutagenesis or to generate priming sites for sequencing of large DNA molecules. For both of these uses, a transposon with minimal insertion bias is desired to allow complete coverage with minimal oversampling. FINDINGS: Three transposons, Mu, Tn5, and Tn7, were used to generate insertions in the same set of fosmids containing Candida glabrata genomic DNA. Tn7 demonstrates markedly less insertion bias than either Mu or Tn5, with both Mu and Tn5 biased toward sequences containing guanosine (G) and cytidine (C). This preference of Mu and Tn5 yields less uniform spacing of insertions than for Tn7, in the adenosine (A) and thymidine (T) rich genome of C. glabrata (39% GC). CONCLUSIONS: In light of its more uniform distribution of insertions, Tn7 should be considered for applications in which insertion bias is deleterious.
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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 | 0.001 |
| 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".