Optimal conditions and specific characteristics of <i>Vent</i> exo– DNA polymerase in ligation-mediated polymerase chain reaction protocols
Bibliographic record
Abstract
An optimized procedure for the ligation-mediated polymerase chain reaction (PCR) technique using Thermococcus litoralis exo- DNA polymerase (Vent exo-) was developed. The optimal dosage of Vent exo- at the primer extension and PCR amplification steps as well as the optimal DNA quantity to use were established. We showed that Vent exo- can efficiently create the blunt-ended termini required for subsequent linker ligation. Vent exo- proves to be more efficient than Pyrococcus furiosus exo- (Pfu exo-) for this task. Vent exo- resolves highly GC-rich sequence substantially better than Thermus aquaticus DNA polymerase (Taq) and with a similar efficiency as Pfu exo-. The DNA/DNA polymerase activity ratio is significantly higher for Vent exo- than for Pfu exo-, which is reflected by the sensibility of Vent exo- in efficiently amplifying genomic DNA. Furthermore, the range of efficiency of Vent exo- demonstrates the importance of conducting evaluative testing to identify the optimal dosage of use of this polymerase to obtain successful PCR amplification. Optimal MgSO4 concentrations to use with Vent exo- were established. Our results show that Vent exo- DNA polymerase produces bands of uniform and strong intensity and can efficiently be used for the analysis of DNA in living cells by ligation-mediated PCR.
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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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.010 |
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".