PCR procedures to amplify GC-rich DNA sequences of <i>Mycobacterium bovis</i>
Bibliographic record
Abstract
Abstract Amplification of high GC content genes by PCR is a major challenge during the creation of recombinant GC-rich DNA constructs. This may be due to the difficulty in DNA denaturation or the possibility of forming secondary structures from DNA templates. Tools have been described to address the technical problems associated with the amplification of shorter sequences (<1000 bp). However, obstacles of synthesizing larger-sized GC-rich sequences by PCR continue to exist. This study aims to investigate the amplification of long and high GC content genes by PCR from the Mycobacterium bovis , a genome with GC content >60%, in comparison to amplifying a gene from the Listeria monocytogenes genome, a genome with a 37.8% GC content. Three PCR protocols were designed and experimented at various conditions with two M. bovis genes, Mb0129 , a large gene of 1794 bp with 77.5% GC content, mpb83 , a smaller gene of 663 bp in length with moderate GC content of 63%, together with LMHCC_RS00060 , a large L.monocytogenes gene of 1617 bp with a lower GC content of 41.53%. The result demonstrated the superiority of the 2-step PCR protocol over other protocols in PCR amplification of Mb0129 when specific high fidelity DNA polymerases were used in the presence of an enhancer. The study highlighted the importance of manipulating the cycling conditions to perform the annealing and extension steps at higher temperatures for a successful PCR amplification of a large GC-rich DNA template. The PCR protocols developed in this study can be valuable tools for the amplification of long GC-rich DNA sequences for various downstream applications.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.006 |
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".