GC-Rich Template Amplification by Inverse PCR: DNA Polymerase and Solvent Effects
Bibliographic record
Abstract
The amplification of GC-rich templates by any PCR method is usually a difficult task and despite the development of modified methods and conditions, this type of amplification still remains a specific case approach. Problems usually observed with GC-rich DNA are constraint of template amplification by stable secondary structures that stall or reduce the DNA polymerase progress, and the presence of secondary annealing sites giving rise to nonspecific amplified bands. This latter point is not exclusive to GC-rich templates but is frequently encountered in other types of templates. In order to design a more general method for GC-rich templates, different DNA polymerases were compared in combination with different organic solvents with the purpose of abolishing stable secondary structures (). Our attention focused on the inverse polymerase chain reaction (iPCR) used to perform site-directed mutagenesis ( ). This very attractive method req⪚res a single pair of primers and involves the amplification of the whole recombinant plasmid, a difficult step with high GC-content DNA. Inverse PCR also proves useful in cloning missing parts of genes by using a self-ligated genomic DNA fragment as template.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".