Simple in vitro single-stranded linear and circular DNA preparation, functional selection, and validation using phosphor-derived modifications
Bibliographic record
Abstract
Interest in the preparation of circular ssDNA library has been increasing recently; therefore, developing a simple and an efficient method for circular DNA generation will be very useful for all procedures and techniques that are dependent on circular ssDNA preparation. In this study, a new simple method for in vitro preparation of circular ssDNA is proposed. We hypothesized that using a phosphorylated-phosphorothioated primer would not affect the efficiency of PCRs but, more importantly, would suppress the activity of the lambda exonuclease enzyme even if it is phosphorylated. The produced phosphorylated ssDNA is ready to be circularized via a ligation reaction using a bridging oligonucleotide. Several optimizations and enhancements have been conducted in the ligation reaction, notably by embedding an extra thymine nucleotide at the ligation site to compensate for the additional adenosine nucleotide added by Taq during the PCR. In addition, the performance of the proposed method has been validated by selecting linear and circular aptamers against Middle East respiratory syndrome coronavirus spike protein during 15 successive cycles of SELEX. Because this new method is simple and user friendly, it has a potential to be automated for high-throughput purposes and may further stir growing interests in preparation of circular ssDNA and its 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".