AFEAP cloning: a precise and efficient method for large DNA sequence assembly
Bibliographic record
Abstract
BACKGROUND: Recent development of DNA assembly technologies has spurred myriad advances in synthetic biology, but new tools are always required for complicated scenarios. Here, we have developed an alternative DNA assembly method named AFEAP cloning (Assembly of Fragment Ends After PCR), which allows scarless, modular, and reliable construction of biological pathways and circuits from basic genetic parts. METHODS: The AFEAP method requires two-round of PCRs followed by ligation of the sticky ends of DNA fragments. The first PCR yields linear DNA fragments and is followed by a second asymmetric (one primer) PCR and subsequent annealing that inserts overlapping overhangs at both sides of each DNA fragment. The overlapping overhangs of the neighboring DNA fragments annealed and the nick was sealed by T4 DNA ligase, followed by bacterial transformation to yield the desired plasmids. RESULTS: We characterized the capability and limitations of new developed AFEAP cloning and demonstrated its application to assemble DNA with varying scenarios. Under the optimized conditions, AFEAP cloning allows assembly of an 8 kb plasmid from 1-13 fragments with high accuracy (between 80 and 100%), and 8.0, 11.6, 19.6, 28, and 35.6 kb plasmids from five fragments at 91.67, 91.67, 88.33, 86.33, and 81.67% fidelity, respectively. AFEAP cloning also is capable to construct bacterial artificial chromosome (BAC, 200 kb) with a fidelity of 46.7%. CONCLUSIONS: AFEAP cloning provides a powerful, efficient, seamless, and sequence-independent DNA assembly tool for multiple fragments up to 13 and large DNA up to 200 kb that expands synthetic biologist's toolbox.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.003 |
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".