The Flp double cross system a simple efficient procedure for cloning DNA fragments
Bibliographic record
Abstract
BACKGROUND: While conventional cloning methods using restriction enzymes and polynucleotide ligase are adequate for most DNAs, fragments made by the polymerase chain reaction are difficult to clone because the amplifying DNA polymerase tends to add untemplated nucleotides to the 3'-termini of the amplified strands. Conservative site-specific recombinases offer an efficient alternative to conventional cloning methods. RESULTS: In this paper I describe the use of the Flp site-specific recombinase for cloning PCR-amplified fragments. A DNA fragment is amplified with primers that contain at their ends inverted target sequences for Flp. Flp readily recombines these fragments in vitro into a vector that also contains two inverted Flp target sequences surrounding the alpha-complementing region of the lacZ gene of E. coli. The recombinants are conveniently detected as white colonies by the familiar blue/white screening test for lacZ activity. A useful feature of the system is that both orientations of the inserted DNA are usually obtained. If the recipient vector is cut between the two inverted Flp targets, Flp "heals" the double-strand break by inserting a linear fragment flanked by Flp targets. CONCLUSION: This system ("The Flp Double Cross System") should be useful for cloning multiple PCR fragments into many sites in several vectors. It has certain advantages over other available recombinase-based cloning procedures.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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) | 0.007 | 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".