A unified multi-kingdom Golden Gate cloning platform
Bibliographic record
Abstract
Assembling composite DNA modules from custom DNA parts has become routine due to recent technological breakthroughs such as Golden Gate modular cloning. Using Golden Gate, one can efficiently assemble custom transcription units and piece units together to generate higher-order assemblies. Although Golden Gate cloning systems have been developed to assemble DNA plasmids required for experimental work in model species, they are not typically applicable to organisms from other kingdoms. Consequently, a typical molecular biology laboratory working across kingdoms must use multiple cloning strategies to assemble DNA constructs for experimental assays. To simplify the DNA assembly process, we developed a multi-kingdom (MK) Golden Gate assembly platform for experimental work in species from the kingdoms Fungi, Eubacteria, Protista, Plantae, and Animalia. Plasmid backbone and part overhangs are consistent across the platform, saving both time and resources in the laboratory. We demonstrate the functionality of the system by performing a variety of experiments across kingdoms including genome editing, fluorescence microscopy, and protein interaction assays. The versatile MK system therefore streamlines the assembly of modular DNA constructs for biological assays across a range of model organisms.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.005 |
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".