A Rice Stowaway MITE for Gene Transfer in Yeast
Bibliographic record
Abstract
Miniature inverted repeat transposable elements (MITEs) lack protein coding capacity and often share very limited sequence similarity with potential autonomous elements. Their capability of efficient transposition and dramatic amplification led to the proposition that MITEs are an untapped rich source of materials for transposable element (TE) based genetic tools. To test the concept of using MITE sequence in gene transfer, a rice Stowaway MITE previously shown to excise efficiently in yeast was engineered to carry cargo genes (neo and gfp) for delivery into the budding yeast genome. Efficient excision of the cargo gene cassettes was observed even though the excision frequency generally decreases with the increase of the cargo sizes. Excised elements insert into new genomic loci efficiently, with about 65% of the obtained insertion sites located in genes. Elements at the primary insertion sites can be remobilized, frequently resulting in copy number increase of the element. Surprisingly, the orientation of a cargo gene (neo) on a construct bearing dual reporter genes (gfp and neo) was found to have a dramatic effect on transposition frequency. These results demonstrated the concept that MITE sequences can be useful in engineering genetic tools to deliver cargo genes into eukaryotic genomes.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".