Selectable marker genes and unintended changes to the plant transcriptome
Bibliographic record
Abstract
The intended effect of a selectable marker gene is to confer a novel trait that allows for the selection and recovery of transgenic plants. Unintended effects may also occur as a result of interactions between the selectable marker gene or its regulatory elements and genetic elements at the site of insertion. These are called position effects. Other unintended effects may occur if the selectable marker gene has a range of pleiotropic effects related to the functional and regulatory domains within the coding region or the regulatory elements used to drive expression. Both pleiotropic and position effects may generate unpredictable events depending on the process used for transgenesis and the state of knowledge associated with the selectable marker gene. Although some selectable marker genes, such as the neomycin phosphotransferase type II gene (nptII), have no pleiotropic effects on the transcriptomes of transgenic plants, others, such as the bialaphos resistance gene (bar), have pleiotropic effects. These must be clearly understood and accounted for when evaluating the expression patterns conferred by other co-transforming transgenes under study. The number and kinds of selectable marker genes are large. A detailed understanding of their unintended effects is needed to develop transgenic strategies that will minimize or eliminate unintended and unpredictable changes to plants with newly inserted genes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.004 |
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".