Deleteagene: a fast neutron deletion mutagenesis‐based gene knockout system for plants
Bibliographic record
Abstract
Deleteagene (Delete-a-gene) is a deletion-based gene knockout system for plants. To obtain deletion mutants for a specific gene, random deletion libraries created by fast neutron mutagenesis are screened by polymerase chain reaction (PCR) using primers flanking the target gene. By adjusting the PCR extension time to preferentially amplify the deletion alleles, deletion mutants can be identified in pools of DNA samples with each sample representing more than a thousand mutant lines. In Arabidopsis, knockout plants for greater than 80% of targeted genes have been obtained from a population of 51,840 lines. A large number of deletion mutants have been identified and multiple deletion alleles are often recovered for targeted loci. In Arabidopsis, the method is very useful for targeting small genes and can be used to find deletion mutants mutating two or three tandem homologous genes. In addition, the method is demonstrated to be effective in rice as a deletion mutant for a rice gene was obtained with a similar approach. Because fast neutron mutagenesis is applicable to all plant genetic systems, Deleteagene has the potential to enable reverse genetics for a wide range of plant species.
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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) | 0.002 | 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) | 0.005 | 0.006 |
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".