Time- and Cost-Efficient Identification of T-DNA Insertion Sites through Targeted Genomic Sequencing
Bibliographic record
Abstract
Forward genetic screens enable the unbiased identification of genes involved in biological processes. In Arabidopsis, several mutant collections are publicly available, which greatly facilitates such practice. Most of these collections were generated by agrotransformation of a T-DNA at random sites in the plant genome. However, precise mapping of T-DNA insertion sites in mutants isolated from such screens is a laborious and time-consuming task. Here we report a simple, low-cost and time efficient approach to precisely map T-DNA insertions simultaneously in many different mutants. By combining sequence capture, next-generation sequencing and 2D-PCR pooling, we developed a new method that allowed the rapid localization of T-DNA insertion sites in 55 out of 64 mutant plants isolated in a screen for gyrase inhibition hypersensitivity.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".