TILLING in the botanical garden: a reverse genetic technique feasible for all plant species.
Bibliographic record
Abstract
Targeting Induced Local Lesions IN Genomes (TILLING) is a powerful technology that employs heteroduplex analysis to detect which organisms in a population carry single nucleotide mutations in specific genes. Genes are amplified by PCR using pooled genomic DNA from several individuals as a template. Following denaturation and renaturation of the amplified DNA, heteroduplexes form if organisms with wild type and mutant sequence are both present in the pool. The heteroduplexes can be detected by cleavage with an endonuclease and resolution of the resulting fragments on a sequencing gel. TILLING can be an effective reverse genetic technique if it is used to screen populations mutagenized with chemical mutagens such as ethyl methane sulfonate (EMS). Since such mutagens induce a diverse array of mutant alleles at a high frequency in any organism without the need of transgenic technology, TILLING is more versatile, universal and requires a smaller mutagenized population than other reverse genetic methods. TILLING can also be used to detect naturally occurring single nucleotide polymorphisms (SNP’s) in genes among accessions, varieties, ecotypes or cultivars. These SNP’s can serve as genetic markers in mapping, breeding and genotyping and can provide information concerning gene structure, linkage disequilibrium, population structure or adaptation.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".