Targeted exome sequencing of unselected heavy‐ion beam‐irradiated populations reveals less‐biased mutation characteristics in the rice genome
Bibliographic record
Abstract
Summary Heavy‐ion beams have been widely utilized as a novel and effective mutagen for mutation breeding in diverse plant species, but the induced mutation spectrum is not fully understood at the genome scale. We describe the development of a multiplexed and cost‐efficient whole‐exome sequencing procedure in rice, and its application to characterize an unselected population of heavy‐ion beam‐induced mutations. The bioinformatics pipeline identified single‐nucleotide mutations as well as small and large (>63 kb) insertions and deletions, and showed good agreement with the results obtained with conventional polymerase chain reaction (PCR) and sequencing analyses. We applied the procedure to analyze the mutation spectrum induced by heavy‐ion beams at the population level. In total, 165 individual M2 lines derived from six irradiation conditions as well as eight pools from non‐irradiated ‘Nipponbare’ controls were sequenced using the newly established target exome sequencing procedure. The characteristics and distribution of carbon‐ion beam‐induced mutations were analyzed in the absence of bias introduced by visual mutant selections. The average (±SE) number of mutations within the target exon regions was 9.06 ± 0.37 induced by 150 Gy irradiation of dry seeds. The mutation frequency changed in parallel to the irradiation dose when dry seeds were irradiated. The total number of mutations detected by sequencing unselected M2 lines was correlated with the conventional mutation frequency determined by the occurrence of morphological mutants. Therefore, mutation frequency may be a good indicator for sequencing‐based determination of the optimal irradiation condition for induction of mutations.
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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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".