Whole-Genome Sequencing of Rice Mutant Library Members Induced by N-Methyl-N-Nitrosourea Mutagenesis of Fertilized Egg Cells
Bibliographic record
Abstract
Abstract Although targeted genome editing technology has become a powerful reverse genetic approach for accelerating functional genomics, conventional mutant libraries induced by chemical mutagens remain valuable for plant studies. Plants containing chemically induced mutations are simple yet effective genetic tools that can be grown without regard for biosafety issues. Whole-genome sequencing of mutant individuals reduces the effort required for mutant screening, thereby increasing their utility. In this study, we sequenced members of a mutant library of Oryza sativa cv. Nipponbare derived from treating single fertilized egg cells with N-methyl-N-nitrosourea (MNU). By whole-genome sequencing 266 M1 plants in this mutant library, we identified a total of 0.66 million induced point mutations. This result represented one mutation in every 146-kb of genome sequence in the 373 Mb assembled rice genome. These point mutations were uniformly distributed throughout the rice genome, and over 70,000 point mutations were located within coding sequences. Although this mutant library was a small population, nonsynonymous mutations were found in nearly 61% of all annotated rice genes, and 8.6% (3248 genes) had point mutations with large effects on gene function, such as gaining a stop codon or losing a start codon. WGS showed MNU-mutagenesis using rice fertilized egg cells induces mutations efficiently and is suitable for constructing mutant libraries for an in silico mutant screening system. Expanding this mutant library and its database will provide a useful in silico screening tool that facilitates functional genomics studies with a special emphasis on rice.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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 | 0.001 |
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".