Mapping-by-Sequencing via eBSRmap (Easy Bulk Segregate RNA Mapping) in a B73 EMS Mutant Population
Bibliographic record
Abstract
BACKGROUND: Maize, crucial for food, feed, and industry, is a model for genetic and breeding research. Kernel traits directly affect maize yield. This study developed the eBSRmap method to simplify gene cloning related to maize kernel traits. METHODS: The eBSRmap method constructs a maize EMS mutant population, then conducts RNA-seq on pooled mutant and wild-type samples to identify SNP markers and map candidate genes for kernel trait mutations. RESULTS: Applied to a maize EMS mutant population, eBSRmap identified candidate genes for twenty kernel trait mutants, successfully mapped twelve, and eight were confirmed by co-segregation analysis (success rate: 40%). The identified genes showed mutations like missense and stop-gained, related to phenotypes such as small, shrunk, and defective kernels. CONCLUSIONS: eBSRmap offers a fast and affordable way to map genes and identify candidate genes in a large-scale mutant population, aiding the understanding of gene functions in maize. The identified candidate genes can be further validated by functional analysis, which is significant for maize breeding and genetic improvement.
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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".