A super pan-genomic landscape of rice
Bibliographic record
Abstract
Pan-genomes from large natural populations can capture genetic diversity and reveal genomic complexity. Using de novo long-read assembly, we generated a graph-based super pan-genome of rice consisting of a 251-accession panel comprising both cultivated and wild species of Asian and African rice. Our pan-genome reveals extensive structural variations (SVs) and gene presence/absence variations. Additionally, our pan-genome enables the accurate identification of nucleotide-binding leucine-rich repeat genes and characterization of their inter- and intraspecific diversity. Moreover, we uncovered grain weight-associated SVs which specify traits by affecting the expression of their nearby genes. We characterized genetic variants associated with submergence tolerance, seed shattering and plant architecture and found independent selection for a common set of genes that drove adaptation and domestication in Asian and African rice. This super pan-genome facilitates pinpointing of lineage-specific haplotypes for trait-associated genes and provides insights into the evolutionary events that have shaped the genomic architecture of various rice species.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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) | 0.002 | 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".