Progress towards a reference genome for sunflower
Bibliographic record
Abstract
The Compositae is one of the largest and most economically important families of flowering plants and includes a diverse array of food crops, horticultural crops, medicinals, and noxious weeds. Despite its size and economic importance, there is no reference genome sequence for the Compositae, which impedes research and improvement efforts. We report on progress toward sequencing the 3.5 Gb genome of cultivated sunflower ( Helianthus annuus ), the most important crop in the family. Our sequencing strategy combines whole-genome shotgun sequencing using the Solexa and 454 platforms with the generation of high-density genetic and physical maps that serve as scaffolds for the linear assembly of whole-genome shotgun sequences. The performance of this approach is enhanced by the construction of a sequence-based physical map, which provides unique sequence-based tags every 5–6 kb across the genome. Thus far, our physical map covers ∼85% of the sunflower genome, and we have generated ∼80× genome coverage with Solexa reads and 15.5× with 454 reads. Preliminary analyses indicated that ∼78% of the sunflower genome consists of repetitive sequences. Nonetheless, ∼76% of contigs >5 kb in size can be assigned to either the physical or genetic map or to both, suggesting that our approach is likely to deliver a highly accurate and contiguous reference genome for sunflower.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".