Lifting of the 1,000 wheat exome project SNPs from Triticum aestivum cv. Chinese Spring assembly RefSeq v1.0 to RefSeq v2.1
Bibliographic record
Abstract
OBJECTIVE: The 1,000 wheat exome project captured the single nucleotide variants in the coding regions of a diverse set of 890 wheat accessions to analyse the contribution of introgression to adaptation of wheat. However, this highly useful single nucleotide polymorphism (SNP) dataset is based on RefSeq v1.0 of the International Wheat Genome Sequencing Consortium (IWGSC) assembly of the bread wheat genome of Chinese Spring. This reference sequence has recently been updated using optical maps and long-read sequencing to produce the improved RefSeq v2.1. Our objective was to develop a reliable high-density SNP dataset positioned onto RefSeq v2.1 because it is the current standard reference sequence used by wheat researchers. RESULTS: The 3,039,822 SNPs originally positioned on RefSeq v1.0 were projected to v2.1 using Liftoff with four different flanking regions, and 2,946,536 SNPs were consistently lifted to the same location irrespective of the flanking region lengths. Of these, 2,799,166 were located on the '+' ve strand. The distribution of the SNPs across the 21 chromosomes on RefSeq v2.1 was similar to that of RefSeq v1.0. Among the SNPs that were based on unanchored scaffolds in RefSeq v1.0, 11,938 were projected to one of the 21 pseudomolecules in the upgraded assembly. This SNP dataset constitutes a much-needed standardized resource for the wheat research community.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.005 |
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".