Whole-genome re-sequencing reveals genome-wide variations between the peach variety Green No. 9 and its bud mutant Daifei
Bibliographic record
Abstract
Whole-genome sequencing technologies provide opportunities to further understand genetic variation among different varieties. Some related genes that are useful for the breeding process could be identified by sequencing technologies. In this study, two peach varieties, Green No. 9 (LH) and its bud mutant Daifei (DT), were analyzed with whole-genome re-sequencing. Approximately 109 million total reads were generated, which covered ∼89% of the peach reference genome. A total of 1 143 757 single nucleotide polymorphisms (SNPs), 169 827 insertion–deletion mutations (InDels), 17 132 structural variations (SVs) and 5 040 copy number variations (CNVs) were detected in Green No. 9 and Daifei. Variant genes were classified by Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) metabolic pathway database. Detected genes such as LOC18768059 (carbohydrate metabolism) and LOC18785045 (anthocyanin biosynthesis), were good candidate genes for exploring the phenotypic variations between Green No. 9 and Daifei. Green No. 9 and its bud mutant Daifei showed obvious differences in phenotypes and variant genetic loci. The detected genomic variations will contribute to explorations of important functional genes and to understanding the genetic basis of peach bud mutations.
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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) | 0.000 | 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) | 0.001 | 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".