Chromosome-level genome assembly of a blood-fleshed peach ‘ZhongHuaXueTao’ provides new insights into fruit flesh color and maturation
Bibliographic record
Abstract
Peach ( Prunus persica ) is an economically significant Rosaceae fruit. While the genome of the ‘Lovell’ reference has been previously released, genome-level variations remain underexplored. Here, we present a chromosome-level genome assembly of a blood-fleshed peach ‘ZhongHuaXueTao’ (‘ZHXT’), an ancient landrace primarily cultivated in central China. The ‘ZHXT’ genome spans 253.28 Mb with a scaffold N50 of 29.37 Mb. Genome comparison with the ‘Lovell’ reference genome uncovered 385 369 single nucleotide polymorphisms (SNPs), 103 395 insertions, 54 512 deletions, and 6 250 structural variants. Gene family analysis highlighted expansions in transmembrane transporter activity and immune response. RNA-seq analyses demonstrated distinct expression patterns of anthocyanin biosynthesis genes ( PpUFGT-1 , PpANS , PpPAL and PpGST) in ‘ZHXT’ compared with white- and yellow-fleshed peaches. Alignment of 338 peach accessions against the ‘ZHXT’ genome identified 11 513 248 SNPs and 1 539 000 InDels markers. Genome-wide association analysis (GWAS) pinpointed multiple genetic loci and candidate genes for five agronomic traits, including fruit flesh color, fruit shape, flesh color around the stone, fruit mature date, and fruit development period. PpMDL1 , PpCCD4 , and PpGTPase were identified as candidate genes for the blood-fleshed trait, while PpUVR8 and PpNAC5 genes were identified as candidates for the fruit mature date trait. Genotype frequency and expression analysis further confirmed the reliability of these trait loci. In general, these genomic resources will enhance our understanding of genetic diversity and facilitate functional genomics in peach breeding and improvement.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".