The pangenome enhances the understanding of the genetic diversity of papaya
Bibliographic record
Abstract
Abstract Papaya (Carica papaya L.) is a nutritionally and medicinally important tropical fruit crop, yet its genetic improvement has been limited by insufficient genomic resources. In this study, we constructed chromosome-level genomes for three key varieties (Zhufeng, T3, and T5) and integrated them with three existing assemblies to build a comprehensive pangenome, including graph-based, linear, and syntelog-based representations. The syntelog-based pangenome revealed 24 453 syntelog groups (SGs). Leveraging resequencing data from 222 accessions aligned to the graph-based pangenome, we identified 26 173 structural variations (SVs), including a functionally relevant 94-bp deletion in the RETARDED ROOT GROWTH (RRG) gene in the T3 genome. This deletion affects the expression of the RRG, resulting in a reduction in its expression level in T3. Further phenotypic analysis showed that RRG can influence papaya root length by promoting the proliferation of root meristem cells and inhibiting cell elongation. Additionally, the linear pangenome uncovered 5273 translocations and 1440 inversions, significantly expanding the known SV repertoire in papaya. This study provides a critical genomic resource for deciphering domestication-related traits and accelerating marker-assisted breeding, ultimately advancing the genetic improvement of papaya.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".