Chromosome-level genome assembly and population genomics reveals crucial selection for subgynoecy development in chieh-qua
Bibliographic record
Abstract
Abstract Chieh-qua is an important cucurbit crop and very popular in South China and Southeast Asia. Despite its significance, its genetic basis and domestication history are unclear. In this study, we have successfully generated a chromosome-level reference genome assembly for the chieh-qua ‘A36’ using a hybrid assembly strategy that combines PacBio long reads and Illumina short reads. The assembled genome of chieh-qua is approximately 953.3 Mb in size and is organized into 12 chromosomes, with contig N50 of 6.9 Mb and scaffold N50 of 68.2 Mb. Notably, the chieh-qua genome is comparable in size to the wax gourd genome. Through gene prediction analysis, we have identified a total of 24 593 protein-coding genes in the A36 genome. Additionally, approximately 56.6% (539.3 Mb) of the chieh-qua genome consists of repetitive sequences. Comparative genome analysis revealed that chieh-qua and wax gourd are closely related, indicating a close evolutionary relationship between the two species. Population genomic analysis, employing 129 chieh-qua accessions and 146 wax gourd accessions, demonstrated that chieh-qua exhibits greater genetic diversity compared to wax gourd. We also employed the GWAS method to identify related QTLs associated with subgynoecy, an interested and important trait in chieh-qua. The MYB59 (BhiCQ0880026447) exhibited relatively high expression levels in the shoot apex of four subgynoecious varieties compared with monoecious varieties. Overall, this research provides insights into the domestication history of chieh-qua and offers valuable genomic resources for further molecular research.
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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".