Comparative Genomic Analysis of Wild Cymbidium Species from Fujian Using Whole-Genome Resequencing
Bibliographic record
Abstract
In this study, we performed whole-genome resequencing (WGS) to investigate genomic variation and functional divergence among four wild Cymbidium species—C. ensifolium, C. sinense, C. kanran, and C. floribundum—collected from Fujian Province, China. A total of 350.58 Gbp of high-quality sequencing data was obtained from 13 samples, enabling comprehensive identification of SNPs and InDels. Genomic variants were unevenly distributed, with lower variation in gene-rich regions and higher levels in non-coding areas. Circos plots and variant density heatmaps revealed significant regional differences across chromosomes, with longer chromosomes exhibiting greater variant enrichment in 1 Mb windows. C. floribundum harbored the highest number of nonsynonymous SNPs and InDel-associated genes, whereas C. sinense and C. kanran had fewer mutations. KEGG pathway enrichment analysis revealed species-specific functional divergence, particularly in metabolism, stress response, and secondary metabolite biosynthesis. Population structure analysis and principal component analysis (PCA) indicated genetic differentiation among these species Notably, C. kanran exhibited high within-population genetic diversity. These findings provide essential genomic resources for the conservation and functional studies of wild Cymbidium species in subtropical China.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 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".