De Novo Transcriptome Analysis Reveals Flowering-Related Genes That Potentially Contribute to Flowering-Time Control in the Japanese Cultivated Gentian Gentiana triflora
Bibliographic record
Abstract
Japanese cultivated gentians are perennial plants that flower in early summer to late autumn in Japan, depending on the cultivar. Several flowering-related genes, including GtFT1 and GtTFL1, are known to be involved in regulating flowering time, but many such genes remain unidentified. In this study, we obtained transcriptome profiling data using the Gentiana triflora cultivar ‘Maciry’, which typically flowers in late July. We conducted deep RNA sequencing analysis using gentian plants grown under natural field conditions for three months before flowering. To investigate diurnal changes, the plants were sampled at 4 h intervals over 24 h. Using these transcriptome data, we determined the expression profiles of leaves based on homology searches against the Flowering-Interactive Database of Arabidopsis. In particular, we focused on transcription factor genes, belonging to the BBX and MADS-box families, and analyzed their developmental and diurnal variation. The expression levels of representative BBX genes were also analyzed under long- and short-day conditions using in-vitro-grown seedlings, and the expression patterns of some BBX genes differed. Clustering analysis revealed that the transcription factor genes were coexpressed with GtFT1. Overall, these expression profiles will facilitate further analysis of the molecular mechanisms underlying the control of flowering time in gentians.
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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) | 0.000 | 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".