Multi-omics sequencing provides insight into floral transition in Catalpa bungei. C.A. Mey
Bibliographic record
Abstract
BACKGROUND: Floral transition plays an important role in development, and proper time is necessary to improve the value of valuable ornamental trees. The molecular mechanisms of floral transition remain unknown in perennial woody plants. "Bairihua" is a type of C. bungei that can undergo floral transition in the first planting year. RESULTS: Here, we combined short-read next-generation sequencing (NGS) and single-molecule real-time (SMRT) sequencing to provide a more complete view of transcriptome regulation during floral transition in C. bungei. The circadian rhythm-plant pathway may be the critical pathway during floral transition in early flowering (EF) C. bungei, according to horizontal and vertical analysis in EF and normal flowering (NF) C. bungei. SBP and MIKC-MADS-box were seemingly involved in EF during floral transition. A total of 61 hub genes were associated with floral transition in the MEturquoise model with Weighted Gene Co-expression Network Analysis (WGCNA). The results reveal that ten hub genes had a close connection with the GASA homologue gene (Cbu.gene.18280), and the ten co-expressed genes belong to five flowering-related pathways. Furthermore, our study provides new insights into the complexity and regulation of alternative splicing (AS). The ratio or number of isoforms of some floral transition-related genes is different in different periods or in different sub-genomes. CONCLUSIONS: Our results will be a useful reference for the study of floral transition in other perennial woody plants. Further molecular investigations are needed to verify our sequencing data.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".