Transcriptome Sequencing Analysis of Flowering Related Genes in <i>Prunus sibirica</i>
Bibliographic record
Abstract
In order to explore the molecular mechanism of regulating flowering in Prunus sibirica , the transcriptome of flower buds at two different stages was sequenced to investigate the candidate genes involved in the flowering. A total of 42.04 Gb clean data were obtained, and more than 92.00% of the clean reads were mapped to the reference genome. A total of 6 850 significantly differentially expressed genes (DEGs) were found, including 2 784 significantly up-regulated and 4 066 significantly down regulated genes. We further found 392 and 346 DEGs were special in sprouting stage and full-bloom stage, respectively. KEGG enrichment analysis showed that the first three DEGs were enriched in plant hormone signal transduction, phenylpropanoid biosynthesis, and starch and sucrose metabolism, respectively. Among these DEGs, 39 genes involved in the flowering regulation pathways were obtained, including 10, 17, 5, 1, and 2 genes in vernalization pathway, photoperiod pathway, autonomous pathway, gibberellin pathway, and temperature pathway, respectively. In addition, four integrators, including SOC1 , FT , and LFY , were also identified. This study will provide the valuable information for studying the candidate genes involved in the flowering and for breeding in P. sibirica .
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| 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".