Combined analysis and miRNA expression profiles of the flowering related genes in common wild rice (oryza rufipogon Griff.)
Bibliographic record
Abstract
Common wild rice (Oryza rufipogon Griff.) is the most closely related ancestral species to Asian cultivated rice (Oryza sativa L.). It contains various valuable traits with regard to tolerance to cold, drought and salinity, flowering diversity and many quantitative trait loci with agronomic important traits. Flowering is one of the most important agronomic traits. However, flowering-related transcriptome and how to be regulated by miRNAs have not been estimated in O.rufipogon. To identify how the genes and miRNAs regulating flowering in O.rufipogon. Three O.rufipogon RNA libraries, two vegetative stages (CWRT-V1 and CWRT-V2) and one flowering stage (CWRT-F2) were constructed using leaves tissue and sequenced using Illumina deep sequencing. 27,405, 27,333, 28,979 unique genes were obtained after mapping to the reference genome from CWRT-V1, CWRT-V2 and CWRT-F2, respectively. Then differentially expressed genes (DEGs) were screened and got 1419 unique genes are likely to involve in flower development. Detailed information showed that MADS box and floral meristem identity genes, such as MADS 1, MADS14, Hd1 are involved in common wild rice. Then, combined analysis of miRNA and mRNA expression profiles was performed. Twenty three known miRNA-mRNA pairs and five new candidates were presented an anti-correlationship. Interestingly, 12 miRNAs were negatively correlated with 20 mRNAs encoding flowering-related proteins, indicating that miRNAs regulated target genes to promote flowering in CWRT-F2 group. The results provided here genomic resources for flowering related genes and how these flowering genes were regulated by miRNAs in common wild rice.
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 | 0.000 |
| 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".