Deciphering the role of a miRNA in rice domestication
Bibliographic record
Abstract
MicroRNAs (miRNAs) are a class of 21 nt non-coding small RNAs (sRNAs) produced from endogenously expressed MIR genes. miRNAs are mostly involved in development and disease resistance. We are interested in identifying key miRNAs that are differentially expressed among wild and cultivated rice species. Analysis of sRNA datasets from two wild species (O. nivara and O. rufipogon) and one cultivated species of rice (O. sativa var. indica Pusa Basmati-1), revealed a surprisingly higher abundance of small RNAs originating from Chromosome 2 in wild rice species. This locus codes for a novel 22 nt miRNA. This novel miRNA was found to be highly abundant in flag leaf of wild species, a tissue that usually provides 70% of energy required for grain filling. This miRNA targets a group of proteins (Os03g0273200, Os01g0827300, Os01g0850700, Os11g0708100 and Os01g0842500) which are involved in secondary metabolite production, although a functional significance of this interaction has not been understood. The expression of these targets also differs across the species. Typical of 22 nt miRNAs, the identified miRNA also triggers a secondary cascade silencing by producing small interfering RNAs (siRNAs) from target mRNAs in O. nivara. These secondary siRNAs are observed only among wild rice species but not in cultivated rice. Currently we are using a range of genetic, biochemical and molecular techniques to understand role of this novel miRNA in domestication of rice.
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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 | 0.001 |
| 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".