Characterization of microRNAs and their targets in wild barley (<i>Hordeum vulgare</i> subsp. <i>spontaneum</i>) using deep sequencing
Bibliographic record
Abstract
MicroRNAs (miRNA) are a class of small, endogenous RNAs that play a negative regulatory role in various developmental and metabolic processes of plants. Wild barley (Hordeum vulgare subsp. spontaneum), as the progenitor of cultivated barley (Hordeum vulgare subsp. vulgare), has served as a valuable germplasm resource for barley genetic improvement. To survey miRNAs in wild barley, we sequenced the small RNA library prepared from wild barley using the Illumina deep sequencing technology. A total of 70 known miRNAs and 18 putative novel miRNAs were identified. Sequence analysis revealed that all of the miRNAs identified in wild barley contained the highly conserved hairpin sequences found in barley cultivars. MiRNA target predictions showed that 12 out of 52 miRNA families were predicted to target transcription factors, including 8 highly conserved miRNA families in plants and 4 wheat-barley conserved miRNA families. In addition to transcription factors, other predicted target genes were involved in diverse physiological and metabolic processes and stress defense. Our study for the first time reported the large-scale investigation of small RNAs in wild barley, which will provide essential information for understanding the regulatory role of miRNAs in wild barley and also shed light on future practical utilization of miRNAs for barley improvement.
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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".