Computational identification of conserved microRNAs and their targets from expression sequence tags of blueberry (<i>Vaccinium corybosum</i>)
Bibliographic record
Abstract
MicroRNAs (miRNAs) are a class of endogenous, approximately 21nt in length, non-coding RNA, which mediate the expression of target genes primarily at post-transcriptional levels. miRNAs play critical roles in almost all plant cellular and metabolic processes. Although numerous miRNAs have been identified in the plant kingdom, the miRNAs in blueberry, which is an economically important small fruit crop, still remain totally unknown. In this study, we reported a computational identification of miRNAs and their targets in blueberry. By conducting an EST-based comparative genomics approach, 9 potential vco-miRNAs were discovered from 22,402 blueberry ESTs according to a series of filtering criteria, designated as vco-miR156-5p, vco-miR156-3p, vco-miR1436, vco-miR1522, vco-miR4495, vco-miR5120, vco-miR5658, vco-miR5783, and vco-miR5986. Based on sequence complementarity between miRNA and its target transcript, 34 target ESTs from blueberry and 70 targets from other species were identified for the vco-miRNAs. The targets were found to be involved in transcription, RNA splicing and binding, DNA duplication, signal transduction, transport and trafficking, stress response, as well as synthesis and metabolic process. These findings will greatly contribute to future research in regard to functions and regulatory mechanisms of blueberry miRNAs.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 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".