In silico identification and functional annotation of miRNAs and their targets from EST and GSS of onion (Allium cepa L.)
Bibliographic record
Abstract
MicroRNAs are a class of approximately 20-24 nucleotides (nt) endogenous small RNAs that negatively regulate gene expression and play vital roles in multiple biological processes, including plant growth, development and responses to environmental stresses. Onion (Allium cepa L.), also called as "queen of kitchen" is a bulbous vegetable crop cultivated in almost all parts of the world. However, the miRNA repertoire of onion is highly ambiguous. In the present study, we report the computational identification of miRNAs and their targets from expressed sequence tags (ESTs) and genome survey sequences (GSSs) of Allium cepa L as well as functionally annotated the target genes. By following a stringent pipeline, we used 20225 ESTs and 10725 GSS from onion to identify 9 new potential miRNA belonging to 8 different miRNA families (miR172, miR1134, miR1223, miR6219, miR7725, miR8570, miR8703 and miR8752). Under a stringent condition, 26 potential targets were identified for the 8 miRNAs with distinct functions related to growth and development, signal transduction, metabolism, defense and stress responses. Overall, the present finding will make the pathway for understanding of molecular mechanisms of miRNA in onion and understanding their involvement in post-transcriptional gene silencing mechanism towards regulation of stress responses in this economically important plant.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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 | 0.001 |
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".