Identification of miRNAs involved in fruit ripening in Cavendish bananas by deep sequencing
Bibliographic record
Abstract
BACKGROUND: MicroRNAs (miRNAs) are a family of non-coding small RNAs that play an important regulatory role in various biological processes. Previous studies have reported that miRNAs are closely related to the ripening process in model plants. However, the miRNAs that are closely involved in the banana fruit ripening process remain unknown. METHODS: Here, we investigated the miRNA populations from banana fruits in response to ethylene or 1-MCP treatment using a deep sequencing approach and bioinformatics analysis combined with quantitative RT-PCR validation. RESULTS: A total of 125 known miRNAs and 26 novel miRNAs were identified from three libraries. MiRNA profiling of bananas in response to ethylene treatment compared with 1-MCP treatment showed differential expression of 82 miRNAs. Furthermore, the differentially expressed miRNAs were predicted to target a total of 815 target genes. Interestingly, some targets were annotated as transcription factors and other functional proteins closely involved in the development and the ripening process in other plant species. Analysis by qRT-PCR validated the contrasting expression patterns between several miRNAs and their target genes. CONCLUSIONS: The miRNAome of the banana fruit in response to ethylene or 1-MCP treatment were identified by high-throughput sequencing. A total of 82 differentially expressed miRNAs were found to be closely associated with the ripening process. The miRNA target genes encode transcription factors and other functional proteins, including SPL, APETALA2, EIN3, E3 ubiquitin ligase, β-galactosidase, and β-glucosidase. These findings provide valuable information for further functional research of the miRNAs involved in banana fruit ripening.
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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 | 0.001 |
| 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".