Characterization of bovine miRNAs by sequencing and bioinformatics analysis
Bibliographic record
Abstract
BACKGROUND: MicroRNAs (miRNAs) are a family of approximately 22 nucleotide small RNA molecules which regulate gene expression by fully or partially binding to their complementary sequences in mRNAs or promoters. A large number of miRNAs and their expression patterns have been reported in human, mouse and rat. However, miRNAs and their expression patterns in live stock species such as beef cattle are not well studied. RESULTS: We constructed and sequenced small-RNA libraries to yield a total of 13,541 small-RNA sequences from 11 bovine tissues including brain, subcutaneous fat, muscle, liver, kidney, spleen and thymus. In total, 228 miRNAs including 29 novel miRNA candidates were identified. Of the 199 miRNAs, 101 have been previously reported as bovine miRNAs and the other 98 are bovine orthologs of known miRNAs that have been identified in at least one other mammalian species. Of the 29 novel miRNA candidates, 17 appeared at this point in time to be bovine specific, while the remaining 12 had evidence of evolutionary conservation in other mammalian species. Five miRNAs (miR-23a, -23b, -99a, -125b and -126-5p) were very abundant across the 11 tissues, accounting for 44.3% of all small RNA sequences. The expression analysis of selected miRNAs using qRT-PCR also showed that miR-26a and -99a were highly expressed in all tissues, while miR-122 and miR-133a were predominantly expressed in liver and muscle, respectively. CONCLUSION: The miRNA expression patterns among 11 tissues from beef cattle revealed that most miRNAs were ubiquitously expressed in all tissues, while only a few miRNAs were tissue specific. Only 60% miRNAs in this study were found to display strand bias, suggesting that there are some key factors for mature miRNA selection other than internal stability. Most bovine miRNAs are highly conserved in other three mammalian species, indicating that these miRNAs may have a role in different species that are potential molecular markers for evolution.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".