Assessment of different enrichment methods revealed the optimal approach to identify bovine circRnas
Bibliographic record
Abstract
Although circular RNAs (circRNAs) play important roles in regulating gene expression, the understanding of circRNAs in livestock animals is scarce due to the significant challenge to characterize them from a biological sample. In this study, we assessed the outcomes of bovine circRNA identification using six enrichment approaches with the combination of ribosomal RNAs removal (Ribo); linear RNAs degradation (R); linear RNAs and RNAs with structured 3′ ends degradation (RTP); ribosomal RNAs coupled with linear RNAs elimination (Ribo-R); ribosomal RNA, linear RNAs and RNAs with poly (A) tailing elimination (Ribo-RP); and ribosomal RNA, linear RNAs and RNAs with structured 3′ ends elimination (Ribo-RTP), respectively. RNA-sequencing analysis revealed that different approaches led to varied ratio of uniquely mapped reads, false-positive rate of identifying circRNAs, and the number of circRNAs per million clean reads (Padj <0.05). Out of 2,285 and 2,939 highly confident circRNAs identified in liver and rumen tissues, respectively, 308 and 260 were commonly identified from five methods, with Ribo-RTP method identified the highest number of circRNAs. Besides, 507 of 4,051 identified bovine highly confident circRNAs had shared splicing sites with human circRNAs. The findings from this work provide optimized methods to identify bovine circRNAs from cattle tissues for downstream research of their biological roles in cattle.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".