circTAIL-seq, a targeted method for deep analysis of RNA 3′ tails, reveals transcript-specific differences by multiple metrics
Bibliographic record
Abstract
Post-transcriptionally added RNA 3' nucleotide extensions, or tails, impose numerous regulatory effects on RNAs, including effects on RNA turnover and translation. However, efficient methods for in-depth tail profiling of a transcript of interest are still lacking, hindering available knowledge particularly of tail populations that are highly heterogeneous. Here, we developed a targeted approach, termed circTAIL-seq, to quantify both major and subtle differences of heterogeneous tail populations. As proof-of-principle, we show that circTAIL-seq quantifies the differences in tail qualities between two selected Trypanosoma brucei mitochondrial transcripts. The results demonstrate the power of the developed method in identification, discrimination, and quantification of different tail states that the population of one transcript can possess. We further show that circTAIL-seq can detect the tail characteristics for variants of transcripts that are not easily detectable by conventional approaches, such as degradation intermediates. Our findings are not only well supported by previous knowledge, but they also expand this knowledge and provide experimental evidence for previous hypotheses. In the future, this approach can be used to determine changes in tail qualities in response to environmental or internal stimuli, or upon silencing of genes of interest in mRNA-processing pathways. In summary, circTAIL-seq is an effective tool for comparing nonencoded RNA tails, especially when the tails are extremely variable or transcript of interest is low abundance.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".