Nanopore direct RNA sequencing of human transcriptomes reveals the complexity of mRNA modifications and crosstalk between regulatory features
Bibliographic record
Abstract
The identification and functional characterization of chemical modifications on an mRNA molecule, in particular N 6 -methyladenosine (m 6 A) modification, significantly broadened our understanding of RNA function and regulation. While interactions between RNA modifications and other RNA features have been proposed, direct evidence showing correlation is limited. Here, using Oxford Nanopore long-read direct RNA sequencing (dRNA-seq), we simultaneously interrogate the transcriptome and epitranscriptome of a human leukemia cell line to investigate the correlation between m 6 A modifications, mRNA abundance, mRNA stability, polyadenylation (poly(A)) tail length, and alternative splicing. High-quality dRNA-seq is important for unbiased and large-scale correlative analyses. Global assessments indicated a negative association between poly(A) tail length and mRNA abundance while uncovering pathway-specific responses upon depletion of the m 6 A-forming enzyme METTL3. Overall, our study presented a rich dRNA-seq data resource that has been validated and can be further exploited to inquire into the complexity of RNA modifications and potential interplays between RNA regulatory elements.
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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 | 0.000 |
| 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".