Endogenous labeling empowers accurate detection of m <sup>6</sup> A from single long reads of direct RNA sequencing
Bibliographic record
Abstract
ABSTRACT Although plenty of machine learning models have been developed to detect m 6 A RNA modification sites using the electric current signals of ONT direct RNA sequencing (DRS) reads, the landscape of m 6 A on different RNA isoforms is still a mystery due to their limited capacity to distinguish the m 6 A on individual long reads and RNA isoforms. The primary challenge in training the model with single-read accuracy is the difficulty of obtaining the training data from individual DRS reads that comprehensively represent the m 6 A on endogenous RNAs. Here, we endogenously label the methylated m 6 A sites on single ONT DRS reads by APOBEC1-YTH induced C-to-U mutations, strategically positioned 10-100 nt away from the known m 6 A sites on the same reads. Adopting a semi-supervised leaning strategy, we obtain 700,438 reliable 5-mer single-read level m 6 A signals, providing a comprehensive representation of m 6 A on endogenous RNAs. Leveraging this dataset, we develop m6Aiso, a deep residual neural network model that not only accurately identifies and quantifies known m 6 A sites but also reveals unknown, subtly methylated m 6 A sites responsive to METTL3 depletion. Analyzing m6Aiso-determined m 6 A on single reads and isoforms uncovers distance-dependent linkages of m 6 A sites along single molecules, as well as differential methylation of identical m 6 A sites on different isoforms. Moreover, we find wide-spread functionally important dynamic changes of m 6 A sites on specific isoforms during epithelial-mesenchymal transition (EMT). The pivotal utilization of the endogenous labeling strategy empowers m6Aiso to achieve remarkable precision in pinpointing m 6 A on individual molecules, underscores its effectiveness in elucidating the intricate dynamics and complexities of m 6 A across RNA isoforms.
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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) | 0.000 | 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 | 0.001 |
| 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".