Localized, Non-parametric Detection of RNA Structural Modification using Nanopore Basecalling.
Bibliographic record
Abstract
Recently, much work has been done on chemical probing strategies with nanopore sequencing to identify RNA modifications at the single nucleotide level. Here, we examine the use of Oxford Nanopore's Guppy basecalling to identify structural modifications using localized, non-parametric peak detection. In a novel experiment, we evaluate whether detection of structural modifications is possible using the Guppy's basecalling error and determine the accuracy of our approach for selected RNA control sequences. Next, we use statistical analysis to determine the dominant structural bindings in a set of averaged read errors. Finally, we compare our approach to average reactivity determined by orthogonal experiments from SHAPE-CE and alternative approaches. We show that localized, non-parametric peak detection demonstrates improved accuracy and coverage of structural modifications in selected control RNA and that our method is agnostic to underlying changes in the distribution. Our approach allows for a more generalizeable methodology for detecting structural modification with nanopore sequencing and the subsequent generated probabilities can be used to refine further downstream analysis.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".