Integrating SHAPE Probing with Direct RNA Nanopore Sequencing Reveals Dynamic RNA Structural Landscapes
Bibliographic record
Abstract
Abstract Traditional SHAPE experiments rely on averaged reactivities, which may limit information on folding patterns, alternate structures, and RNA dynamics. Short-read sequencing often suffers from false stopping, stalls, and biases during reverse transcription. The introduction of direct, long-read nanopore technology offers an opportunity to expand RNA structure probing methods to better understand RNA structural diversity. While many comparative approaches have been developed for detection of endogenous modifications, fewer have explored the expansion of SHAPE based methods. We introduce Dashing Turtle (DT), an algorithm using probabilistic, weighted, stacked ensemble learning to perform high-resolution detection of structural modifications that can capture detailed information about RNA architecture across dynamic structural landscapes. We apply our method to several well-characterized RNA samples, identify dominant conformations, and structurally conserved regions. We show that our landscapes correlate well with expected structures and recapitulate important functional elements. DT achieves accuracy 10–20% higher than comparable methods on many sequences. It accurately identifies structural features at a rate of 80–100%, approximately 10–30% better than its peers. DT’s predictions are robust across replicates and sub-sampled datasets and can help detect changes in conformational states, inform RNA folding mechanisms, and indicate interaction efficiency. Overall, it expands the capabilities of direct RNA sequencing and structural probing.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| 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 | 0.000 |
| 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 | 0.000 |
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".