Bibliographic record
Abstract
R-loops are transient three-stranded nucleic acids that form during transcription when the nascent RNA hybridizes to the template DNA, freeing the DNA non-template strand. There is growing evidence that R-loops contribute to chromosomal instability and disease. It is known that R-loop formation is influenced by both the sequence and the topology of the DNA substrate, but many questions remain about how R-loops form. Here we represent an R-loop as a word in a formal grammar called the R-loop grammar and predict R-loop formation. We train the R-loop grammar on experimental data obtained by single-molecule R-loop footprinting and sequencing (SMRF-seq). This data includes a large set of R-loops formed on two different plasmids of varying DNA topologies. The R-loop grammar accurately predicts R-loop formation for varying starting topologies and outperforms previous methods in R-loop prediction.This record contains data and software associated with the paper "The R-loop Grammar predicts R-loop formation under different topological constraints." by Ferrari et al. (Submitted 2025). Related software, including code can also be found on GitHub. The current release includes the following files: R-loopGrammar.zip: Software to generate the R-loop grammar. fasta_bed.zip: FASTA and BED files used for building the model. training-data.zip: Contains model configuration and seed for data set splits. model.zip: Contains final model for use in generating predictions. predictions.zip: Contains the final results of model predictions used in the paper.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.007 | 0.003 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.038 | 0.062 |
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".