The R-loop Grammar predicts R-loop formation under different topological constraints
Bibliographic record
Abstract
Abstract R-loops are transient three-stranded nucleic acids that form during transcription when the nascent RNA hybridizes with the template DNA, freeing the DNA non-template strand. There is growing evidence that R-loops play important roles in physiological processes such as control of gene expression, and that they 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 and the 3-dimensional structures that they adopt. 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). Despite not containing explicit topological information, the R-loop grammar accurately predicts R-loop formation on plasmids with varying starting topologies and outperforms previous methods in R-loop prediction. Author summary R-loops are prevalent triple helices that play regulatory roles in gene expression and are involved in various diseases. Our work improves the understanding of the relationship between the nucleotide sequence and DNA topology in R-loop formation. We use a mathematical approach from formal language theory to define an R-loop language and a set of rules to model R-loops as words in that language. We train the resulting R-loop grammar on experimental data of co-transcriptional R-loops formed on different DNA plasmids of varying topology. The model accurately predicts R-loop formation and outperforms prior methods. The R-loop grammar distills the effect of topology versus sequence, thus advancing our understanding of R-loop structure and formation.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".