The R-loop grammar predicts R-loop formation under different topological constraints
Margherita Maria Ferrari1, Svetlana Poznanović2, Manda Riehl3
1Department of Mathematics, University of Manitoba, Winnipeg, Manitoba, Canada.
Plos Computational Biology
|August 29, 2025
Summary
This study introduces a novel R-loop grammar to predict R-loop formation, a key nucleic acid structure involved in gene regulation and disease. The grammar accurately forecasts R-loop occurrences, even without explicit topological data.
Area of Science:
- Molecular Biology
- Genetics
- Bioinformatics
Background:
- R-loops are three-stranded nucleic acid structures formed during transcription.
- They play critical roles in gene expression regulation.
- R-loop formation is influenced by DNA sequence and topology, but mechanisms remain unclear.
Purpose of the Study:
- To develop a predictive model for R-loop formation.
- To represent R-loops using formal grammar.
- To investigate the influence of DNA topology on R-loop prediction.
Main Methods:
- Development of the R-loop grammar.
- Training the grammar on experimental data from single-molecule R-loop footprinting and sequencing (SMRF-seq).
- Testing predictive accuracy on plasmids with varying topologies.
Main Results:
- The R-loop grammar accurately predicts R-loop formation.
- The model performs well even without explicit topological information.
- The R-loop grammar outperforms previous prediction methods.
Conclusions:
- A formal grammar can effectively model and predict R-loop formation.
- This approach offers a new tool for studying R-loop dynamics.
- The R-loop grammar has implications for understanding gene regulation and disease pathogenesis.
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