LinearCapR: Linear-time computation of per-nucleotide structural-context probabilities of RNA without base-pair span limits

Motivation RNA molecules adopt dynamic ensembles of secondary structures, where the local structural context of each nucleotide–such as whether it resides in a stem or a specific type of loop–strongly shapes molecular interactions and regulatory function. Structural-context probabilities therefore provide a more functionally informative view of RNA folding than the minimum free energy structures or base-pairing probabilities. However, existing tools either require O ( N 3 ) time or employ span-restricted approximations that omit long-range base-pairs, limiting their applicability to large and biologically important RNAs. Results We introduce LinearCapR, enabling linear-time, span-unrestricted computation of structural-context marginalized probabilities, using beam-pruned Stochastic Context Free Grammar-based computation. LinearCapR retains global ensemble features lost by span-limited methods and yields superior predictive power on bpRNA-1m(90) dataset, especially for multiloops and exterior regions, as well as long-distance stems. LinearCapR supports analysis of long RNAs, demonstrated on the full genome of SARS-CoV-2. Conclusions LinearCapR provides the first base-pair-span-unrestricted, linear-time framework for RNA structural-context analysis, retaining key thermodynamic ensemble features essential for functional interpretation. It enables large-scale studies of viral genomes, long non-coding RNAs, and downstream analyses such as RNA-binding protein site prediction. Availability The source code of LinearCapR is available at https://github.com/hoget157/LinearCapR .