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

Abstract

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(N3) 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.

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