Motivation The folding dynamics of ribonucleic acids (RNAs) are typically studied via coarse-grained models of the underlying energy landscape to face the exponential growths of the RNA secondary structure space. Still, studies of exact folding kinetics based on gradient basin abstractions are currently limited to short sequence lengths due to vast memory requirements. In order to compute exact transition rates between gradient basins, state-of-the-art approaches apply global flooding schemes that require to memorize the whole structure space at once. pourRNA tackles this problem via local flooding techniques where memorization is limited to the structure ensembles of individual gradient basins. Results Compared to the only available tool for exact gradient basin-based macro-state transition rates (namely barriers), pourRNA computes the same exact transition rates up to 10 times faster and requires two orders of magnitude less memory for sequences that are still computationally accessible for exhaustive enumeration. Parallelized computation as well as additional heuristics further speed up computations while still producing high-quality transition model approximations. The introduced heuristics enable a guided trade-off between model quality and required computational resources. We introduce and evaluate a macroscopic direct path heuristics to efficiently compute refolding energy barrier estimations for the co-transcriptionally trapped RNA sv11 of length 115 nt. Finally, we also show how pourRNA can be used to identify folding funnels and their respective energetically lowest minima. Availability and implementation pourRNA is freely available at https://github.com/ViennaRNA/pourRNA. Supplementary information Supplementary data are available at Bioinformatics online.

Motivation Predicting the folding dynamics of RNAs is a computationally difficult problem, first and foremost due to the combinatorial explosion of alternative structures in the folding space. Abstractions are therefore needed to simplify downstream analyses, and thus make them computationally tractable. This can be achieved by various structure sampling algorithms. However, current sampling methods are still time consuming and frequently fail to represent key elements of the folding space. Method We introduce RNAxplorer, a novel adaptive sampling method to efficiently explore the structure space of RNAs. RNAxplorer uses dynamic programming to perform an efficient Boltzmann sampling in the presence of guiding potentials, which are accumulated into pseudo-energy terms and reflect similarity to already well-sampled structures. This way, we effectively steer sampling towards underrepresented or unexplored regions of the structure space. Results We developed and applied different measures to benchmark our sampling methods against its competitors. Most of the measures show that RNAxplorer produces more diverse structure samples, yields rare conformations that may be inaccessible to other sampling methods and is better at finding the most relevant kinetic traps in the landscape. Thus, it produces a more representative coarse graining of the landscape, which is well suited to subsequently compute better approximations of RNA folding kinetics. Availability https://github.com/ViennaRNA/RNAxplorer/ Supplementary information Supplementary data are available at Bioinformatics online.

Motivation: Predicting the folding dynamics of RNAs is a computationally difficult problem, first and foremost due to the combinatorial explosion of alternative structures in the structure space. Abstractions are therefore needed to simplify downstream analyses, and make them computationally tractable. This can be achieved by various structure sampling algorithms. However, current sampling methods are still time consuming and frequently fail to represent key elements of the folding space. Method: We introduce RNAxplorer, a novel adaptive sampling method which uses dynamic programming to perform an efficient Boltzmann sampling in the presence of guiding potentials reflecting the similarity to already well-sampled structures. These potentials are accumulated into pseudo-energy terms that effectively steer sampling towards underrepresented or unexplored regions of the structure space. Results: RNAxplorer allows us to efficiently explore RNA state space. It yields rare conformations that may be inaccessible to other sampling methods. We developed and applied different measures to benchmark our sampling methods against its competitors. Most of the measures show that RNAxplorer produces more diverse structure samples and is better at finding the most relevant kinetic traps in the landscape. Thus, it produces a more representative coarse graining of the landscape that is well suited to compute better approximations of RNA folding kinetics. Availability: https://github.com/ViennaRNA/RNAxplorer/