VIRSE: a variational Bayesian framework for RNA structural ensemble inference

Most RNA molecules adopt multiple alternative structures, forming dynamic ensembles that cannot be captured by single-structure prediction. Recent advances in chemical probing methods (e.g. DMS-MaPseq and SHAPE-MaP sequencing) now provide single-molecule signals that reflect this structural heterogeneity, enabling computational reconstruction of RNA conformational states. However, existing ensemble-inference approaches based on expectation-maximization (EM) often suffer from instability, convergence to suboptimal local optima, and poor scalability on high-dimensional, sparse mutation matrices, particularly for complex or modification-dependent RNA ensembles. To address these limitations, we developed VIRSE, a variational Bayesian framework that uses coordinate ascent variational inference to achieve efficient, scalable, and noise-robust reconstruction of RNA conformational mixtures from chemical probing data. We evaluated VIRSE using extensive simulations, including mechanism-informed mutation simulations that mimic realistic DMS-MaP-seq behavior (A/C mutation bias, context-dependent dropouts, position-specific mutation rates) and idealized Bernoulli-mixture datasets without experimental artifacts. Across all conditions, especially in high-dimensional and long RNA regimes, VIRSE achieved superior ensemble separation and improved cluster identifiability compared with EM, while maintaining stable posteriors, resolving low-abundance states, and scaling to thousands of nucleotide positions. Applied to experimental datasets, including the human immunodeficiency virus-1 Rev response element, SARS-CoV-2 SHAPE-MaP measurements, and the Escherichia coli mgtL Mg2+-responsive riboswitch, VIRSE successfully recovered biologically meaningful and physically plausible RNA conformational ensembles. VIRSE is freely available at https://github.com/QSong-github/VIRSE.