Machine learning of RNA structure is more challenging than you might think. Using synthetic data from ViennaRNA's RNAfold to study the capabilities and shortcomings of neural networks for RNA secondary structure prediction in a controlled setting, we argue that shortcomings in the artificial setting will translate to real data
Discovering the characteristic traits of Musashi Binding Elements (MBEs) in Flaviviruses with a Thermodynamic Model
Riboswitches are RNA molecules that regulate gene expression by sensing metabolites, presenting an interesting target for synthetic biology applications. We present a computational approach based on ViennaRNA tools to dissect and model RNA-ligand interaction dynamics under kinetic control, enabling simulation of riboswitch folding
In the world of synthetic biology, the design of RNA switches holds immense promise for various applications, ranging from diagnostics to therapeutics. This paper presents a comprehensive workflow for designing RNA switches that can dynamically alter their structural conformations in response to specific ligands
The ViennaRNA Package supports three published approaches for SHAPE-guided RNA structure prediction. Here we evaluate and compare the methods by Deigan, Zarringhalam, and Washietl