Integrating SHAPE Probing with Direct RNA Nanopore Sequencing Reveals Dynamic RNA Structural Landscapes

Abstract

Traditional SHAPE experiments rely on averaged reactivities, which may limit information on folding patterns, alternate structures, and RNA dynamics. Short-read sequencing often suffers from false stopping, stalls, and biases during reverse transcription. The introduction of direct, long-read nanopore technology offers an opportunity to expand RNA structure probing methods to better understand RNA structural diversity. While many comparative approaches have been developed for detection of endogenous modifications, fewer have explored the expansion of SHAPE based methods. We introduce Dashing Turtle (DT), an algorithm using probabilistic, weighted, stacked ensemble learning to perform high-resolution detection of structural modifications that can capture detailed information about RNA architecture across dynamic structural landscapes. We apply our method to several well-characterized RNA samples, identify dominant conformations, and structurally conserved regions. We show that our landscapes correlate well with expected structures and recapitulate important functional elements. DT achieves accuracy 10-20% higher than comparable methods on many sequences. It accurately identifies structural features at a rate of 80-100%, approximately 10-30% better than its peers. DTs predictions are robust across replicates and sub-sampled datasets and can help detect changes in conformational states, inform RNA folding mechanisms, and indicate interaction efficiency. Overall, it expands the capabilities of direct RNA sequencing and structural probing.