What question did this study set out to answer?

This research aims to develop a deep learning framework for analyzing RNA structures using nanopore sequencing data.

July 1, 2026Open Access

NanoRAPID: A Deep Learning-based Framework for Single-molecule RNA Structure Analysis Using Nanopore Direct RNA Sequencing

Key Points

This research aims to develop a deep learning framework for analyzing RNA structures using nanopore sequencing data.
Introduced NanoRAPID, a convolutional neural network framework, to analyze raw currents from direct RNA sequencing.
Validated using datasets from specific RNA modification treatments, such as NAI-N3 and DEPC.
Performed transcriptome-wide analysis to identify structural features in different RNA categories.
NanoRAPID improved accuracy of probe-site identification compared to traditional methods.
Discovered variable structures in mRNAs and compact structures in rRNAs across gene bodies and UTRs.
Established a positive correlation between m6A modification levels and 3' UTR accessibility.

Abstract

Abstract RNA structure is fundamental to its diverse biological functions. Current chemical probing methods, coupled with next-generation sequencing, offer insights into RNA secondary structure but are limited by indirect readouts and averaging of multiple molecules. Nanopore direct RNA sequencing (DRS) enables direct detection of modifications on long RNA reads, while accurately identifying probe-modified sites from DRS data remains challenging. Here, we present NanoRAPID (Nanopore RNA Structural Probe IDentification), a convolutional neural network (CNN)-based framework for analyzing RNA secondary structures using DRS data. By directly analyzing raw current signals, NanoRAPID achieves improved accuracy in probe-site identification. Validation with DRS datasets, including NAI-N3 2-(azidomethyl)nicotinic acid imidazolide and diethyl pyrocarbonate (DEPC) treatments, demonstrates the robustness and transferability of NanoRAPID. Transcriptome-wide analysis reveals distinct structural features across RNA categories, with mRNAs exhibiting variable structures in gene bodies and untranslated regions (UTRs), in contrast to the compact and uniform architecture of rRNAs. Notably, NanoRAPID identifies isoforms with distinct structural conformations, suggesting a dynamic equilibrium between conformational states. Furthermore, we observe a positive correlation between N6-methyladenosine (m6A) modification levels and 3′ UTR accessibility, suggesting that RNA structural context may influence m6A deposition. NanoRAPID is a precise and versatile tool for detecting RNA structural probing signals from DRS data and is freely available on GitHub: https://github.com/luolab-sysu/NanoRAPID and BioCode: https://ngdc.cncb.ac.cn/biocode/tools/BT008087.

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