Raw signal segmentation for estimating RNA modifications and structures from Nanopore direct RNA sequencing data

Abstract

AbstractEstimating RNA modifications from Nanopore direct RNA sequencing data is an important task for the RNA research community. Current computational methods could not provide satisfactory results due to an inaccurate hypothesis in the raw signal segmentation. We present a new method, SegPore, that utilizes a jiggling translocation hypothesis to segment the raw signal. Based on the segmentation results, we demonstrate SegPore’s interpretable results and decent performances in m6A and inosine modification estimation, as well as RNA secondary structure estimation. We find that the stop points of the reads take place at the start of estimated stem structures. Our results indicate the SegPore’s capability to concurrently estimate multiple modifications at the individual molecule level using the same Nanopore direct RNA sequencing data. Additionally, our study reveals novel perspectives for RNA structure estimation.