Researchers introduce a new tool for more accurate RNA modification detection from nanopore signals

Researchers from the University of Eastern Finland and Aalto University have developed a computational framework called SegPore that enhances the accuracy of RNA modification detection from direct RNA nanopore sequencing data. The study, led by Dr. Lu Cheng, has been published as a reviewed preprint in eLife.

RNA modifications are essential epigenetic regulators of gene expression and are involved in diverse biological processes, including cell differentiation, stress response and disease progression. Nanopore direct RNA sequencing offered by Oxford Nanopore Technologies provides a potential avenue for detecting RNA modifications at the single-molecule level, based on the direct measurement of electrical currents as RNA molecules translocate through a nanopore. However, accurately identifying these modifications remains a challenge due to the high noise and structural complexity of raw nanopore signals.

SegPore introduces a hierarchical, white-box segmentation model that more faithfully represents the molecular process underlying nanopore signal generation. The model leverages a molecular jiggling translocation hypothesis: instead of moving smoothly in one direction, the motor protein may cause the RNA molecule to slightly jiggle back and forth as it passes through the nanopore.

This motor protein acts as a molecular engine that controls the speed and direction of RNA movement through the pore. Small irregularities in its motion can lead to subtle changes in the electrical signal, which SegPore captures and models precisely. By explicitly modeling these dynamics, SegPore achieves superior segmentation accuracy and improves downstream RNA modification detection compared with existing methods.

“By delving deeply into the raw signals of nanopore sequencing, we discovered that the motor protein may move the RNA molecule both forward and backward, rather than strictly in one direction. By modeling this dynamic process, we developed a transparent white-box model that outperforms current state-of-the-art methods on in vitro transcribed datasets,” says Academy Research Fellow Dr. Lu Cheng from the Institute of Biomedicine at the University of Eastern Finland.

SegPore enables more reliable interpretation of single-molecule sequencing data, paving the way for improved studies of RNA modification landscapes and their functional roles in health and disease.