Work

Raw signal segmentation for estimating RNA modification from Nanopore direct RNA sequencing data

Year: 2025

Type: preprint

Abstract: Estimating RNA modifications from Nanopore direct RNA sequencing data is a critical task for the RNA research community. However, current computational methods often fail to deliver satisfactory results due to inaccurate segmentation of the raw signal. We have developed a new method, SegPore, which leverages a molecular jiggling translocation hypothesis to improve raw signal segmentation. SegPore is a pure white-box model with enhanced interpretability, significantly reducing structured noise in the raw signal. We demonstrate that SegPore outperforms state-of-the-art methods, such as Nanopolish and Tombo, in raw signal segmentation across three large benchmark datasets. Moreover, the improved signal segmentation achieved by SegPore enables SegPore+m6Anet to deliver state-of-the-art performance in site-level m6A identification. Additionally, SegPore surpasses baseline methods like CHEUI in single-molecule level m6A identification.

Source: eLife

DOI: 10.7554/elife.104618.2

Authors: Guangzhao Cheng, Aki Vehtari, Lu Cheng

Institutions: Aalto University, University of Eastern Finland

Language: English

Topic: RNA modifications and cancer

Subfield: Molecular Biology

Field: Biochemistry, Genetics and Molecular Biology

Domain: Life Sciences

Open Access status: gold

Estimated APC paid: $2,500

Funder: Academy of Finland