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bioRxiv · 10.1101/2024.01.11.575207

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

Cheng, G.·Vehtari, A.·Cheng, L.
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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.

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Cheng, G., Vehtari, A., Cheng, L.. 2024-01-11. Raw signal segmentation for estimating RNA modifications and structures from Nanopore direct RNA sequencing data. https://doi.org/10.1101/2024.01.11.575207

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