Transformer-based deep learning for accurate detection of multiple base modifications using single molecule real-time sequencing

We had previously reported a convolutional neural network (CNN) based approach, called the holistic kinetic model (HK model 1), for detecting 5-methylcytosine (5mC) by single molecule real-time sequencing (Pacific Biosciences). In this study, we constructed a hybrid model with CNN and transformer layers, named HK model 2. We improve the area under the receiver operating characteristic curve (AUC) for 5mC detection from 0.91 for HK model 1 to 0.99 for HK model 2. We further demonstrate that HK model 2 can detect other types of base modifications, such as 5-hydroxymethylcytosine (5hmC) and N6-methyladenine (6mA). Using HK model 2 to analyze 5mC patterns of cell-free DNA (cfDNA) molecules, we demonstrate the enhanced detection of patients with hepatocellular carcinoma, with an AUC of 0.97. Moreover, HK model 2-based detection of 6mA enables the detection of jagged ends of cfDNA and the delineation of cellular chromatin structures. HK model 2 is thus a versatile tool expanding the applications of single molecule real-time sequencing in liquid biopsies.

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Publication Details

Journal
Communications Biology
Published
2025-04-14
DOI
https://doi.org/10.1038/s42003-025-08009-8
Citations
5
Primary Topic
Epigenetics and DNA Methylation
Type
article
Field-Weighted Citation Impact
4.03

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article

Transformer-based deep learning for accurate detection of multiple base modifications using single molecule real-time sequencing

Yuk Ming Dennis Lo, Suk Hang Cheng, L Y Lois Choy, Yi Zhang et al.
5 citations
Communications Biology
Epigenetics and DNA Methylation
4.03
article

Transformer-based deep learning for accurate detection of multiple base modifications using single molecule real-time sequencing

Yuk Ming Dennis Lo, Suk Hang Cheng, L Y Lois Choy, Yi Zhang, Stephanie C Y Yu, L Mary-Jane, Wenlei Peng, Rong Qiao, Spencer C Ding, Zhaoyang Huang, K.C. Allen Chan, W.K. Lam, Jiaen Deng, Peiyong Jiang, Jing Liu, Xiaoyu Shi, Hongsheng Li, Yuwei Shi, Xi Hu, Ze Zhou
article en
5 citations

Abstract

We had previously reported a convolutional neural network (CNN) based approach, called the holistic kinetic model (HK model 1), for detecting 5-methylcytosine (5mC) by single molecule real-time sequencing (Pacific Biosciences). In this study, we constructed a hybrid model with CNN and transformer layers, named HK model 2. We improve the area under the receiver operating characteristic curve (AUC) for 5mC detection from 0.91 for HK model 1 to 0.99 for HK model 2. We further demonstrate that HK model 2 can detect other types of base modifications, such as 5-hydroxymethylcytosine (5hmC) and N6-methyladenine (6mA). Using HK model 2 to analyze 5mC patterns of cell-free DNA (cfDNA) molecules, we demonstrate the enhanced detection of patients with hepatocellular carcinoma, with an AUC of 0.97. Moreover, HK model 2-based detection of 6mA enables the detection of jagged ends of cfDNA and the delineation of cellular chromatin structures. HK model 2 is thus a versatile tool expanding the applications of single molecule real-time sequencing in liquid biopsies.

Communications BiologyVol. 8(1)
Chinese University of Hong Kong (HK), Hong Kong Science and Technology Parks Corporation (HK), Prince of Wales Hospital (CN)
Innovation and Technology Commission
Life below water
Openalex Percentile: Top 7%
Epigenetics and DNA Methylation
4.03
DOIOpen Access PDFOpenAlex
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