Detection of DNA N6-Methyladenine Modification through SMRT-seq Features and Machine Learning Model

Yichu Guo; Yixuan Zhang; Xiaoqing Liu; Pingan He; Yuni Zeng; Qi Dai

doi:10.2174/0115748936300671240523044154

ISSN: 1574-8936
E-ISSN: 2212-392X

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oa Detection of DNA N6-Methyladenine Modification through SMRT-seq Features and Machine Learning Model
Authors: Yichu Guo¹, Yixuan Zhang², Xiaoqing Liu³, Pingan He⁴, Yuni Zeng² and Qi Dai¹
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¹ College of Life Sciences, Zhejiang Sci-Tech University, Hangzhou, 310018, China ; ² College of Computer Sciences, Zhejiang Sci-Tech University, Hangzhou, 310018, China ; ³ College of Sciences, Hangzhou Dianzi University, Hangzhou, 310018, China ; ⁴ College of Sciences, Zhejiang Sci-Tech University, Hangzhou, 310018, China
Source: Current Bioinformatics, Volume 20, Issue 5, Jun 2025, p. 392 - 401
DOI: https://doi.org/10.2174/0115748936300671240523044154
- Received: 26 Dec 2023
- Accepted: 26 Apr 2024
- Available online: 01 Jun 2025

Abstract

Introduction

N6-methyldeoxyadenine (6mA) is the most prevalent DNA modification in both prokaryotes and eukaryotes. While single-molecule real-time sequencing (SMRT-seq) can detect 6mA events at the individual nucleotide level, its practical application is hindered by a high rate of false positives.

Methods

We propose a computational model for identifying DNA 6mA that incorporates comprehensive site features from SMRT-seq and employs machine learning classifiers.

Results

The results demonstrate that 99.54% and 96.55% of the identified DNA 6mA instances in C. reinhardtii correspond with motifs and peak regions identified by methylated DNA immunoprecipitation sequencing (MeDIP-seq), respectively. Compared to SMRT-seq, the proportion of predicted DNA 6mA instances within MeDIP-seq peak regions increases by 2% to 70% across the six bacterial strains.

Conclusion

Our proposed method effectively reduces the false-positive rate in DNA 6mA prediction.

This is an open access article published under CC BY 4.0 https://creativecommons.org/licenses/by/4.0/legalcode

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Detection of DNA N6-Methyladenine Modification through SMRT-seq Features and Machine Learning Model

Curr Bioinform 20, 392 (2025); https://doi.org/10.2174/0115748936300671240523044154

/content/journals/cbio/10.2174/0115748936300671240523044154

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Article Type: Research Article

Keyword(s): C. reinhardtii; detection method; DNA 6mA; machine learning; MeDIP-seq; SMRT-seq features

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oa Detection of DNA N6-Methyladenine Modification through SMRT-seq Features and Machine Learning Model

Abstract

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