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基因甲基:通过动力信号建模和深度特征集成,在 prokaryotic SMRT 测序中进行强大的甲基化检测

Jichen Zhang¹, Yutaka Saito^1,2,3

¹Graduate School of Frontier Sciences, The University of Tokyo, Kashiwanoha, Kashiwa, Chiba 277-0882, Japan.

Bioinformatics advances

|October 27, 2025

概括

KinMethyl通过整合序列和动力数据,通过使用PacBio SMRT测序来增强 prokaryotic DNA 甲基化检测. 这种深度学习框架提高了5-甲基细胞因和其他多种细菌基因组的修改的准确性.

科学领域:

表观基因组学是指表观基因组学.
生物信息学是一种生物信息学.
基因组学就是基因组学.

背景情况:

通过PacBio SMRT测序检测 prokaryotes 中的5-methylcytosine (5mC) 是很困难的,因为动力信号较弱和各种动机.
现有的方法在低信号噪声比率和模式依赖性方面扎.

研究的目的:

开发一种可通用的深度学习框架,KinMethyl,用于改善细菌中的DNA甲基化检测.
为了整合序列和动态信号,进行强大的甲基化分析.

主要方法:

开发了KinMethyl,一个深度学习框架,利用回归模型来预测非甲基化序列动力学.
将预测的动力学集成到下游分类器中,以增强甲基化检测.
在各种细菌基因组和PacBio测序数据上训练和评估模型.

主要成果:

在检测5mC,N6-methyladenine (6mA) 和N4-methylcytosine (4mC) 方面,KinMethyl显著优于现有的工具 (kineticstools,ccsmeth).
在5mC分类 (0.6165到0.8190,P <1e-10) 中,AUC得到了0.20的改善.
在不同细菌物种,基因和测序平台 (RSII,Sequel) 中表现出一致的性能.

结论:

KinMethyl提供了一种强大且独立于动机的方法来分析 prokaryotic DNA 甲基化.
动态信号建模和特征集成对于推进细菌表观基因组分析至关重要.
该框架的通用性有助于各种细菌表观基因组研究.

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