跨RNA可转移序列表示学习用于通过新型深域区分网络检测lncRNA m6A位点
Zhixia Teng1, Zhenjiang Li1, Di Liu1
1College of Computer and Control Engineering, Northeast Forestry University, No. 26 Hexing Road, Xiangfang District, Harbin, Heilongjiang, China.
Briefings in bioinformatics
|December 5, 2025
概括
在长非编码RNA (lncRNAs) 中检测N6-甲基亚丁 (m6A) 位点对于理解疾病至关重要. 我们的DSNm6A框架通过学习来自lncRNA和信使RNA (mRNA) 的可转移特征,有效地预测IncRNA m6A位点.
科学领域:
- 史诗转录组学 史诗转录组学
- 计算生物学 计算生物学
- 基因组学就是基因组学.
背景情况:
- N6-甲基氨酸 (m6A) 是一种在长非编码RNA (lncRNAs) 中发现的关键表观转录组修饰,影响复杂的疾病机制.
- 准确识别 lncRNA 中的 m6A 位点对于疾病研究至关重要,但由于有限的注释数据和现有的以信使RNA (mRNA) 为重点的工具的普遍性较差,因此计算预测受到阻碍.
- 利用 lncRNA 和 mRNA 之间的共同特征对于开发有效的 lncRNA m6A 位点预测器至关重要.
研究的目的:
- 开发一个强大的计算框架,DSNm6A,用于准确预测lncRNAs中的m6A位点.
- 创建一种可转移的学习方法,利用 lncRNA 和 mRNA 共同的序列表示.
- 为了提高m6A位点检测在不同RNA类型和物种中的通用性和准确性.
主要方法:
- DSNm6A采用深度学习架构,集成CNN,Bi-LSTM和BERT模块进行序列编码.
- RNA序列 (lncRNA和mRNA) 使用多个方面进行编码:单热编码,核酸物理化学性质,累积频率和位置特异性倾向.
- 一个域区分网络将域不变 (共享) 特征从域特定特征分离出来,以提高预测准确性.
主要成果:
- 在各种性能指标中,DSNm6A在预测 lncRNA m6A 位点方面显著优于现有的方法.
- 该框架在学习可转移的m6A相关序列特征方面表现出卓越的能力,适用于不同类型的RNA.
- DSNm6A在不同物种中表现出强大的稳定性和概括能力,表明其广泛适用性.
结论:
- DSNm6A通过学习跨RNA可转移特征,为lncRNA m6A位点预测提供了有效的解决方案.
- 拟议的域分离网络成功识别了共享的序列模式,这对于准确检测 lncRNA 中的 m6A 位点至关重要.
- DSNm6A代表了表观转录学研究的重大进步,促进了对复杂疾病中的lncRNA功能的更深入的理解.
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