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CCTAD: 一种拓关联域检测方法,集成卷积自编码器和层次集群

Feng Ruiping¹, Luo Junwei¹, Liu Kaihua¹

¹School of Software, Henan Polytechnic University, Jiaozuo 454003, China.

Computational biology and chemistry

|January 24, 2026

概括

一种新的无监督方法,CCTAD,通过将卷积自编码器与集群集成,准确地识别拓关联域 (TAD). 这种方法增强了对染色体组织和基因调节的理解.

科学领域:

基因组学就是基因组学.
计算生物学计算生物学
表观遗传学在表观遗传学中,表观遗传学是指表观遗传学.

背景情况:

准确识别拓关联域 (TADs) 对于理解染色体组织和基因调节至关重要.
现有的TAD检测计算方法在建模相互作用模式,参数稳定性和对分辨率和细胞类型的敏感性方面存在局限性.

研究的目的:

开发一种新的无监督方法,用于强大而准确的TAD识别.
克服现有的TAD检测方法的局限性,提高生物解释性.

主要方法:

提出了CCTAD,一种无监督的TAD识别方法,集成一维卷积自编码器 (1D-CAE) 与受连接限制的等级集群.
1D-CAE自动从Hi-C接触矩阵中提取低维特征,捕获本地和全球色素相互作用模式.
使用基因组邻近性约束的等级聚类精细化了TAD边界,确保了连续性和适应性细粒度.

主要成果:

CCTAD有效地捕获了本地和全球色素相互作用模式.
该方法在不同的分辨率上产生了具有生物意义和强大的TAD划分.
与传统方法相比,CCTAD克服了边界精度和稳定性的局限性.

结论:

在确定关键监管要素的边界方面,CCTAD表现出卓越的表现.
该方法在多个细胞系和分辨率上的稳定性验证了其实用性.
开发的源代码是公开可用的,以便进一步研究.

关键词:

3D基因组组织 3D基因组组织卷积式自动编码器卷积式自动编码器层次化的集群化层次化的集群化在拓上关联域名.

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