Wang Xiaoyan, Luo Junwei, Wu Lili, Luo Huimin, Guo Fei
School of Software, Henan Polytechnic University, 2001 Century Road, Jiaozuo 454003, China.
School of Computer and Information Engineering, Henan University, North Section of Jinming Avenue, Kaifeng 475001, China.
Brief Bioinform. 2025 Mar 4;26(2). doi: 10.1093/bib/bbaf127.
Topologically associated domains (TADs) play a key role in the 3D organization and function of genomes, and accurate detection of TADs is essential for revealing the relationship between genomic structure and function. Most current methods are developed to extract features in Hi-C interaction matrix to identify TADs. However, due to complexities in Hi-C contact matrices, it is difficult to directly extract features associated with TADs, which prevents current methods from identifying accurate TADs.
In this paper, a novel method is proposed, deepTAD, which is developed based on a convolutional neural network (CNN) and transformer model. First, based on Hi-C contact matrix, deepTAD utilizes CNN to directly extract features associated with TAD boundaries. Next, deepTAD takes advantage of the transformer model to analyze the variation features around TAD boundaries and determines the TAD boundaries. Second, deepTAD uses the Wilcoxon rank-sum test to further identify false-positive boundaries. Finally, deepTAD computes cosine similarity among identified TAD boundaries and assembles TAD boundaries to obtain hierarchical TADs. The experimental results show that TAD boundaries identified by deepTAD have a significant enrichment of biological features, including structural proteins, histone modifications, and transcription start site loci. Additionally, when evaluating the completeness and accuracy of identified TADs, deepTAD has a good performance compared with other methods. The source code of deepTAD is available at https://github.com/xiaoyan-wang99/deepTAD.
拓扑相关结构域（TADs）在基因组的三维组织和功能中起关键作用，准确检测TADs对于揭示基因组结构与功能之间的关系至关重要。当前大多数方法是为了在Hi-C相互作用矩阵中提取特征以识别TADs而开发的。然而，由于Hi-C接触矩阵的复杂性，难以直接提取与TADs相关的特征，这使得当前方法无法识别准确的TADs。
本文提出了一种新方法deepTAD，它是基于卷积神经网络（CNN）和Transformer模型开发的。首先，基于Hi-C接触矩阵，deepTAD利用CNN直接提取与TAD边界相关的特征。接下来，deepTAD利用Transformer模型分析TAD边界周围的变异特征并确定TAD边界。其次，deepTAD使用Wilcoxon秩和检验进一步识别假阳性边界。最后，deepTAD计算已识别的TAD边界之间的余弦相似度并组装TAD边界以获得分层TADs。实验结果表明，由deepTAD识别的TAD边界具有生物特征的显著富集，包括结构蛋白、组蛋白修饰和转录起始位点。此外，在评估已识别TADs的完整性和准确性时，deepTAD与其他方法相比具有良好的性能。deepTAD的源代码可在https://github.com/xiaoyan-wang99/deepTAD获取。
