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<p>Accurate identification of topologically associated domains (TADs), the basic units of 3D genomes, is crucial for deciphering gene regulatory mechanisms. Existing methods rely mostly on Hi-C pairwise contact signals, and struggle to extract synergistic interactions among multiple loci, limiting boundary accuracy. In contrast, multiway chromatin interaction data can overcome the limitations of binary interactions by directly capturing high-order interactions that occur simultaneously across multiple regions, thereby providing critical data support for a more comprehensive analysis of three-dimensional chromatin organization.This study proposes HyperDeepTAD: which involves constructing hypergraphs from multiway chromatin interaction data to retain high-order information, inputting transition probability matrices into dynamic convolutional networks to capture local features, obtaining candidate boundaries via BiGRU and residual connections, screening with hypergraph clustering coefficients and using cosine similarity to obtain hierarchical TADs. Experiments show it that outperforms Hi-C-based methods in metrics such as boundary enrichment. The source code is available from <a href="https://github.com/liukaihua0213-source/HyperDeepTAD">https://github.com/liukaihua0213-source/HyperDeepTAD</a>.</p>

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HyperDeepTAD: a topologically associated domains detection method based on multiway chromatin interaction data and deep learning

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年3月9日

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  • Junwei Luo,
  • Kaihua Liu,
  • Ruiping Feng,
  • Fei Guo

摘要

Accurate identification of topologically associated domains (TADs), the basic units of 3D genomes, is crucial for deciphering gene regulatory mechanisms. Existing methods rely mostly on Hi-C pairwise contact signals, and struggle to extract synergistic interactions among multiple loci, limiting boundary accuracy. In contrast, multiway chromatin interaction data can overcome the limitations of binary interactions by directly capturing high-order interactions that occur simultaneously across multiple regions, thereby providing critical data support for a more comprehensive analysis of three-dimensional chromatin organization.This study proposes HyperDeepTAD: which involves constructing hypergraphs from multiway chromatin interaction data to retain high-order information, inputting transition probability matrices into dynamic convolutional networks to capture local features, obtaining candidate boundaries via BiGRU and residual connections, screening with hypergraph clustering coefficients and using cosine similarity to obtain hierarchical TADs. Experiments show it that outperforms Hi-C-based methods in metrics such as boundary enrichment. The source code is available from https://github.com/liukaihua0213-source/HyperDeepTAD.