Xu Jinsheng, Zhang Ping, Sun Weicheng, Zhang Junying, Zhang Wenxue, Hou Chunhui, Li Li
Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan 430070, China.
Food Science Program, Division of Food, Nutrition and Exercise Sciences, University of Missouri, 1406 E Rollins Street, Columbia, MO 65211, USA.
Biology (Basel). 2023 Sep 3;12(9):1203. doi: 10.3390/biology12091203.
The recently emerging high-throughput Pore-C (HiPore-C) can identify whole-genome high-order chromatin multi-way interactions with an ultra-high output, contributing to deciphering three-dimensional (3D) genome organization. However, it also brings new challenges to relevant data analysis. To alleviate this problem, we proposed the EpiMCI, a model for multi-way chromatin interaction prediction based on a hypergraph neural network with epigenomic signals as the input. The EpiMCI integrated separate hyperedge representations with coupling hyperedge information and obtained AUCs of 0.981 and 0.984 in the GM12878 and K562 datasets, respectively, which outperformed the current available method. Moreover, the EpiMCI can be applied to denoise the HiPore-C data and improve the data quality efficiently. Furthermore, the vertex embeddings extracted from the EpiMCI reflected the global chromatin architecture accurately. The principal component analysis suggested that it was well aligned with the activities of genomic regions at the chromatin compartment level. Taken together, the EpiMCI can accurately predict multi-way chromatin interactions and can be applied to studies relying on chromatin architecture.
最近出现的高通量Pore-C（HiPore-C）能够以超高产量识别全基因组高阶染色质多向相互作用，有助于解析三维（3D）基因组组织。然而，它也给相关数据分析带来了新挑战。为缓解这一问题，我们提出了EpiMCI，这是一种基于超图神经网络的多向染色质相互作用预测模型，以表观基因组信号作为输入。EpiMCI将单独的超边表示与耦合超边信息相结合，在GM12878和K562数据集中分别获得了0.981和0.984的曲线下面积（AUC），优于当前可用方法。此外，EpiMCI可用于对HiPore-C数据进行去噪并有效提高数据质量。此外，从EpiMCI中提取的顶点嵌入准确反映了全局染色质结构。主成分分析表明，它在染色质区室水平上与基因组区域的活性高度一致。综上所述，EpiMCI能够准确预测多向染色质相互作用，并可应用于依赖染色质结构的研究。
