Li Kai, Zhang Ping, Xu Jinsheng, Wen Zi, Zhang Junying, Zi Zhike, Li Li
Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan 430070, China.
Shenzhen Key Laboratory of Synthetic Genomics, Guangdong Provincial Key Laboratory of Synthetic Genomics, Key Laboratory of Quantitative Synthetic Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China.
Genomics Proteomics Bioinformatics. 2025 Jan 15;22(6). doi: 10.1093/gpbjnl/qzae091.
Chromatin compartmentalization and epigenomic modifications play crucial roles in cell differentiation and disease development. However, precise mapping of chromatin compartment patterns requires Hi-C or Micro-C data at high sequencing depth. Exploring the systematic relationship between epigenomic modifications and compartment patterns remains challenging. To address these issues, we present COCOA, a deep neural network framework using convolution and attention mechanisms to infer fine-scale chromatin compartment patterns from six histone modification signals. COCOA extracts 1D track features through bidirectional feature reconstruction after resolution-specific binning of epigenomic signals. These track features are then cross-fused with contact features using an attention mechanism and transformed into chromatin compartment patterns through residual feature reduction. COCOA demonstrates accurate inference of chromatin compartmentalization at a fine-scale resolution and exhibits stable performance on test sets. Additionally, we explored the impact of histone modifications on chromatin compartmentalization prediction through in silico epigenomic perturbation experiments. Unlike obscure compartments observed in high-depth experimental data at 1-kb resolution, COCOA generates clear and detailed compartment patterns, highlighting its superior performance. Finally, we demonstrate that COCOA enables cell-type-specific prediction of unrevealed chromatin compartment patterns in various biological processes, making it an effective tool for gaining insights into chromatin compartmentalization from epigenomics in diverse biological scenarios. The COCOA Python code is publicly available at https://github.com/onlybugs/COCOA and https://ngdc.cncb.ac.cn/biocode/tools/BT007498.
染色质区室化和表观基因组修饰在细胞分化和疾病发展中起着至关重要的作用。然而，精确绘制染色质区室模式需要高测序深度的Hi-C或Micro-C数据。探索表观基因组修饰与区室模式之间的系统关系仍然具有挑战性。为了解决这些问题，我们提出了COCOA，这是一个使用卷积和注意力机制从六种组蛋白修饰信号推断精细尺度染色质区室模式的深度神经网络框架。COCOA在对表观基因组信号进行分辨率特定的分箱后，通过双向特征重建提取一维轨迹特征。然后，这些轨迹特征使用注意力机制与接触特征进行交叉融合，并通过残差特征约简转换为染色质区室模式。COCOA在精细尺度分辨率下展示了对染色质区室化的准确推断，并在测试集上表现出稳定的性能。此外，我们通过计算机模拟表观基因组扰动实验探索了组蛋白修饰对染色质区室化预测的影响。与在1 kb分辨率的高深度实验数据中观察到的模糊区室不同，COCOA生成了清晰详细的区室模式，突出了其优越的性能。最后，我们证明COCOA能够在各种生物过程中对未揭示的染色质区室模式进行细胞类型特异性预测，使其成为在不同生物场景中从表观基因组学深入了解染色质区室化的有效工具。COCOA的Python代码可在https://github.com/onlybugs/COCOA和https://ngdc.cncb.ac.cn/biocode/tools/BT007498上公开获取。
