Wang Junfeng, Wu Lili, Wei Jingjing, Yan Chaokun, Luo Huimin, Luo Junwei, Guo Fei
School of Software, Henan Polytechnic University, Jiaozuo, 454003, China.
College of Chemical and Environmental Engineering, Anyang Institute of Technology, Anyang, 455000, China.
BMC Genomics. 2025 Apr 5;26(1):342. doi: 10.1186/s12864-025-11531-y.
Chromosomes of species exhibit a variety of high-dimensional organizational features, and chromatin loops, which are fundamental structures in the three-dimensional (3D) structure of the genome. Chromatin loops are visible speckled patterns on Hi-C contact matrix generated by chromosome conformation capture methods. The chromatin loops play an important role in gene expression, and predicting the chromatin loops generated during whole genome interactions is crucial for a deeper understanding of the 3D genome structure and function.
Here, we propose CGLoop, a deep learning based neural network framework that detects chromatin loops in Hi-C contact matrix. CGLoop combines the convolutional neural network (CNN) with Convolutional Block Attention Module (CBAM) and the Bidirectional Gated Recurrent Unit (BiGRU) to capture important features related to chromatin loops by comprehensively analyzing the Hi-C contact matrix, enabling the prediction of candidate chromatin loops. And CGLoop employs a density based clustering method to filter the candidate chromatin loops predicted by the neural network model. Finally, we compared CGloop with other chromatin loops prediction methods on several cell line including GM12878, K562, IMR90, and mESC. The code is available from https://github.com/wllwuliliwll/CGLoop .
The experimental results show that, loops predicted by CGLoop show high APA scores and there is an enrichment of multiple transcription factors and binding proteins at the predicted loops anchors, which outperforms other methods in terms of accuracy and validity of chromatin loops prediction.
物种的染色体呈现出多种高维组织特征，染色质环是基因组三维（3D）结构中的基本结构。染色质环是通过染色体构象捕获方法在Hi-C接触矩阵上可见的斑点图案。染色质环在基因表达中起重要作用，预测全基因组相互作用过程中产生的染色质环对于深入理解3D基因组结构和功能至关重要。
在此，我们提出了CGLoop，一种基于深度学习的神经网络框架，用于检测Hi-C接触矩阵中的染色质环。CGLoop将卷积神经网络（CNN）与卷积块注意力模块（CBAM）和双向门控循环单元（BiGRU）相结合，通过全面分析Hi-C接触矩阵来捕获与染色质环相关的重要特征，从而能够预测候选染色质环。并且CGLoop采用基于密度的聚类方法对神经网络模型预测的候选染色质环进行过滤。最后，我们在包括GM12878、K562、IMR90和mESC在内的几种细胞系上，将CGloop与其他染色质环预测方法进行了比较。代码可从https://github.com/wllwuliliwll/CGLoop获取。
实验结果表明，CGLoop预测的环显示出高APA分数，并且在预测的环锚点处有多种转录因子和结合蛋白的富集，在染色质环预测的准确性和有效性方面优于其他方法。
