CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing, 100101, China.
School of Life Science, University of Chinese Academy of Sciences, Beijing, 100049, China.
Adv Sci (Weinh). 2023 Sep;10(27):e2301058. doi: 10.1002/advs.202301058. Epub 2023 Jul 28.
Deciphering variations in chromosome conformations based on bulk three-dimensional (3D) genomic data from heterogenous tissues is a key to understanding cell-type specific genome architecture and dynamics. Surprisingly, computational deconvolution methods for high-throughput chromosome conformation capture (Hi-C) data remain very rare in the literature. Here, a deep convolutional neural network (CNN), deconvolve bulk Hi-C data (deCOOC) that remarkably outperformed all the state-of-the-art tools in the deconvolution task is developed. Interestingly, it is noticed that the chromatin accessibility or the Hi-C contact frequency alone is insufficient to explain the power of deCOOC, suggesting the existence of a latent embedded layer of information pertaining to the cell type specific 3D genome architecture. By applying deCOOC to in-house-generated bulk Hi-C data from visceral and subcutaneous adipose tissues, it is found that the characteristic chromatin features of M2 cells in the two anatomical loci are distinctively bound to different physiological functionalities. Taken together, deCOOC is both a reliable Hi-C data deconvolution method and a powerful tool for functional extraction of 3D genome architecture.
基于异质组织的大规模三维（3D）基因组数据来破译染色体构象的变异是理解细胞类型特异性基因组结构和动态的关键。令人惊讶的是，高通量染色体构象捕获（Hi-C）数据的计算去卷积方法在文献中仍然非常罕见。在这里，开发了一种深度卷积神经网络（CNN），用于对大规模 Hi-C 数据（deCOOC）进行去卷积，该方法在去卷积任务中的表现明显优于所有最先进的工具。有趣的是，人们注意到染色质可及性或 Hi-C 接触频率本身不足以解释 deCOOC 的强大功能，这表明存在与细胞类型特异性 3D 基因组结构相关的潜在嵌入信息层。通过将 deCOOC 应用于内脏和皮下脂肪组织中生成的内部批量 Hi-C 数据，发现两个解剖部位中 M2 细胞的特征性染色质特征与不同的生理功能明显相关。总之，deCOOC 既是一种可靠的 Hi-C 数据去卷积方法，也是一种提取 3D 基因组结构功能的强大工具。
