Galan Silvia, Serra François, Marti-Renom Marc A
CNAG-CRG, Centre for Genomic Regulation (CRG), Barcelona Institute of Science and Technology (BIST), Baldiri i Reixac 4, 08028 Barcelona, Spain.
NAR Genom Bioinform. 2022 Mar 8;4(1):lqac021. doi: 10.1093/nargab/lqac021. eCollection 2022 Mar.
Genome-wide profiling of long-range interactions has revealed that the CCCTC-Binding factor (CTCF) often anchors chromatin loops and is enriched at boundaries of the so-called Topologically Associating Domains, which suggests that CTCF is essential in the 3D organization of chromatin. However, the systematic topological classification of pairwise CTCF-CTCF interactions has not been yet explored. Here, we developed a computational pipeline able to classify all CTCF-CTCF pairs according to their chromatin interactions from Hi-C experiments. The interaction profiles of all CTCF-CTCF pairs were further structurally clustered using self-organizing feature maps and their functionality characterized by their epigenetic states. The resulting clusters were then input to a convolutional neural network aiming at the detecting chromatin loops from Hi-C interaction matrices. Our new method, called LOOPbit, is able to automatically detect significant interactions with a higher proportion of enhancer-promoter loops compared to other callers. Our highly specific loop caller adds a new layer of detail to the link between chromatin structure and function.
全基因组范围内的长程相互作用分析表明，CCCTC结合因子（CTCF）常常锚定染色质环，并在所谓的拓扑相关结构域的边界处富集，这表明CTCF在染色质的三维组织中至关重要。然而，成对CTCF-CTCF相互作用的系统拓扑分类尚未得到探索。在此，我们开发了一种计算流程，能够根据来自Hi-C实验的染色质相互作用对所有CTCF-CTCF对进行分类。使用自组织特征映射对所有CTCF-CTCF对的相互作用谱进行进一步的结构聚类，并通过其表观遗传状态对其功能进行表征。然后将所得聚类输入到一个卷积神经网络中，旨在从Hi-C相互作用矩阵中检测染色质环。我们的新方法名为LOOPbit，与其他调用程序相比，它能够自动检测出显著相互作用，且增强子-启动子环的比例更高。我们高度特异的环调用程序为染色质结构与功能之间的联系增添了新的细节层次。
