Eukaryotic genomes are highly organised within the nucleus of a cell, allowing widely dispersed regulatory elements such as enhancers to interact with gene promoters through physical contacts in three-dimensional space. Recent chromosome conformation capture methodologies such as Hi-C have enabled the analysis of interacting regions of the genome providing a valuable insight into the three-dimensional organisation of the chromatin in the nucleus, including chromosome compartmentalisation and gene expression. Complicating the analysis of Hi-C data, however, is the massive amount of identified interactions, many of which do not directly drive gene function, thus hindering the identification of potentially biologically functional 3D interactions. In this review, we collate and examine the downstream analysis of Hi-C data with particular focus on methods that prioritise potentially functional interactions. We classify three groups of approaches: structural-based discovery methods, e.g. A/B compartments and topologically associated domains, detection of statistically significant chromatin interactions, and the use of epigenomic data integration to narrow down useful interaction information. Careful use of these three approaches is crucial to successfully identifying potentially functional interactions within the genome.

中文翻译：

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透过树木看森林：优先考虑 Hi-C 的潜在功能交互

真核基因组在细胞核内高度组织化，允许广泛分散的调节元件（例如增强子）通过三维空间中的物理接触与基因启动子相互作用。最近的染色体构象捕获方法（例如 Hi-C）已经能够分析基因组的相互作用区域，为细胞核中染色质的三维组织（包括染色体区室化和基因表达）提供有价值的见解。然而，使 Hi-C 数据分析变得复杂的是大量已识别的相互作用，其中许多相互作用并不直接驱动基因功能，从而阻碍了潜在生物学功能 3D 相互作用的识别。在这篇综述中，我们整理并检查了 Hi-C 数据的下游分析，特别关注优先考虑潜在功能相互作用的方法。我们将方法分为三组：基于结构的发现方法，例如 A/B 区室和拓扑相关域、检测统计上显着的染色质相互作用，以及使用表观基因组数据集成来缩小有用的相互作用信息的范围。仔细使用这三种方法对于成功识别基因组内潜在的功能相互作用至关重要。