BACKGROUND Current Hi-C technologies for chromosome conformation capture allow to understand a broad spectrum of functional interactions between genome elements. Although significant progress has been made into analysis of Hi-C data to identify biologically significant features, many questions still remain open, in particular regarding potential biological significance of various topological features that are characteristic for chromatin interaction networks. RESULTS It has been previously observed that promoter capture Hi-C (PCHi-C) interaction networks tend to separate easily into well-defined connected components that can be related to certain biological functionality, however, such evidence was based on manual analysis and was limited. Here we present a novel method for analysis of chromatin interaction networks aimed towards identifying characteristic topological features of interaction graphs and confirming their potential significance in chromatin architecture. Our method automatically identifies all connected components with an assigned significance score above a given threshold. These components can be subjected afterwards to different assessment methods for their biological role and/or significance. The method was applied to the largest PCHi-C data set available to date that contains interactions for 17 haematopoietic cell types. The results demonstrate strong evidence of well-pronounced component structure of chromatin interaction networks and provide some characterisation of this component structure. We also performed an indicative assessment of potential biological significance of identified network components with the results confirming that the network components can be related to specific biological functionality. CONCLUSIONS The obtained results show that the topological structure of chromatin interaction networks can be well described in terms of isolated connected components of the network and that formation of these components can be often explained by biological features of functionally related gene modules. The presented method allows automatic identification of all such components and evaluation of their significance in PCHi-C dataset for 17 haematopoietic cell types. The method can be adapted for exploration of other chromatin interaction data sets that include information about sufficiently large number of different cell types, and, in principle, also for analysis of other kinds of cell type-specific networks.

中文翻译：

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染色质相互作用网络的拓扑结构分析。

背景技术当前用于染色体构象捕获的Hi-C技术允许理解基因组元件之间的广泛功能相互作用。尽管在分析Hi-C数据以识别生物学重要特征方面已取得重大进展，但许多问题仍然悬而未决，特别是关于染色质相互作用网络所特有的各种拓扑特征的潜在生物学意义。结果以前已经观察到启动子捕获Hi-C（PCHi-C）相互作用网络倾向于容易地分离成可以与某些生物学功能相关的定义明确的连接组件，但是，这种证据是基于人工分析的，并且是有限的。在这里，我们提出了一种用于染色质相互作用网络分析的新方法，旨在识别相互作用图的特征拓扑特征并确认其在染色质结构中的潜在意义。我们的方法会自动识别所有关联的组件，这些组件的指定显着性得分高于给定阈值。这些成分的生物学作用和/或重要性随后可以接受不同的评估方法。该方法应用于迄今为止最大的PCHi-C数据集，其中包含针对17种造血细胞类型的相互作用。结果证明了染色质相互作用网络的清晰发音的组分结构的有力证据，并提供了该组分结构的一些表征。我们还对鉴定出的网络组件的潜在生物学意义进行了指示性评估，结果证实了网络组件可以与特定的生物学功能相关。结论所获得的结果表明，染色质相互作用网络的拓扑结构可以用网络中孤立的连接组件很好地描述，而这些组件的形成通常可以通过功能相关基因模块的生物学特征来解释。提出的方法可以自动识别所有此类成分，并在PCHi-C数据集中评估17种造血细胞类型的重要性。该方法可适用于探索其他染色质相互作用数据集，其中包括关于足够大量不同细胞类型的信息，