Genome-wide ligation-based assays such as Hi-C provide us with an unprecedented opportunity to investigate the spatial organization of the genome. Results of a typical Hi-C experiment are often summarized in a chromosomal contact map, a matrix whose elements reflect the co-location frequencies of genomic loci. To elucidate the complex structural and functional interactions between those genomic loci, networks offer a natural and powerful framework. We propose a novel graph-theoretical framework, the Corrected Gene Proximity (CGP) map to study the effect of the 3D spatial organization of genes in transcriptional regulation. The starting point of the CGP map is a weighted network, the gene proximity map, whose weights are based on the contact frequencies between genes extracted from genome-wide Hi-C data. We derive a null model for the network based on the signal contributed by the 1D genomic distance and use it to “correct” the gene proximity for cell type 3D specific arrangements. The CGP map, therefore, provides a network framework for the 3D structure of the genome on a global scale. On human cell lines, we show that the CGP map can detect and quantify gene co-regulation and co-localization more effectively than the map obtained by raw contact frequencies. Analyzing the expression pattern of metabolic pathways of two hematopoietic cell lines, we find that the relative positioning of the genes, as captured and quantified by the CGP, is highly correlated with their expression change. We further show that the CGP map can be used to form an inter-chromosomal proximity map that allows large-scale abnormalities, such as chromosomal translocations, to be identified. The Corrected Gene Proximity map is a map of the 3D structure of the genome on a global scale. It allows the simultaneous analysis of intra- and inter- chromosomal interactions and of gene co-regulation and co-localization more effectively than the map obtained by raw contact frequencies, thus revealing hidden associations between global spatial positioning and gene expression. The flexible graph-based formalism of the CGP map can be easily generalized to study any existing Hi-C datasets.

中文翻译：

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校正后的基因邻近图，用于使用 Hi-C 数据分析 3D 基因组组织。


Hi-C 等基于全基因组连接的检测为我们提供了前所未有的机会来研究基因组的空间组织。典型的 Hi-C 实验的结果通常总结在染色体接触图上，该矩阵的元素反映了基因组位点的共置频率。为了阐明这些基因组位点之间复杂的结构和功能相互作用，网络提供了一个自然而强大的框架。我们提出了一种新颖的图论框架，即校正基因邻近度（CGP）图，以研究基因的 3D 空间组织在转录调控中的影响。 CGP图的起点是一个加权网络，即基因邻近图，其权重基于从全基因组Hi-C数据中提取的基因之间的接触频率。我们根据 1D 基因组距离贡献的信号得出网络的零模型，并用它来“纠正”细胞类型 3D 特定排列的基因接近度。因此，CGP 图谱为全球范围内的基因组 3D 结构提供了一个网络框架。在人类细胞系上，我们表明 CGP 图谱可以比通过原始接触频率获得的图谱更有效地检测和量化基因共调控和共定位。通过分析两种造血细胞系代谢途径的表达模式，我们发现 CGP 捕获和量化的基因的相对定位与其表达变化高度相关。我们进一步表明，CGP 图谱可用于形成染色体间邻近图谱，从而可以识别大规模异常，例如染色体易位。校正基因邻近图是全球范围内基因组 3D 结构的图。 与通过原始接触频率获得的图谱相比，它可以更有效地同时分析染色体内和染色体间相互作用以及基因共调控和共定位，从而揭示全局空间定位和基因表达之间隐藏的关联。 CGP 图的灵活的基于图形的形式可以很容易地推广到研究任何现有的 Hi-C 数据集。