The three-dimensional structure of the human genome has been proven to have a significant functional impact on gene expression. The high-order spatial chromatin is organised first by looping mediated by multiple protein factors, and then it is further formed into larger structures of topologically associated domains (TADs) or chromatin contact domains (CCDs), followed by A/B compartments and finally the chromosomal territories (CTs). The genetic variation observed in human population influences the multi-scale structures, posing a question regarding the functional impact of structural variants reflected by the variability of the genes expression patterns. The current methods of evaluating the functional effect include eQTLs analysis which uses statistical testing of influence of variants on spatially close genes. Rarely, non-coding DNA sequence changes are evaluated by their impact on the biomolecular interaction network (BIN) reflecting the cellular interactome that can be analysed by the classical graph-theoretic algorithms. Therefore, in the second part of the review, we introduce the concept of BIN, i.e. a meta-network model of the complete molecular interactome developed by integrating various biological networks. The BIN meta-network model includes DNA-protein binding by the plethora of protein factors as well as chromatin interactions, therefore allowing connection of genomics with the downstream biomolecular processes present in a cell. As an illustration, we scrutinise the chromatin interactions mediated by the CTCF protein detected in a ChIA-PET experiment in the human lymphoblastoid cell line GM12878. In the corresponding BIN meta-network the DNA spatial proximity is represented as a graph model, combined with the Proteins-Interaction Network (PIN) of human proteome using the Gene Association Network (GAN). Furthermore, we enriched the BIN with the signalling and metabolic pathways and Gene Ontology (GO) terms to assert its functional context. Finally, we mapped the Single Nucleotide Polymorphisms (SNPs) from the GWAS studies and identified the chromatin mutational hot-spots associated with a significant enrichment of SNPs related to autoimmune diseases. Afterwards, we mapped Structural Variants (SVs) from healthy individuals of 1000 Genomes Project and identified an interesting example of the missing protein complex associated with protein Q6GYQ0 due to a deletion on chromosome 14. Such an analysis using the meta-network BIN model is therefore helpful in evaluating the influence of genetic variation on spatial organisation of the genome and its functional effect in a cell.

人类基因组的三维结构已被证明对基因表达具有显着的功能影响。高阶空间染色质首先通过由多种蛋白质因子介导的成环组织，然后进一步形成更大的拓扑相关域（TAD）或染色质接触域（CCD）结构，然后是A / B区室，最后是染色体区域（CT）。在人群中观察到的遗传变异影响多尺度结构，提出了一个关于基因表达模式变异所反映的结构变异的功能影响的问题。目前评估功能效应的方法包括 eQTLs 分析，它使用变异对空间接近基因的影响的统计测试。很少，非编码 DNA 序列变化通过它们对生物分子相互作用网络 (BIN) 的影响来评估，该网络反映了可以通过经典图论算法分析的细胞相互作用组。因此，在评论的第二部分，我们介绍了BIN的概念，即通过整合各种生物网络开发的完整分子相互作用组的元网络模型。BIN 元网络模型包括由大量蛋白质因子结合的 DNA-蛋白质以及染色质相互作用，因此允许将基因组学与细胞中存在的下游生物分子过程联系起来。作为说明，我们仔细检查了在人类淋巴母细胞系 GM12878 中的 ChIA-PET 实验中检测到的 CTCF 蛋白介导的染色质相互作用。在相应的 BIN 元网络中，DNA 空间邻近性表示为图形模型，并使用基因关联网络 (GAN) 与人类蛋白质组的蛋白质相互作用网络 (PIN) 相结合。此外，我们用信号和代谢途径和基因本体论 (GO) 术语丰富了 BIN，以断言其功能背景。最后，我们绘制了来自 GWAS 研究的单核苷酸多态性 (SNP)，并确定了与自身免疫性疾病相关 SNP 显着富集相关的染色质突变热点。之后，我们绘制了来自 1000 基因组计划的健康个体的结构变异体 (SV)，并确定了一个有趣的例子，即由于第 14 号染色体上的缺失，与蛋白质 Q6GYQ0 相关的缺失蛋白质复合物。