Visualizing Interchromosomal Interactions at Sub-Megabase Resolution Using Network Clustering Coefficients

Abstract

Specific interchromosomal interactions involve communication between non-homologous chromosomes, enabling coordinated genomic activities such as gene regulation. However, because these communications are often embedded within a nonspecific and noisy background of contact interactions, it is essential to annotate these interaction patterns at the resolution of genomic positions. Such annotation facilitates clean visualization and comparison with linear genomic features to reveal underlying biological functions. We developed and validated a set of network-based metrics as cross-chromosomal interaction descriptors that bridge complex 3D genome structures and 1D functional genomics. By utilizing graph-theoretic representations, these network-based features succinctly summarize complex inter-chromosomal relationships. We constructed a graph representation of contact interactions derived from Hi-C data and implemented three annotations that capture the distinct "many-body" nature of the interactions. Among these, we demonstrate that {Delta}C4 (a cis-contact-mediated 4-cycle interaction metric) is superior to both {Delta}C3 (a cis-contact-mediated 3-cycle metric) and C4E (a direct 4-cycle metric of trans contacts) at filtering noise and providing the most straightforward interpretation. Applying these metrics to chromosomes 17, 19, and 22 of the GM12878 cell line, we found clear evidence that different chromosomes rely on a shared set of interaction hot spots to communicate. Overall, this network-based framework reveals distinct chromosomal regulation patches and provides insight into how chromosomes associate with each other and organize relative to the nuclear envelope.