iceDP: identifying inter-chromatin engagement via density peaks clustering algorithm

Chromatin topological structure is critical for gene regulation. Hi-C based experiments have significantly advanced our understanding chromatin organization. Numerous computational tools have been developed to identify various structural levels of chromatin, ranging from compartments to loops. However, there remains a lack of specialized tools for identifying non-homologous inter-chromatin contacts (NHCCs), which play important roles in chromosome territories. In this study, we present iceDP, a tool that leverages the Density Peaks clustering algorithm to identify local high-density regions within inter-chromatin. These regions undergo two subsequent filtering steps to eliminate obvious false positives. When applied to three Hi-C datasets, iceDP accurately identified known NHCCs, including olfactory receptor genes in mature olfactory sensory neurons and Polycomb repressive complex-regulated developmental genes in mouse embryonic stem cells (mESCs). Notably, iceDP also uncovered previously unreported transcriptionally active NHCCs. Compared to diffHiC and FitHiC, iceDP exhibited superior performance with the highest positive rate. Moreover, iceDP is compatible with a wide range of chromatin conformation capture techniques, including in-situ Hi-C, Micro-C, HiChIP, and BL-HiC, demonstrating its versatility and utility.