Abstract
Sequential Oligopaints DNA FISH is an imaging technique that measures higher-order genome folding at single-allele resolution via multiplexed, probe-based tracing. Currently there is a paucity of algorithms to identify 3D genome features in sequential Oligopaints data. Here, we present FISHnet, a graph theory method based on optimization of network modularity to detect chromatin domains in pairwise distance matrices. FISHnet sensitively and specifically identifies domains and boundaries in both simulated and real single-allele imaging data and provides statistical tests for the identification of cell-type-specific domains-like folding patterns. Application of FISHnet across multiple published Oligopaints datasets confirms that nested domains consistent with TADs and subTADs are not an emergent property of ensemble Hi-C data but also observable on single alleles. We make FISHnet code freely available to the scientific community, thus enabling future studies aiming to elucidate the role of single-allele folding variation on genome function.