DeepLoop robustly maps chromatin interactions from sparse allele-resolved or single-cell Hi-C data at kilobase resolution

DeepLoop is a modular Hi-C processing workflow that enables kilobase-resolution analysis of sparse data. Reanalysis of published data demonstrates that DeepLoop can identify allele-specific chromatin loops and large heterozygous structural variants. Mapping chromatin loops from noisy Hi-C heatmaps remains a major challenge. Here we present DeepLoop, which performs rigorous bias correction followed by deep-learning-based signal enhancement for robust chromatin interaction mapping from low-depth Hi-C data. DeepLoop enables loop-resolution, single-cell Hi-C analysis. It also achieves a cross-platform convergence between different Hi-C protocols and micrococcal nuclease (micro-C). DeepLoop allowed us to map the genetic and epigenetic determinants of allele-specific chromatin interactions in the human genome. We nominate new loci with allele-specific interactions governed by imprinting or allelic DNA methylation. We also discovered that, in the inactivated X chromosome (X-i), local loops at the DXZ4 'megadomain' boundary escape X-inactivation but the FIRRE 'superloop' locus does not. Importantly, DeepLoop can pinpoint heterozygous single-nucleotide polymorphisms and large structure variants that cause allelic chromatin loops, many of which rewire enhancers with transcription consequences. Taken together, DeepLoop expands the use of Hi-C to provide loop-resolution insights into the genetics of the three-dimensional genome.

Automated summary

DeepLoop is a modular Hi-C processing workflow that enables robust chromatin interaction mapping from low-depth Hi-C data through deep-learning-based signal enhancement. It can identify allele-specific chromatin loops and structural variants, providing loop-resolution insights into the genetics of the three-dimensional genome.