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

Abstract

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 (Xi), 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.

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Fig. 1: HiCorr and LoopDenoise reveal chromatin loops from noisy Hi-C datasets.
Fig. 2: LoopEnhance enables sensitive and robust loop calling from low-depth Hi-C data.
Fig. 3: DeepLoop outputs convergent Hi-C loop profiles regardless of read depth and digestion resolution.
Fig. 4: DeepLoop identifies chromatin interactions from low-depth and single-cell Hi-C data.
Fig. 5: Homolog-specific chromatin interactions are associated with imprinting and DMR.
Fig. 6: Homolog-specific chromatin interactions are associated with X-inactivation.
Fig. 7: Allelic DeepLoop maps detect and functionally characterize large heterozygous SVs.
Fig. 8: Allelic DeepLoop maps pinpoint common SNPs that affect chromatin loops.

Data availability

Accession numbers for third-party data used in this study can be found in Supplementary Table 1. The raw data of H9 Hi-C and 4C–seq generated in this study, and reanalyzed published data, can be found at accession no. GSE167200. The 40 Hi-C datasets analyzed by DeepLoop can be found at https://hiview.case.edu/public/DeepLoop/.

Code availability

The code is available is available at Zenodo (https://doi.org/10.5281/zenodo.6495831) and github (https://github.com/JinLabBioinfo/DeepLoop).

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Acknowledgements

This work is supported by grants from the National Institutes of Health (nos. R01HG009658 to F.J. and R01DK113185 to Y.L.) and Mount Sinai Health Care Foundation (nos. OSA510113 to F.J. and OSA510114 to Y.L.). F.J. is also supported by a subaward from the University of Miami (no. NIH U01AG072579) and a Cancer Data Sciences pilot grant from Case Comprehensive Cancer Center Support Grant (no. NIH P30CA043703). J.L. is supported in part by National Science Foundation grant nos. CCF-2006780 and CCF-1815139. D.P. is supported by a NIH training grant (no. T32HL007567) and a fellowship from the Callahan Foundation. This work made use of the High-Performance Computing Resource in the Core Facility for Advanced Research Computing at Case Western Reserve University.

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Contributions

F.J., J.L. and Y.L. designed the study. S.Z. and D.P. performed analyses. L.L. and J.C. performed validation experiments. W.X., X.L. and N.P. helped twith Hi-C data analyses. M.W., J.S., D.S. and P.F. helped analyze mESC pcHi-C data. S.Z., D.P. and F.J. wrote the manuscript with help from all the authors.

Corresponding authors

Correspondence to Yan Li, Jing Li or Fulai Jin.

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The authors declare no competing interests.

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Extended data

Extended Data Fig. 1 LoopDenoise training procedure, performance and visualization.

a, Detailed LoopDenoise convolutional autoencoder model architecture showing five convolution layers, two in the encoding path using eight 13 × 13 filters, two transpose convolution layers in the decoding path using eight 2 × 2 filters and one final convolution layer using a single 13 × 13 filter. The matrices dimensions of each layer output were also shown. Each layer is visualized by the filters used, the output of convolving the input with this filter, the result of applying ReLU activation and the result of max pooling. The convolution operation is denoted by *. b, Venn diagram showing the reproducible loop pixels between three human fetal brain replicates. The table showing the number of overlapped pixels between significant pixels in the pooled data and each part of pixels shown in the Venn diagram. The pixels that are significant in both pooled data and at least one of the three replicates are the training target in the LoopDenoise model (P < 0.05, negative binomial test). The significance of loop pixels come from the negative binomial test wrapped in HiCorr package. c, Pairwise reproducibility at pixel level (defined as the fraction of common ones when calling the same number of loop pixels from two datasets) between biological replicates of human fetal cortex Hi-C data, when the same numbers of the loop pixels were called. d, The heatmap examples from 7 locus in three human fetal brain replicates, and LoopDenoise output showing more reproducible contact patterns.

Extended Data Fig. 2 LoopDenoise generalization across cell types and species.

a, Eight heatmap examples in GM12878, the highlight row is the output from LoopDenoise. b, The distance distribution of top 300K pixels in H1(hESC), GM12878, IMR90 and mESC. Upper and lower limits of boxes indicate interquartile ranges, center lines indicate median values, whiskers indicate values with a maximum of 1.5 times the interquartile range and outliers indicate values beyond 1.5 times the interquartile range. c, The number of loops pixels with at least one anchor overlapped with ChIP-seq peaks out of top 300K pixels. d, Density plots show the distribution of distances between loop anchors (top 100K loop pixels used) and their nearest ChIP-seq peaks in GM12878, IMR90, H1(hESC) and mESC. e, The heatmap examples of six loci with known long-range gene regulation. The height of browser tracks indicating the raw counts of ChIP-seq.

Extended Data Fig. 3 LoopDenoise enables the quantitation of dynamic chromatin interactions.

a, Scatterplots showing the pixel-level correlation between CP and GZ sample in human fetal cortex before and after LoopDenoise. The R-square values were also shown in the plots. b, GO analyses of genes associated with GZ- or CP-specific loops. Fisher’s Exact test was used to measure the gene-enrichment in annotation terms. c, The contact heatmaps of selected gene loci with top GZ- or CP-specific loop pixels. ATAC-seq tracks in CP (yellow) and GZ (blue) are also included for comparison. The height of browser tracks indicating the raw counts of ATAC-seq.

Extended Data Fig. 4 Compare the performance of different pipelines on 6-cutter and 4-cutter Hi-C data in GM12878 cells.

For 4-cutter Hi-C datasets, we chose a 94M down-sampled dataset (1/16 of the original depth) used in HiCPlus, HiCNN2 and SRHiC studies, and the 1.35 billion full-depth as reference. For 6-cutter Hi-C datasets, we chose a 50M down-sampled dataset and the 380M full-depth as reference. For locus chr5:87,964,000-88,764000, the left side showed the contact heatmaps from 6-cutter (HindIII) GM12878 Hi-C processed by different pipelines (colored in background). The right side showed the 4-cutter (MboI) GM12878 Hi-C. The height of browser tracks indicating the raw counts of ChIP-seq.

Extended Data Fig. 5 Compare the consistency of Hi-C and Micro-C in H1.

a, Similar to Fig. 3a, b, more heatmap examples at 4 loci. b, Size breakdown of recovered micro-C HICCUPS loops by 50M deep HindIII- or DpnII- Hi-C after enhancement.

Extended Data Fig. 6 DeepLoop reveals tissue-specific loop interactions for low-depth Hi-C data.

Applying LoopEnhance to low depth Hi-C data from 14 human tissues. Contact heatmaps of three tissue-specifically expressed genes in all the tissues were shown. a, ALB, highly expressed in liver. b, MYOZ2, highly expressed in heart tissues. c, ADD2, highly expressed in brain tissues.

Extended Data Fig. 7 DeepLoop reveals cell type specific loop interactions from sn-m3C-seq data.

Same as Fig. 4e,f, single cells from the same cell type are pooled and enhanced by DeepLoop. The tSNE plots show the identities of each cell population (left) and the methylation level at the locus of interest (right).

Extended Data Fig. 8 Large heterozygous deletions and inversions detected by allelic DeepLoop analysis.

a, The scatterplots highlight the loop pixels within the entire four SVs region (two inversions and two deletions). b, The contact heatmaps of paternal deletion Del-chr14 and maternal deletion Del-chr22. c, The contact heatmaps of Inv-chr7. d, The genome track of Inv-chr7 shows the chromatin interactions, CTCF and H3K27ac binding on the un-inverted allele and ‘inversion-fix’ allele. In this region, the un-inverted paternal genome has A1-A4 and A5-A6 cross-boundary CTCF loops. The maternal inversion created new A1-A5 and A4-A6 cross-boundary loops due to the inverted orientation the CTCF motifs. Note that in paternal genome, the A1-A4 loop encompass multiple enhancers, while in the inverted maternal genome the A1-A5 loop lack enhancers. e, The gene expression level of gene CCZ1 in two alleles. The height of browser tracks indicating the raw counts of ChIP-seq.

Extended Data Fig. 9 The contact heatmaps and browser snapshots of 24 loci containing 27 SNPs associated with both allelic CTCF binding and allelic DNA looping.

For each SNP, the paternal (blue) and maternal (red) genotypes are included. The allelic loops are circled in the heatmaps. The CTCF motif orientation are indicated with triangles. The height of browser tracks indicating the raw counts of ChIP-seq.

Extended Data Fig. 10 Allele-specific chromatin loops regulate gene expression.

a, 3C assays showing the loss of chromatin loop between the SNP (highlight in yellow) and ACBD7 locus after displacing CTCF binding with either dCas9-KRAB or dCas9 protein. b,c, Bar plots showing the changes of allelic gene expression upon blocking CTCF loops with dCas9 or dCas9-KRAB. df, CTCF blocking experiments at GPNMB locus. n = 2 biologically independent experiments. All data are presented as means ± SEM from 4 replicated experiments. **P < 0.01, ***P < 0.001. NS, no significant difference. Two-sided Wilcoxon test. The height of browser tracks indicating the raw counts of ChIP-seq.

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Zhang, S., Plummer, D., Lu, L. et al. DeepLoop robustly maps chromatin interactions from sparse allele-resolved or single-cell Hi-C data at kilobase resolution. Nat Genet 54, 1013–1025 (2022). https://doi.org/10.1038/s41588-022-01116-w

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