Highly rearranged chromosomes reveal uncoupling between genome topology and gene expression

Abstract

Chromatin topology is intricately linked to gene expression, yet its functional requirement remains unclear. Here, we comprehensively assessed the interplay between genome topology and gene expression using highly rearranged chromosomes (balancers) spanning ~75% of the Drosophila genome. Using transheterozyte (balancer/wild-type) embryos, we measured allele-specific changes in topology and gene expression in cis, while minimizing trans effects. Through genome sequencing, we resolved eight large nested inversions, smaller inversions, duplications and thousands of deletions. These extensive rearrangements caused many changes to chromatin topology, disrupting long-range loops, topologically associating domains (TADs) and promoter interactions, yet these are not predictive of changes in expression. Gene expression is generally not altered around inversion breakpoints, indicating that mis-appropriate enhancer–promoter activation is a rare event. Similarly, shuffling or fusing TADs, changing intra-TAD connections and disrupting long-range inter-TAD loops does not alter expression for the majority of genes. Our results suggest that properties other than chromatin topology ensure productive enhancer–promoter interactions.

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Fig. 1: Genomic variation in balancer and wild-type chromosomes.
Fig. 2: Impact of chromosomal rearrangements on gene expression.
Fig. 3: Extensive changes in TADs have limited impact on gene expression.
Fig. 4: Changes in promoter contacts and their relationship to gene expression.
Fig. 5: Loss of long-range chromatin loops has little impact on gene expression.
Fig. 6: Chromatin organization and expression around a 38-kb inversion.

Data availability

All raw data, which consist of 75 demultiplexed files, were submitted to ArrayExpress (https://www.ebi.ac.uk/arrayexpress/browse.html) under accession numbers E-MTAB-7510 (whole-genome and mate-pair sequencing), E-MTAB-7512 (Hi-C), E-MTAB-7513 (Capture-C) and E-MTAB-7511 (RNA-Seq). The Hi-C contact maps, RNA-Seq read counts and other processed data are available on the Furlong Laboratory web page at http://furlonglab.embl.de/data.

Code availability

Custom code used for the analysis is available at https://github.com/ajank/balancer-paper.

References

  1. 1.

    Bulger, M. & Groudine, M. Functional and mechanistic diversity of distal transcription enhancers. Cell 144, 327–339 (2011).

    CAS  Article  Google Scholar 

  2. 2.

    Levine, M. Transcriptional enhancers in animal development and evolution. Curr. Biol. 20, R754–R763 (2010).

    CAS  Article  Google Scholar 

  3. 3.

    Spitz, F. & Furlong, E. E. M. Transcription factors: from enhancer binding to developmental control. Nat. Rev. Genet. 13, 613–626 (2012).

    CAS  Article  Google Scholar 

  4. 4.

    Furlong, E. E. M. & Levine, M. Developmental enhancers and chromosome topology. Science 361, 1341–1345 (2018).

    CAS  Article  Google Scholar 

  5. 5.

    Jin, F. et al. A high-resolution map of the three-dimensional chromatin interactome in human cells. Nature 503, 290–294 (2013).

    CAS  Article  Google Scholar 

  6. 6.

    Dixon, J. R. et al. Topological domains in mammalian genomes identified by analysis of chromatin interactions. Nature 485, 376–380 (2012).

    CAS  Article  Google Scholar 

  7. 7.

    Nora, E. P. et al. Spatial partitioning of the regulatory landscape of the X-inactivation centre. Nature 485, 381–385 (2012).

    CAS  Article  Google Scholar 

  8. 8.

    Rao, S. S. P. et al. A 3D map of the human genome at kilobase resolution reveals principles of chromatin looping. Cell 159, 1665–1680 (2014).

    CAS  Article  Google Scholar 

  9. 9.

    Sexton, T. et al. Three-dimensional folding and functional organization principles of the Drosophila genome. Cell 148, 458–472 (2012).

    CAS  Article  Google Scholar 

  10. 10.

    Lupiáñez, D. G. et al. Disruptions of topological chromatin domains cause pathogenic rewiring of gene-enhancer interactions. Cell 161, 1012–1025 (2015).

    Article  Google Scholar 

  11. 11.

    Franke, M. et al. Formation of new chromatin domains determines pathogenicity of genomic duplications. Nature 538, 265–269 (2016).

    CAS  Article  Google Scholar 

  12. 12.

    Tsujimura, T. et al. A discrete transition zone organizes the topological and regulatory autonomy of the adjacent Tfap2c and Bmp7 genes. PLoS Genet. 11, e1004897 (2015).

    Article  Google Scholar 

  13. 13.

    Narendra, V. et al. CTCF establishes discrete functional chromatin domains at the Hox clusters during differentiation. Science 347, 1017–1021 (2015).

    CAS  Article  Google Scholar 

  14. 14.

    Guo, Y. et al. CRISPR inversion of CTCF sites alters genome topology and enhancer/promoter function. Cell 162, 900–910 (2015).

    CAS  Article  Google Scholar 

  15. 15.

    Lettice, L. A. et al. Enhancer-adoption as a mechanism of human developmental disease. Hum. Mutat. 32, 1492–1499 (2011).

    CAS  Article  Google Scholar 

  16. 16.

    Northcott, P. A. et al. Enhancer hijacking activates GFI1 family oncogenes in medulloblastoma. Nature 511, 428–434 (2014).

    CAS  Article  Google Scholar 

  17. 17.

    Flavahan, W. A. et al. Insulator dysfunction and oncogene activation in IDH mutant gliomas. Nature 529, 110–114 (2016).

    CAS  Article  Google Scholar 

  18. 18.

    Hnisz, D. et al. Activation of proto-oncogenes by disruption of chromosome neighborhoods. Science 351, 1454–1458 (2016).

    CAS  Article  Google Scholar 

  19. 19.

    Northcott, P. A. et al. The whole-genome landscape of medulloblastoma subtypes. Nature 547, 311–317 (2017).

    CAS  Article  Google Scholar 

  20. 20.

    Weischenfeldt, J. et al. Pan-cancer analysis of somatic copy-number alterations implicates IRS4 and IGF2 in enhancer hijacking. Nat. Genet. 49, 65–74 (2017).

    CAS  Article  Google Scholar 

  21. 21.

    Shen, Y. et al. Deep functional analysis of synII, a 770-kilobase synthetic yeast chromosome. Science 355, eaaf4791 (2017).

    Article  Google Scholar 

  22. 22.

    Shao, Y. et al. Creating a functional single-chromosome yeast. Nature 560, 331–335 (2018).

    CAS  Article  Google Scholar 

  23. 23.

    Lee, H. et al. Effects of gene dose, chromatin, and network topology on expression in Drosophila melanogaster. PLoS Genet. 12, e1006295 (2016).

    Article  Google Scholar 

  24. 24.

    Meadows, L. A., Chan, Y. S., Roote, J. & Russell, S. Neighbourhood continuity is not required for correct testis gene expression in Drosophila. PLoS Biol. 8, e1000552 (2010).

    CAS  Article  Google Scholar 

  25. 25.

    Rodríguez-Carballo, E. et al. The HoxD cluster is a dynamic and resilient TAD boundary controlling the segregation of antagonistic regulatory landscapes. Genes Dev. 31, 2264–2281 (2017).

    Article  Google Scholar 

  26. 26.

    Nora, E. P. et al. Targeted degradation of CTCF decouples local insulation of chromosome domains from genomic compartmentalization. Cell 169, 930–944.e22 (2017).

    CAS  Article  Google Scholar 

  27. 27.

    Splinter, E. et al. CTCF mediates long-range chromatin looping and local histone modification in the β-globin locus. Genes Dev. 20, 2349–2354 (2006).

    CAS  Article  Google Scholar 

  28. 28.

    Rao, S. S. P. et al. Cohesin loss eliminates all loop domains. Cell 171, 305–320.e24 (2017).

    CAS  Article  Google Scholar 

  29. 29.

    Schwarzer, W. et al. Two independent modes of chromatin organization revealed by cohesin removal. Nature 551, 51–56 (2017).

    Article  Google Scholar 

  30. 30.

    Wutz, G. et al. Topologically associating domains and chromatin loops depend on cohesin and are regulated by CTCF, WAPL, and PDS5 proteins. EMBO J. 36, 3573–3599 (2017).

    CAS  Article  Google Scholar 

  31. 31.

    Oster, I. I. A new crossing-over suppressor in chromosome 2 effective in the presence of heterologous inversions. Drosophila Inform. Serv. 30, 145 (1956).

    Google Scholar 

  32. 32.

    Tinderholt, V. New mutants report. Drosophila Inform. Serv. 34, 53–54 (1960).

    Google Scholar 

  33. 33.

    Ashburner, M., Golic, K. G. & Hawley, R. S. Drosophila: A Laboratory Handbook (Cold Spring Harbor Laboratory Press, 2005).

  34. 34.

    Korbel, J. O. et al. Paired-end mapping reveals extensive structural variation in the human genome. Science 318, 420–426 (2007).

    CAS  Article  Google Scholar 

  35. 35.

    Weischenfeldt, J., Symmons, O., Spitz, F. & Korbel, J. O. Phenotypic impact of genomic structural variation: insights from and for human disease. Nat. Rev. Genet. 14, 125–138 (2013).

    CAS  Article  Google Scholar 

  36. 36.

    Mackay, T. F. C. et al. The Drosophila melanogaster genetic reference panel. Nature 482, 173–178 (2012).

    CAS  Article  Google Scholar 

  37. 37.

    Zichner, T. et al. Impact of genomic structural variation in Drosophila melanogaster based on population-scale sequencing. Genome Res. 23, 568–579 (2013).

    CAS  Article  Google Scholar 

  38. 38.

    Lindsley, D. L. & Zimm, G. G. The Genome of Drosophila Melanogaster (Academic Press, 1992).

  39. 39.

    Sudmant, P. H. et al. An integrated map of structural variation in 2,504 human genomes. Nature 526, 75–81 (2015).

    CAS  Article  Google Scholar 

  40. 40.

    Miller, D. E. et al. The molecular and genetic characterization of second chromosome balancers in Drosophila melanogaster. G3 (Bethesda) 8, 1161–1171 (2018).

    CAS  Article  Google Scholar 

  41. 41.

    Miller, D. E., Cook, K. R., Arvanitakis, A. V. & Hawley, R. S. Third chromosome balancer inversions disrupt protein-coding genes and influence distal recombination events in Drosophila melanogaster. G3 (Bethesda) 6, 1959–1967 (2016).

    CAS  Article  Google Scholar 

  42. 42.

    Huang, W. et al. Natural variation in genome architecture among 205 Drosophila melanogaster genetic reference panel lines. Genome Res. 24, 1193–1208 (2014).

    CAS  Article  Google Scholar 

  43. 43.

    Thomas, S. et al. Dynamic reprogramming of chromatin accessibility during Drosophila embryo development. Genome Biol. 12, R43 (2011).

    CAS  Article  Google Scholar 

  44. 44.

    Ramírez, F. et al. High-resolution TADs reveal DNA sequences underlying genome organization in flies. Nat. Commun. 9, 189 (2018).

    Article  Google Scholar 

  45. 45.

    Ogiyama, Y., Schuettengruber, B., Papadopoulos, G. L., Chang, J.-M. & Cavalli, G. Polycomb-dependent chromatin looping contributes to gene silencing during Drosophila development. Mol. Cell 71, 73–88.e5 (2018).

    CAS  Article  Google Scholar 

  46. 46.

    Spielmann, M., Lupiáñez, D. G. & Mundlos, S. Structural variation in the 3D genome. Nat. Rev. Genet. 19, 453–467 (2018).

    CAS  Article  Google Scholar 

  47. 47.

    Mardin, B. R. et al. A cell‐based model system links chromothripsis with hyperploidy. Mol. Syst. Biol. 11, 828 (2015).

    Article  Google Scholar 

  48. 48.

    Li, H. Aligning sequence reads, clone sequences and assembly contigs with BWA-MEM. Preprint at https://arxiv.org/abs/1303.3997 (2013).

  49. 49.

    Garrison, E. & Marth, G. Haplotype-based variant detection from short-read sequencing. Preprint at https://arxiv.org/abs/1207.3907 (2012).

  50. 50.

    Tan, A., Abecasis, G. R. & Kang, H. M. Unified representation of genetic variants. Bioinformatics 31, 2202–2204 (2015).

    CAS  Article  Google Scholar 

  51. 51.

    Rausch, T. et al. DELLY: structural variant discovery by integrated paired-end and split-read analysis. Bioinformatics 28, i333–i339 (2012).

    CAS  Article  Google Scholar 

  52. 52.

    Untergasser, A. et al. Primer3—new capabilities and interfaces. Nucleic Acids Res. 40, e115 (2012).

    CAS  Article  Google Scholar 

  53. 53.

    Anders, S., Pyl, P. T. & Huber, W. HTSeq—a Python framework to work with high-throughput sequencing data. Bioinformatics 31, 166–169 (2015).

    CAS  Article  Google Scholar 

  54. 54.

    Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-Seq data with DESeq2. Genome Biol. 15, 550 (2014).

    Article  Google Scholar 

  55. 55.

    Strimmer, K. fdrtool: a versatile R package for estimating local and tail area-based false discovery rates. Bioinformatics 24, 1461–1462 (2008).

    CAS  Article  Google Scholar 

  56. 56.

    Bonn, S. et al. Tissue-specific analysis of chromatin state identifies temporal signatures of enhancer activity during embryonic development. Nat. Genet. 44, 148–156 (2012).

    CAS  Article  Google Scholar 

  57. 57.

    Davies, J. O. J. et al. Multiplexed analysis of chromosome conformation at vastly improved sensitivity. Nat. Methods 13, 74–80 (2016).

    CAS  Article  Google Scholar 

  58. 58.

    Zhao, H. et al. CrossMap: a versatile tool for coordinate conversion between genome assemblies. Bioinformatics 30, 1006–1007 (2014).

    Article  Google Scholar 

  59. 59.

    Cairns, J. et al. CHiCAGO: robust detection of DNA looping interactions in Capture Hi-C data. Genome Biol. 17, 127 (2016).

    Article  Google Scholar 

  60. 60.

    Loader, C. locfit: Local Regression, Likelihood and Density Estimation https://CRAN.R-project.org/package=locfit (2013).

  61. 61.

    Cusanovich, D. A. et al. The cis-regulatory dynamics of embryonic development at single-cell resolution. Nature 555, 538–542 (2018).

    CAS  Article  Google Scholar 

  62. 62.

    Zinzen, R. P., Girardot, C., Gagneur, J., Braun, M. & Furlong, E. E. M. Combinatorial binding predicts spatio-temporal cis-regulatory activity. Nature 462, 65–70 (2009).

    CAS  Article  Google Scholar 

Download references

Acknowledgements

We thank all members of the Furlong Laboratory for discussions and comments on the manuscript. We thank M. Davis, H. Gustafson, D. Garfield, T. Rausch and S. Waszak for useful discussions and suggestions at the various stages of the project. This work was technically supported by the EMBL Genomics Core Facility, with specific thanks to R. Hercog for WGS library preparation. This work was financially supported by an FRM grant (AJE20161236686) to Y.G.-H., the EMBL International PhD Programme to S.M., an EU Horizon 2020 Marie Skłodowska-Curie grant (708111) to A.J., an ERC starting grant (336045) to J.O.K. and ERC advanced grant DeCRyPT (787611) to E.E.M.F.

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Contributions

Y.G.-H. and E.E.M.F. designed the study. Y.G.-H., A.J., S.M., J.O.K. and E.E.M.F. analyzed the results. Y.G.-H., A.J., S.M. and E.E.M.F. wrote the manuscript. Y.G.-H. performed all of the experiments, except the mate-pair library experiment, which was performed by R.R.V. S.M. performed the SNV and structural variant calling and RNA-Seq analysis. A.J. performed the Hi-C and Capture-C data analysis. All authors discussed the results and commented on the manuscript.

Corresponding authors

Correspondence to Yad Ghavi-Helm or Jan O. Korbel or Eileen E. M. Furlong.

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Integrated supplementary information

Supplementary Figure 1 Crossing scheme to generate heterozygote embryos.

a, Males from a double balancer line (If/CyO; Sb/TM3) were crossed to females from an isogenic wild-type “virginizer” line (+/+; +/+; see Methods). Resulting heterozygous males from the F1 generation (+/CyO; +/TM3) were back-crossed to females of the parental line. Embryos from the resulting N1pat generation (pool of 4 genotypes) were collected to perform allele-specific RNA-seq, Hi-C and Capture-C experiments. The reciprocal N1mat generation, used for RNA-seq, was obtained by back-crossing heterozygous females from the F1 generation to the male parental line. b, Table listing all datasets generated in the study, including genotype and developmental stage.

Supplementary Figure 2 Example and characteristics of Structural Variants in the balancer chromosomes.

a, A 258 kb balancer-specific duplication in chr2L. Top to bottom: wild-type and balancer Hi-C contact maps, log2 fold change before normalization (balancer/wild-type, red/blue), gene models, and differential gene expression (balancer/wild-type, log2 fold change). Differentially expressed genes (DE-genes) in orange, non-differentially expressed (non-DE) genes in blue and non-tested genes (lowly expressed or lacking SNVs) in grey. Hi-C contacts (~two-fold increase in balancer) confirm the duplication, inserted in an inverted orientation shown in (b). Expressed genes have a ~2-fold increase in allelic expression, as expected. Dotted lines indicate TAD boundaries. b, Model of the duplication and associated changes in TAD structure. Arrows mark the duplicated fragment, gradient indicates newly created contacts. c, Number of DNase hypersensitive (DHS) peaks deleted by ≥ 5% of their length in the wild-type (top) and balancer (bottom), indicated by orange vertical line. Deletions generally overlap a DHS less frequently than expected from randomly shuffling deleted regions in the genome (blue, green bars, respectively), as anticipated due to purifying selection. d-e, A 17.5 kb balancer-specific deletion at the breakpoint of the inversion chr3R:20.32 Mb: Wild-type (d) and balancer (e) Hi-C contact maps, deleted region in balancer highlighted in grey (d). DHS, gene models, and differential gene expression (balancer/wild-type, log2 fold change) are shown underneath. DE-genes in orange, non-DE genes in blue and non-tested genes (lowly expressed or lacking SNVs) in grey. Dashed purple lines indicate the location of the inversion boundary.

Supplementary Figure 3 Changes in gene expression between balancer and wild-type haplotypes.

a, Change in gene expression (balancer/wild-type, log2 fold change) in F1 heterozygous and single balancer (CyO only or TM3 only) adult fly heads (mixed sexes). Genes with significant (two-sided Wald test, 5% FDR) effect of genotype on differential expression (DE-genes) highlighted in blue, Pearson correlation coefficient (r) indicated. b, Same as (a), but comparing F1 heterozygous adult female heads to mixed sex heads as a proxy for expected biological noise. c, Change in allele-specific gene expression (log2 fold change) for testable genes with exons overlapping an allele-specific deletion or duplication. Fully duplicated genes are shown separately in Fig. 2b. Points, individual genes (180 genes partially deleted in balancer, 116 partially deleted in wild-type, 13 partially duplicated in balancer, 14 partially duplicated in wild-type); center line, median; box limits, upper and lower quartiles; whiskers, 1.5x interquartile range; dashed lines, two-fold increase or decrease.

Supplementary Figure 4 Extensive genome rearrangements within TADs have limited impact on gene expression.

a, Insulation score profiles, calculated at 21,800 genomic bins, are highly correlated between wild-type and balancer haplotypes when comparing replicates (R1 and R2) for each allele, even when considering regions affected by nested inversion breakpoints (red). Pearson correlation coefficient (r) indicated. Average r between biological replicates (same haplotype) = 0.912, average r between haplotypes (i.e. between the wild-type and balancer) = 0.857. b-c, Zoomed-in versions of Fig. 3b, c. d, Location (promoter) and change in expression (balancer/wild-type, log2 fold change) of differentially expressed genes (DE-genes, orange, 23 genes), non-differentially expressed (non-DE, blue, 138 genes), lowly expressed (light-blue, 161), not testable due to lack of SNVs (grey, 121) genes within the 16 disrupted TADs. TADs (outlined in grey) are centered on the location of the inversion breakpoint, indicated by the vertical dashed line (purple). In the top six, the closest gene to the breakpoint is differentially expressed (orange), while the 10 TADs below the dashed line have one or more non-DE genes (blue, light blue) closer to the breakpoint. e, Expression levels of DE-genes and non-DE genes within disrupted TADs; FPKM, Fragments Per Kilobase of transcript per Million mapped reads.

Supplementary Figure 5 Characteristics of differential Hi-C and Capture-C contacts.

a-b, Top: positional distribution of differential Hi-C (a) or Capture-C (b) contacts originating from Transcription Start Sites (TSSs) of differentially expressed genes (DE-genes) with >1.5 fold change in expression (orange) and non-differentially expressed (non-DE, grey) genes. Genes are aligned by their TSS and transcribed to the right. 95% confidence bands (shaded ribbons) estimated using bootstrapping. Bottom right: heatmap of differential contacts (balancer/wild-type, log2 fold change) for 200 randomly sampled DE-genes (Hi-C) (a) or for all captured DE-genes (Capture C) (b). Bottom left: expression change (log2 fold change) for genes shown in the heatmap. c, Fraction of Hi-C (left) and Capture-C (right) differential contacts originating from different classes and having their other end overlapping a promoter of a differentially expressed or non-differentially expressed gene, a distal enhancer or a distal DHS. The observed fraction (purple) is compared to the distribution obtained from random shuffling (1,000 times) of differential contacts (grey). Center line, median; box limits, upper and lower quartiles; whiskers, 1.5x interquartile range. p-values (two-sided permutation test) of significant differences (p<0.05) are indicated. Numbers in parentheses indicate: number of differential contacts in the given class, and (if applicable) number of unique genes, DE-genes or non-DE genes on the viewpoint side. d, Correlation of change in expression (log2 fold change) between pairs of DE-genes linked by a differential contact. Concordant gene expression (14 cases) in green, discordant (4 cases) in purple. Pearson correlation coefficient (r) indicated. e, Strength of all differential Capture-C contacts (balancer/wild-type, log2 fold change) established from each Capture-C probe region, ordered (left to right) based on the change in expression (log2 fold change) of the most DE gene assigned to the probe. Note that strongly differentially expressed genes (to the left and right of the purple arrows) do not have more, or stronger, differential Capture-C contacts.

Supplementary Figure 6 Nested inversions impact three neighboring loops.

a, Hi-C contact map (2 kb resolution, all Hi-C reads) showing long-range loops between Wbp2 and Nufip (top left), Wbp2 and kug (bottom left) and Nufip and kug (bottom right), indicated by asterisks. The locations of DNase hypersensitive sites (DHS), gene models, and differential gene expression (balancer/wild-type, log2 fold change) are shown. Differentially expressed genes (DE-genes) in orange, non-differentially expressed genes (non-DE) in blue and non-tested genes (lowly expressed or lacking SNVs) in grey. b-d, Zoomed-in wild-type (top) and balancer (bottom) Hi-C contact maps at 5 kb (left) and 20 kb (right) resolution showing the presence or absence of a long-range loop between Wbp2 and Nufip (b), Wbp2 and kug (c), Nufip and kug (d). e, Zoomed-out Hi-C contact map showing the presence or absence of long-range loops between these three loci in wild-type (bottom left) and balancer (top right) haplotypes. f, Schematic of the location and distance of Wbp2, Nufip and kug on chromosome 3 on wild-type (top) and balancer (bottom) haplotypes with respect to inversion breakpoints. Wbp2/Nufip are separated by similar distances in the wild-type (5.89 Mb) and balancer (5.34 Mb) chromosomes, and still form a loop despite intervening rearrangements (b). Wbp2/kug (c) and Nufip/kug (d) are separated by large distances in the balancer chromosome, leading to a severe diminishment of the looping interaction.

Supplementary information

Supplementary Information

Supplementary Figs. 1–6

Reporting Summary

Supplementary Table 1

PCR validation of randomly selected medium and large deletions

Supplementary Table 2

Exact balancer breakpoint coordinates

Supplementary Table 3

Hi-C read statistics

Supplementary Table 4

RNA-Seq library overview

Supplementary Table 5

Capture-C read statistics

Supplementary Table 6

Capture-C viewpoints

Supplementary Table 7

Differential Hi-C interactions between promoters of differentially expressed genes

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Ghavi-Helm, Y., Jankowski, A., Meiers, S. et al. Highly rearranged chromosomes reveal uncoupling between genome topology and gene expression. Nat Genet 51, 1272–1282 (2019). https://doi.org/10.1038/s41588-019-0462-3

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