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Genome-wide mapping and analysis of chromosome architecture

Key Points

  • Looping of chromatin fibres is an important mechanism for transcription regulation in animals. The past decade has witnessed an explosion of chromosome conformation capture (3C) technologies aimed at mapping such local or genome-wide chromatin architecture.

  • Key recent methodological advancements for mapping chromatin conformation include improved methods for chromatin fragmentation, proximity ligation, single-cell analysis and targeted 3C. These improvements have propelled the field forward with optimized protocols that enhance the efficiency, scale and resolution of chromatin contact maps.

  • Improved protocols and advances in ultra-high-throughput DNA-sequencing technology have facilitated the rapid accumulation of 3C data sets. The need to extract meaningful insight into hierarchical genome architecture has necessitated the development of novel computational algorithms and bioinformatics pipelines.

  • Accounting for bias in Hi-C and Capture-HiC data is a critical first step towards appropriately analysing their data sets and reaching grounded conclusions. Current methods to account for bias use either explicit or implicit assumption models; however, it is recommended that researchers analyse their data using both approaches to ensure the biological relevance of their findings.

  • Analyses of chromatin contact maps at various resolutions have revealed principles of hierarchical genome architecture, spanning from chromosome territories, compartments and topologically associating domains to contact domains, loops and other important contacts mediated by cis-regulatory elements. Numerous approaches exist for defining each of these features, and the selection of each method should be guided by a full understanding of the statistical model used by each approach.

  • An exhaustive comparison of mapping technologies and analysis methods is sorely needed. To facilitate the evaluation of the 'accuracy' of each method, future efforts should focus on the development of new interaction data standards that consist of loci, the interaction tendencies of which have been rigorously characterized using genetic, biochemical and microscopy approaches.

Abstract

Chromosomes of eukaryotes adopt highly dynamic and complex hierarchical structures in the nucleus. The three-dimensional (3D) organization of chromosomes profoundly affects DNA replication, transcription and the repair of DNA damage. Thus, a thorough understanding of nuclear architecture is fundamental to the study of nuclear processes in eukaryotic cells. Recent years have seen rapid proliferation of technologies to investigate genome organization and function. Here, we review experimental and computational methodologies for 3D genome analysis, with special focus on recent advances in high-throughput chromatin conformation capture (3C) techniques and data analysis.

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Figure 1: Experimental modifications to genome-wide chromosome conformation capture (3C)-based technologies (C-technologies).
Figure 2: Comparison of computational methods to account for bias in Hi-C data.

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References

  1. ENCODE Project Consortium. An integrated encyclopedia of DNA elements in the human genome. Nature 489, 57–74 (2012).

  2. Roadmap Epigenomics Consortium et al. Integrative analysis of 111 reference human epigenomes. Nature 518, 317–330 (2015).

  3. Bickmore, W. A. & van Steensel, B. Genome architecture: domain organization of interphase chromosomes. Cell 152, 1270–1284 (2013).

    Article  CAS  PubMed  Google Scholar 

  4. de Laat, W. & Duboule, D. Topology of mammalian developmental enhancers and their regulatory landscapes. Nature 502, 499–506 (2013).

    Article  CAS  PubMed  Google Scholar 

  5. de Wit, E. & de Laat, W. A decade of 3C technologies: insights into nuclear organization. Genes Dev. 26, 11–24 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Dixon, J. R. et al. Topological domains in mammalian genomes identified by analysis of chromatin interactions. Nature 485, 376–380 (2012). The original study to describe TADs from Hi-C analysis, using novel computation approaches. It discovered that TADs are conserved between cell types and species, and demarcated by CCCTC-binding factor (CTCF) binding at TAD boundaries.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Gorkin, D. U., Leung, D. & Ren, B. The 3D genome in transcriptional regulation and pluripotency. Cell Stem Cell 14, 762–775 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Levine, M., Cattoglio, C. & Tjian, R. Looping back to leap forward: transcription enters a new era. Cell 157, 13–25 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Nora, E. P., Dekker, J. & Heard, E. Segmental folding of chromosomes: a basis for structural and regulatory chromosomal neighborhoods? Bioessays 35, 818–828 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Phillips-Cremins, J. E. et al. Architectural protein subclasses shape 3D organization of genomes during lineage commitment. Cell 153, 1281–1295 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Deng, W. et al. Reactivation of developmentally silenced globin genes by forced chromatin looping. Cell 158, 849–860 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Deng, W. et al. Controlling long-range genomic interactions at a native locus by targeted tethering of a looping factor. Cell 149, 1233–1244 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Kim, A. & Dean, A. Chromatin loop formation in the β-globin locus and its role in globin gene transcription. Mol. Cells 34, 1–5 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Krivega, I. & Dean, A. Enhancer and promoter interactions-long distance calls. Curr. Opin. Genet. Dev. 22, 79–85 (2012).

    Article  CAS  PubMed  Google Scholar 

  16. Plank, J. L. & Dean, A. Enhancer function: mechanistic and genome-wide insights come together. Mol. Cell 55, 5–14 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Dowen, J. M. et al. Control of cell identity genes occurs in insulated neighborhoods in mammalian chromosomes. Cell 159, 374–387 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Heidari, N. et al. Genome-wide map of regulatory interactions in the human genome. Genome Res. 24, 1905–1917 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Jin, F. et al. A high-resolution map of the three-dimensional chromatin interactome in human cells. Nature 503, 290–294 (2013). The first paper to report Hi-C interaction maps at the resolution of individual restriction fragments in mammals. This study also introduced the global background model.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Li, G. et al. Extensive promoter-centered chromatin interactions provide a topological basis for transcription regulation. Cell 148, 84–98 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Sanyal, A., Lajoie, B. R., Jain, G. & Dekker, J. The long-range interaction landscape of gene promoters. Nature 489, 109–113 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Lieberman-Aiden, E. et al. Comprehensive mapping of long-range interactions reveals folding principles of the human genome. Science 326, 289–293 (2009). The original study describing Hi-C technology. This study was also the first to describe the genome compartments A and B, which respectively mark colocalizing active and repressed regions of the genome.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Fullwood, M. J. et al. An oestrogen-receptor-α-bound human chromatin interactome. Nature 462, 58–64 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Kalhor, R., Tjong, H., Jayathilaka, N., Alber, F. & Chen, L. Genome architectures revealed by tethered chromosome conformation capture and population-based modeling. Nat. Biotechnol. 30, 90–98 (2012).

    Article  CAS  Google Scholar 

  25. Hughes, J. R. et al. Analysis of hundreds of cis-regulatory landscapes at high resolution in a single, high-throughput experiment. Nat. Genet. 46, 205–212 (2014).

    Article  CAS  PubMed  Google Scholar 

  26. Kolovos, P. et al. Targeted chromatin capture (T2C): a novel high resolution high throughput method to detect genomic interactions and regulatory elements. Epigenetics Chromatin 7, 10 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Rao, S. S. et al. A 3D map of the human genome at kilobase resolution reveals principles of chromatin looping. Cell 159, 1665–1680 (2014). The highest-resolution Hi-C analysis to date, at 1–5kb resolution in 9 human and mouse cell types. This study reports that the genome is organized globally into 6 sub-compartments, within which the genome is organized into 10,000 chromatin loops, many of which are conserved across species and cell types, and are anchored by CTCF binding in convergent orientation.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Selvaraj, S., R. Dixon, J., Bansal, V. & Ren, B. Whole-genome haplotype reconstruction using proximity-ligation and shotgun sequencing. Nat. Biotechnol. 31, 1111–1118 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Selvaraj, S., Schmitt, A. D., Dixon, J. R. & Ren, B. Complete haplotype phasing of the MHC and KIR loci with targeted HaploSeq. BMC Genomics 16, 900 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. de Vree, P. J. et al. Targeted sequencing by proximity ligation for comprehensive variant detection and local haplotyping. Nat. Biotechnol. 32, 1019–1025 (2014).

    Article  CAS  PubMed  Google Scholar 

  31. Burton, J. N. et al. Chromosome-scale scaffolding of de novo genome assemblies based on chromatin interactions. Nat. Biotechnol. 31, 1119–1125 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Kaplan, N. & Dekker, J. High-throughput genome scaffolding from in vivo DNA interaction frequency. Nat. Biotechnol. 31, 1143–1147 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Marie-Nelly, H. et al. High-quality genome (re)assembly using chromosomal contact data. Nat. Commun. 5, 5695 (2014).

    Article  CAS  PubMed  Google Scholar 

  34. Beitel, C. W. et al. Strain- and plasmid-level deconvolution of a synthetic metagenome by sequencing proximity ligation products. PeerJ 2, e415 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Burton, J. N., Liachko, I., Dunham, M. J. & Shendure, J. Species-level deconvolution of metagenome assemblies with Hi-C-based contact probability maps. G3 (Bethesda) 4, 1339–1346 (2014).

    Article  CAS  Google Scholar 

  36. Marbouty, M. et al. Metagenomic chromosome conformation capture (meta3C) unveils the diversity of chromosome organization in microorganisms. eLife 3, e03318 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  37. Duan, Z. et al. A three-dimensional model of the yeast genome. Nature 465, 363–367 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Nagano, T. et al. Single-cell Hi-C reveals cell-to-cell variability in chromosome structure. Nature 502, 59–64 (2013).

    Article  CAS  PubMed  Google Scholar 

  39. Snyder, M. W., Adey, A., Kitzman, J. O. & Shendure, J. Haplotype-resolved genome sequencing: experimental methods and applications. Nat. Rev. Genet. 16, 344–358 (2015).

    Article  CAS  PubMed  Google Scholar 

  40. Flot, J. F., Marie-Nelly, H. & Koszul, R. Contact genomics: scaffolding and phasing (meta)genomes using chromosome 3D physical signatures. FEBS Lett. 589, 2966–2974 (2015).

    Article  CAS  PubMed  Google Scholar 

  41. Imakaev, M. V., Fudenberg, G. & Mirny, L. A. Modeling chromosomes: beyond pretty pictures. FEBS Lett. 589, 3031–3036 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Serra, F. et al. Restraint-based three-dimensional modeling of genomes and genomic domains. FEBS Lett. 589, 2987–2995 (2015).

    Article  CAS  PubMed  Google Scholar 

  43. Dekker, J., Rippe, K., Dekker, M. & Kleckner, N. Capturing chromosome conformation. Science 295, 1306–1311 (2002). The original study describing 3C technology.

    Article  CAS  PubMed  Google Scholar 

  44. Cullen, K. E., Kladde, M. P. & Seyfred, M. A. Interaction between transcription regulatory regions of prolactin chromatin. Science 261, 203–206 (1993).

    Article  CAS  PubMed  Google Scholar 

  45. Zhao, Z. et al. Circular chromosome conformation capture (4C) uncovers extensive networks of epigenetically regulated intra- and interchromosomal interactions. Nat. Genet. 38, 1341–1347 (2006). A study reporting chromosome conformation capture-on-chip (4C), which explores the genome-wide interactions of individual loci at high resolution.

    Article  CAS  PubMed  Google Scholar 

  46. van de Werken, H. J. et al. Robust 4C-seq data analysis to screen for regulatory DNA interactions. Nat. Methods 9, 969–972 (2012).

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  48. Deng, X. et al. Bipartite structure of the inactive mouse X chromosome. Genome Biol. 16, 152 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Ma, W. et al. Fine-scale chromatin interaction maps reveal the cis-regulatory landscape of human lincRNA genes. Nat. Methods 12, 71–78 (2015). The first study to report the use of DNase Hi-C and DNase Capture-HiC, and the first application of Capture-HiC to specifically enrich for gene promoters.

    Article  CAS  PubMed  Google Scholar 

  50. Hsieh, T. H. et al. Mapping nucleosome resolution chromosome folding in yeast by micro-C. Cell 162, 108–119 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Simonis, M. et al. Nuclear organization of active and inactive chromatin domains uncovered by chromosome conformation capture-on-chip (4C). Nat. Genet. 38, 1348–1354 (2006). Another study reporting chromosome conformation capture-on-chip (4C), which explores the genome- wide interactions of individual loci at high resolution.

    Article  CAS  PubMed  Google Scholar 

  52. Dostie, J. et al. Chromosome conformation capture carbon copy (5C): a massively parallel solution for mapping interactions between genomic elements. Genome Res. 16, 1299–1309 (2006). The original study describing 5C, which explores the interaction profiles of several contiguous loci with each other at high resolution.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Martin, P. et al. Capture Hi-C reveals novel candidate genes and complex long-range interactions with related autoimmune risk loci. Nat. Commun. 6, 10069 (2015).

    Article  CAS  PubMed  Google Scholar 

  54. Mifsud, B. et al. Mapping long-range promoter contacts in human cells with high-resolution capture Hi-C. Nat. Genet. 47, 598–606 (2015).

    Article  CAS  PubMed  Google Scholar 

  55. Sahlen, P. et al. Genome-wide mapping of promoter-anchored interactions with close to single-enhancer resolution. Genome Biol. 16, 156 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Schoenfelder, S. et al. The pluripotent regulatory circuitry connecting promoters to their long-range interacting elements. Genome Res. 25, 582–597 (2015). The first application of Capture-HiC to capture all promoters in the genome, demonstrating the feasibility and quality of obtaining high-resolution promoter interaction profiles for >20,000 loci in a single assay.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Schoenfelder, S. et al. Polycomb repressive complex PRC1 spatially constrains the mouse embryonic stem cell genome. Nat. Genet. 47, 1179–1186 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Dryden, N. H. et al. Unbiased analysis of potential targets of breast cancer susceptibility loci by Capture Hi-C. Genome Res. 24, 1854–1868 (2014). The original study describing Capture-HiC technology and its use to interrogate the interaction landscapes of several disease-associated risk loci.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  59. Jager, R. et al. Capture Hi-C identifies the chromatin interactome of colorectal cancer risk loci. Nat. Commun. 6, 6178 (2015).

    Article  CAS  PubMed  Google Scholar 

  60. Sanborn, A. L. et al. Chromatin extrusion explains key features of loop and domain formation in wild-type and engineered genomes. Proc. Natl Acad. Sci. USA 112, E6456–E6465 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  61. Schmitt, M. W. et al. Sequencing small genomic targets with high efficiency and extreme accuracy. Nat. Methods 12, 423–425 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  62. Dixon, J. R. et al. Chromatin architecture reorganization during stem cell differentiation. Nature 518, 331–336 (2015). A high-resolution Hi-C analysis in human embryonic stem cells and four derived cell types, revealing a relationship between dynamic chromatin organization and gene expression, as well as haplotype-resolved dynamics in chromatin organization patterns.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  63. Fraser, J. et al. Hierarchical folding and reorganization of chromosomes are linked to transcriptional changes in cellular differentiation. Mol. Syst. Biol. 11, 852 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  64. Leung, D. et al. Integrative analysis of haplotype-resolved epigenomes across human tissues. Nature 518, 350–354 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  65. Nagano, T. et al. Comparison of Hi-C results using in-solution versus in-nucleus ligation. Genome Biol. 16, 175 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  66. Seitan, V. C. et al. Cohesin-based chromatin interactions enable regulated gene expression within preexisting architectural compartments. Genome Res. 23, 2066–2077 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  67. Sofueva, S. et al. Cohesin-mediated interactions organize chromosomal domain architecture. EMBO J. 32, 3119–3129 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  68. Zuin, J. et al. Cohesin and CTCF differentially affect chromatin architecture and gene expression in human cells. Proc. Natl Acad. Sci. USA 111, 996–1001 (2014).

    Article  CAS  PubMed  Google Scholar 

  69. Belton, J. M. et al. Hi-C: a comprehensive technique to capture the conformation of genomes. Methods 58, 268–276 (2012).

    Article  CAS  PubMed  Google Scholar 

  70. van Berkum, N. L. et al. Hi-C: a method to study the three-dimensional architecture of genomes. J. Vis. Exp. 39, 1869 (2010).

    Google Scholar 

  71. Comet, I., Schuettengruber, B., Sexton, T. & Cavalli, G. A chromatin insulator driving three-dimensional Polycomb response element (PRE) contacts and Polycomb association with the chromatin fiber. Proc. Natl Acad. Sci. USA 108, 2294–2299 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  72. van de Werken, H. J. et al. 4C technology: protocols and data analysis. Methods Enzymol. 513, 89–112 (2012).

    Article  CAS  PubMed  Google Scholar 

  73. Adey, A. et al. Rapid, low-input, low-bias construction of shotgun fragment libraries by high-density in vitro transposition. Genome Biol. 11, R119 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  74. Williamson, I. et al. Anterior-posterior differences in HoxD chromatin topology in limb development. Development 139, 3157–3167 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  75. Bickmore, W. A. The spatial organization of the human genome. Annu. Rev. Genomics Hum. Genet. 14, 67–84 (2013).

    Article  CAS  PubMed  Google Scholar 

  76. Williamson, I. et al. Spatial genome organization: contrasting views from chromosome conformation capture and fluorescence in situ hybridization. Genes Dev. 28, 2778–2791 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  77. Yaffe, E. & Tanay, A. Probabilistic modeling of Hi-C contact maps eliminates systematic biases to characterize global chromosomal architecture. Nat. Genet. 43, 1059–1065 (2011).

    Article  CAS  PubMed  Google Scholar 

  78. Hu, M. et al. HiCNorm: removing biases in Hi-C data via Poisson regression. Bioinformatics 28, 3131–3133 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  79. Imakaev, M. et al. Iterative correction of Hi-C data reveals hallmarks of chromosome organization. Nat. Methods 9, 999–1003 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  80. Servant, N. et al. HiC-Pro: an optimized and flexible pipeline for Hi-C data processing. Genome Biol. 16, 259 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  81. Li, W., Gong, K., Li, Q., Alber, F. & Zhou, X. J. Hi-Corrector: a fast, scalable and memory-efficient package for normalizing large-scale Hi-C data. Bioinformatics 31, 960–962 (2015).

    Article  CAS  PubMed  Google Scholar 

  82. Knopp, P. & Sinkhorn, R. Concerning nonnegative matrices and doubly stochastic matrices. Pacif. J. Math. 21, 343–348 (1967).

    Article  Google Scholar 

  83. Knight, P. A. & Ruiz, D. A fast algorithm for matrix balancing. IMA J. Numer. Analysis 33, 1029–1047 (2012).

    Article  Google Scholar 

  84. Shavit, Y. & Lio, P. Combining a wavelet change point and the Bayes factor for analysing chromosomal interaction data. Mol. Biosyst. 10, 1576–1585 (2014).

    Article  CAS  PubMed  Google Scholar 

  85. Cairns, J. et al. CHiCAGO: robust detection of DNA looping interactions in capture Hi-C data. Genome Biol. 17, 127 (2015).

    Article  CAS  Google Scholar 

  86. Cournac, A., Marie-Nelly, H., Marbouty, M., Koszul, R. & Mozziconacci, J. Normalization of a chromosomal contact map. BMC Genomics 13, 436 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  87. Dekker, J. & Heard, E. Structural and functional diversity of topologically associating domains. FEBS Lett. 589, 2877–2884 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  88. Filippova, D., Patro, R., Duggal, G. & Kingsford, C. Identification of alternative topological domains in chromatin. Algorithms Mol. Biol. 9, 14 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  89. Levy-Leduc, C., Delattre, M., Mary-Huard, T. & Robin, S. Two-dimensional segmentation for analyzing Hi-C data. Bioinformatics 30, i386–i392 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  90. Crane, E. et al. Condensin-driven remodelling of X chromosome topology during dosage compensation. Nature 523, 240–244 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  91. Ay, F., Bailey, T. L. & Noble, W. S. Statistical confidence estimation for Hi-C data reveals regulatory chromatin contacts. Genome Res. 24, 999–1011 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  92. Mifsud, B. et al. GOTHiC, a simple probabilistic model to resolve complex biases and to identify real interactions in Hi-C data. Preprint at bioRxiv http://dx.doi.org/10.1101/023317 (2015).

  93. Xu, Z. et al. A hidden Markov random field based Bayesian method for the detection of long-range chromosomal intereactions in Hi-C data. Bioinformatics 32, 650–656 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  94. Lun, A. T. & Smyth, G. K. diffHic: a bioconductor package to detect differential genomic interactions in Hi-C data. BMC Bioinformatics 16, 258 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  95. Robinson, M. D., McCarthy, D. J. & Smyth, G. K. edgeR: A Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26, 139–140 (2010).

    Article  CAS  PubMed  Google Scholar 

  96. Nagano, T. et al. Single-cell Hi-C for genome-wide detection of chromatin interactions that occur simultaneously in a single cell. Nat. Protoc. 10, 1986–2003 (2015).

    Article  CAS  PubMed  Google Scholar 

  97. Dekker, J. The three 'C' s of chromosome conformation capture: controls, controls, controls. Nat. Methods 3, 17–21 (2006).

    Article  CAS  PubMed  Google Scholar 

  98. Hagege, H. et al. Quantitative analysis of chromosome conformation capture assays (3C-qPCR). Nat. Protoc. 2, 1722–1733 (2007).

    Article  CAS  PubMed  Google Scholar 

  99. Louwers, M., Splinter, E., van Driel, R., de Laat, W. & Stam, M. Studying physical chromatin interactions in plants using chromosome conformation capture (3C). Nat. Protoc. 4, 1216–1229 (2009).

    Article  CAS  PubMed  Google Scholar 

  100. Naumova, N., Smith, E. M., Zhan, Y. & Dekker, J. Analysis of long-range chromatin interactions using chromosome conformation capture. Methods 58, 192–203 (2012).

    Article  CAS  PubMed  Google Scholar 

  101. Ribeiro de Almeida, C. et al. The DNA-binding protein CTCF limits proximal Vκ recombination and restricts κ enhancer interactions to the immunoglobulin κ light chain locus. Immunity 35, 501–513 (2011).

    Article  CAS  PubMed  Google Scholar 

  102. Stadhouders, R. et al. Multiplexed chromosome conformation capture sequencing for rapid genome-scale high-resolution detection of long-range chromatin interactions. Nat. Protoc. 8, 509–524 (2013).

    Article  CAS  PubMed  Google Scholar 

  103. Wurtele, H. & Chartrand, P. Genome-wide scanning of HoxB1-associated loci in mouse ES cells using an open-ended chromosome conformation capture methodology. Chromosome Res. 14, 477–495 (2006).

    Article  CAS  PubMed  Google Scholar 

  104. Harismendy, O. et al. 9p21 DNA variants associated with coronary artery disease impair interferon-gamma signalling response. Nature 470, 264–268 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  105. Gondor, A., Rougier, C. & Ohlsson, R. High-resolution circular chromosome conformation capture assay. Nat. Protoc. 3, 303–313 (2008).

    Article  CAS  PubMed  Google Scholar 

  106. Splinter, E. et al. The inactive X chromosome adopts a unique three-dimensional conformation that is dependent on Xist RNA. Genes Dev. 25, 1371–1383 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  107. Gheldof, N., Leleu, M., Noordermeer, D., Rougemont, J. & Reymond, A. Detecting long-range chromatin interactions using the chromosome conformation capture sequencing (4C-seq) method. Methods Mol. Biol. 786, 211–225 (2012).

    Article  CAS  PubMed  Google Scholar 

  108. Splinter, E., de Wit, E., van de Werken, H. J., Klous, P. & de Laat, W. Determining long-range chromatin interactions for selected genomic sites using 4C-seq technology: from fixation to computation. Methods 58, 221–230 (2012).

    Article  CAS  PubMed  Google Scholar 

  109. Schoenfelder, S. et al. Preferential associations between co-regulated genes reveal a transcriptional interactome in erythroid cells. Nat. Genet. 42, 53–61 (2010).

    Article  CAS  PubMed  Google Scholar 

  110. Sexton, T. et al. Sensitive detection of chromatin coassociations using enhanced chromosome conformation capture on chip. Nat. Protoc. 7, 1335–1350 (2012).

    Article  CAS  PubMed  Google Scholar 

  111. Ling, J. Q. et al. CTCF mediates interchromosomal colocalization between Igf2/H19 and Wsb1/Nf1. Science 312, 269–272 (2006).

    Article  CAS  PubMed  Google Scholar 

  112. Ling, J. & Hoffman, A. R. Associated chromosome trap for identifying long-range DNA interactions. J. Vis. Exp. 50, 2621 (2011).

    Google Scholar 

  113. Dostie, J., Zhan, Y. & Dekker, J. Chromosome conformation capture carbon copy technology. Curr. Protoc. Mol. Biol. http://dx.doi.org/10.1002/0471142727.mb2114s80 (2007).

  114. Ferraiuolo, M. A., Sanyal, A., Naumova, N., Dekker, J. & Dostie, J. From cells to chromatin: capturing snapshots of genome organization with 5C technology. Methods 58, 255–267 (2012).

    Article  CAS  PubMed  Google Scholar 

  115. Fraser, J., Ethier, S. D., Miura, H. & Dostie, J. A. Torrent of data: mapping chromatin organization using 5C and high-throughput sequencing. Methods Enzymol. 513, 113–141 (2012).

    Article  CAS  PubMed  Google Scholar 

  116. Umbarger, M. A. Chromosome conformation capture assays in bacteria. Methods 58, 212–220 (2012).

    Article  CAS  PubMed  Google Scholar 

  117. Rodley, C. D., Bertels, F., Jones, B. & O'Sullivan, J. M. Global identification of yeast chromosome interactions using genome conformation capture. Fungal Genet. Biol. 46, 879–886 (2009).

    Article  CAS  PubMed  Google Scholar 

  118. Duan, Z. et al. A genome-wide 3C-method for characterizing the three-dimensional architectures of genomes. Methods 58, 277–288 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  119. Tanizawa, H. et al. Mapping of long-range associations throughout the fission yeast genome reveals global genome organization linked to transcriptional regulation. Nucleic Acids Res. 38, 8164–8177 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

The authors dedicate this manuscript in loving memory of Joseph Schmitt. They would like to give special thanks to members of the Ren laboratory for their suggestions. This work is supported by the Ludwig Institute for Cancer Research, La Jolla, California, USA, and grants from US National Institutes of Health (NIH; grant U54DK107977 to B.R. and M.H., and grants U54 HG006997 and R01 ES024984 to B.R.). A.D.S. is supported by NIH genetics training grant T32 GM008666.

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Correspondence to Ming Hu or Bing Ren.

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B.R. is a co-founder of Arima Genomics, Inc. A.D.S. is a consultant for Arima Genomics, Inc.

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FURTHER INFORMATION

4C protocol variant

4D Nucleome Project

https://commonfund.nih.gov/4dnucleome/index

Glossary

Hi-C

A high-throughput, genome-wide chromosome conformation capture assay using affinity purification of labelled-DNA ligation junctions to measure pairwise interaction frequencies in cell populations.

Chromosome conformation capture carbon copy

(5C). A high-throughput chromosome conformation capture assay that examines the spatial proximity of two defined sets of genomic regions, measured using a pair of DNA oligos corresponding to the sequences upstream and downstream of the ligation junction.

Target size

The cumulative length (in base pairs) targeted by capture probes in a Capture-HiC experiment.

Bin size

A measure of Hi-C data resolution. A bin is a fixed, non-overlapping genomic span to which Hi-C reads are grouped to increase the signal of chromatin interaction frequency.

Restriction enzyme fragment lengths

The total genomic length in each bin that is within 500 bp of restriction enzyme cut sites used in the Hi-C library preparation.

Mappability

The probability of a read-mapping uniquely to the effective fragment length sequence within each bin.

Poisson distribution

A probability distribution for the discrete random variable in which the variance is the same as the mean.

Negative binomial distribution

A probability distribution for the discrete random variable in which the variance is larger than the mean.

Hi-C contact matrices

Symmetric, two-dimensional matrices (M), for which each matrix entry (Mij) represents the raw or normalized contact frequency between bin i and bin j.

Bait-specific bias

An experimental bias in the Capture-HiC procedure, referring to the unequal probability of probe hybridization to the target sequence as a result of variable sequence content and hybridization properties.

Other-end-specific bias

An experimental bias in the Capture-HiC procedure, referring to the unequal probability of ligation between the bait locus and its interacting restriction fragment as a result of variable local genomic features.

Principal component analysis

(PCA). A statistical approach for multivariate data analysis. PCA converts a set of correlated variables into a set of linearly uncorrelated variables named principal components, each of which is a linear combination of the original correlated variables.

First eigenvector

The coefficients of the linear combination in the first principle component, which has the largest variance among all principal components. In Hi-C data analysis, the sign of the first eigenvector was used to determinate the A and B compartments.

Hidden Markov model

(HMM). A statistical model assuming that the observed data are determined by a set of unobserved (hidden) states with the Markov property: the future state depends on only the current state and is independent of all the previous states.

Heuristic tuning parameters

The parameters in the statistical models and computational pipelines that are not estimated from the observed data but are determined based on prior knowledge and expectation.

Global background model

The statistical model for the expected chromatin contact frequency estimated from genome-wide measurements. It is used to systematically identify significant pairwise Hi-C interactions throughout the genome. All interacting loci pairs at a given linear distance share the same global background model.

Non-parametric spline

A statistical approach to fit the observed data using a piecewise-defined polynomial function.

Benjamini–Hochberg multiple-testing correction

A statistical procedure that uses stringent statistical significance thresholds to control the false discovery rate when performing multiple comparisons.

Local background model

The statistical model for the expected chromatin contact frequency estimated from local chromatin interaction properties. Each pair of interacting loci has a unique local background model, which depends on the definition of its local neighbouring regions.

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Schmitt, A., Hu, M. & Ren, B. Genome-wide mapping and analysis of chromosome architecture. Nat Rev Mol Cell Biol 17, 743–755 (2016). https://doi.org/10.1038/nrm.2016.104

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