Characterization of noncoding regulatory DNA in the human genome

Journal name:
Nature Biotechnology
Year published:
Published online


Genetic variants associated with common diseases are usually located in noncoding parts of the human genome. Delineation of the full repertoire of functional noncoding elements, together with efficient methods for probing their biological roles, is therefore of crucial importance. Over the past decade, DNA accessibility and various epigenetic modifications have been associated with regulatory functions. Mapping these features across the genome has enabled researchers to begin to document the full complement of putative regulatory elements. High-throughput reporter assays to probe the functions of regulatory regions have also been developed but these methods separate putative regulatory elements from the chromosome so that any effects of chromatin context and long-range regulatory interactions are lost. Definitive assignment of function(s) to putative cis-regulatory elements requires perturbation of these elements. Genome-editing technologies are now transforming our ability to perturb regulatory elements across entire genomes. Interpretation of high-throughput genetic screens that incorporate genome editors might enable the construction of an unbiased map of functional noncoding elements in the human genome.

At a glance


  1. Genome-wide identification of candidate regulatory regions.
    Figure 1: Genome-wide identification of candidate regulatory regions.

    (a) The conditions in which each gene is expressed are determined by a complex interplay between cis-regulatory DNA elements embedded near the gene's transcription start site (TSS) (the gene's promoter region, typically taken as 1,000 bp upstream to 200 bp downstream of the TSS) and distal enhancer elements located far (along the linear genomic DNA) from the gene's TSS. These DNA elements are bound by TFs that modulated the efficiency by which RNA polymerase is recruited to the gene's TSS to initiate transcription. Image adapted with permission from Figure 1, ref. 21, Springer Nature. (b) Distinct chromatin marks correlate with different regulatory states. Thus, epigenomic profiling of chromatin accessibility, histone modifications and TF binding in large panels of cell lines and tissues predicts comprehensive maps of putative regulatory elements across the genome and indicates the conditions under which each element is active. Reprinted from Figure 2, ref. 157, Mol. Cell., 55, Plank, J.L. & Dean, A., Enhancer function: mechanistic and genome-wide insights come together. 514 (2014), with permission from Elsevier. (c) Bidirectional production of eRNAs emerges as an effective mark of active enhancers. Thus, expression profiling of eRNAs is used on top of the epigenomic layers to improve the identification of enhancers and delineate the conditions in which they are activated. This cartoon shows tracks for epigenetic hallmarks of enhancers (DHS, histone marks and TF binding sites (TF BS) in addition to bidirectional production of eRNAs (as detected by GRO-seq)).

  2. High-throughput measurements of enhancer activity using exogenous assays.
    Figure 2: High-throughput measurements of enhancer activity using exogenous assays.

    (a) MPRA, MPFD and CRE-seq assays use plasmid constructs in which the tested DNA segments are inserted upstream of a minimal promoter and a reporter gene while a barcode is inserted into the 3′ UTR of the gene. On the other hand, STARR-seq exploits the fact that enhancers function independently of their position relative to their target promoter, and inserts the tested enhancers themselves in the 3′ UTR of the reporter gene, so the enhancer sequences are included in the RNA transcripts, and thus obviate the need for barcoding the library vectors. (b) Cells are transfected by the enhancer library, and the region that corresponds to the barcode sequences (in MPREA, MFDA and CRE-seq) or the enhancer sequences (in STARR-seq) on the transcribed RNAs is extracted and deep-sequenced. For normalization, these regions are also sequenced from the plasmid DNA, to control for differences in transfection efficiencies. Normalized counts provide estimates of the relative activity of tested enhancers in the assayed cells/conditions.

  3. Elucidation of functional variants in regulatory elements.
    Figure 3: Elucidation of functional variants in regulatory elements.

    (a) Neighbor SNP alleles in the human genome are frequently inherited together (that is, they form a haplotype). GWAS usually genotype only a single or very few SNPs from each haplotype (genotypes of other SNPs in the haplotype can be computationally imputed with accuracy that is increased with the strength of linkage disequilibrium between the genotyped and imputed variants). Linkage disequilibrium between pairs of SNPs is typically measured on a 0–1 scale and presented in linkage disequilibrium (LD) plots. Thus, variants that were associated by GWAS with increased disease susceptibility (here, SNP1) are only tag SNPs. Any SNP with strong linkage disequilibrium with a tag SNP is similarly likely to be a causal variant (SNP 6, which is in perfect linkage disequilibrium with the tag SNP, is the causal variant and it acts by disrupting a regulatory element). Image adapted with permission from Figure 1, ref. 66, Cold Spring Harbor Laboratory Press. (b) Epigenetic QTL analyses examine associations between the SNP genotype and the signal of the epigenetic mark in the region in which the variant is located. Such association suggests a functional effect for the examined variant (or for a variant that is in strong linkage disequilibrium with it) on local chromatin state. Adapted from graphical abstract, ref. 76, Cell, 162, Grubert, F. et al., Genetic control of chromatin states in humans involves local and distal chromosomal interactions. 10511065 (2015), with permission from Elsevier. (c) Left: many GWAS SNPs are bQTLs, indicating that modulation of TF binding affinity is a central mode of action of genetic variation that affect human traits. The C allele of a SNP associated with human disease disrupts a TF binding motif and hence results in lower binding of the TF to this regulatory element. Right: allelic-imbalanced binding observed in individuals who are heterozygous for the SNP provide additional support for a functional effect for the examined SNP. The analyzed TF binds with much higher affinity to the site with the G allele than the one with the C allele, and thus, in ChIP-seq performed in individuals who are heterozygous for this SNP, many more reads originate from the binding site that carry the G allele. Image adapted with permission from Figure 1, ref. 158, Cold Spring Harbor Laboratory Press.

  4. Inference of enhancer-promoter links.
    Figure 4: Inference of enhancer-promoter links.

    (a) Enhancer-promoter (E-P) interactions are predicted based on their correlated activation pattern measured over a large panel of cells and tissues. Activation pattern could be measured by epigenetic marks, DHS or transcriptional activity (e.g., mRNA and eRNA levels). (b) Top: eQTL analysis detects associations between SNP genotypes and expression level of target genes. In this example, individuals who are homozygous for the reference allele (GG) show significantly lower expression of the target gene than individuals who are homozygous for the alternative allele (AA). Heterozygous individuals show an intermediate expression level. If either the eQTL SNP itself or any other SNP that is in strong linkage disequilibrium with it is located within a regulatory element, then a putative functional link between that enhancer and the promoter of the associated gene is predicted. Bottom: allele-specific expression analysis requires the presence of a heterozygous SNP within the target RNA (in the figure, the SNP with the T/C alleles), and tests for imbalanced expression from the two copies (maternal and paternal copies) of the gene. Imbalanced expression of the two copies implies that the individual is also heterozygous for another SNP that modulates the activity of a cis-regulatory element that controls the expression of the target gene. The A allele of the SNP located within the enhancer increases the enhancer activity and thus causes elevated expression of the copy of the gene encoded on the same chromosome (the copy of the gene that carries the C allele).

  5. Mapping physical interactions between putative enhancers and promoters.
    Figure 5: Mapping physical interactions between putative enhancers and promoters.

    (a) ChIA-PET combines TF ChIP with chromosome conformation capture that is based on nuclear proximity ligation. This procedure enables the detection of genomic segments that, although they might be located far away from each other on the linear DNA sequence, are brought into close spatial proximity by long-range chromatin looping. Such long-range chromatin interactions are inferred from inter-ligation PET products. Image adapted from Figure 1, ref. 159, under a Creative Commons license ( (b) ChIA-PET detects, with very high resolution (<500 bp), physical interactions between different genomic loci that involve the immunoprecipitated protein factor. (c) Hi-C experiments detected the organization of the genome into TADs and demonstrated that E-E and E-P interactions are largely restricted by TAD boundaries.

  6. Functional screens for DNA regulatory elements using genome-editing tools.
    Figure 6: Functional screens for DNA regulatory elements using genome-editing tools.

    Genome-editing screens that were so far applied to study the noncoding genome were either gene-centric or TF-centric. Gene-centric screens apply saturation mutagenesis analysis that systematically tiles and targets the genome in the surrounding of selected target genes. TF-centric screens use sgRNA libraries that systematically target TF binding sites within putative enhancer regions. The readout of these screens is based on either sorting the pooled cell population using GFP or endogenous gene expression or on acquisition of proliferation advantage/disadvantage by specific sgRNA clones. DNA segments that include the incorporated sgRNAs are amplified and deep-sequenced. The prevalence of each sgRNA is calculated in the two pools and enrichment scores are calculated (after normalization of counts). Functional elements are detected by identification of sgRNA with significant enrichment (or depletion) scores. From Figure 1, ref. 130, Sanjana, N.E. et al. High-resolution interrogation of functional elements in the nongenome. Science 353, 15451549 (2016). Adapted with permission from AAAS.


  1. ENCODE Project Consortium. An integrated encyclopedia of DNA elements in the human genome. Nature 489, 5774 (2012).
  2. Cech, T.R. & Steitz, J.A. The noncoding RNA revolution—trashing old rules to forge new ones. Cell 157, 7794 (2014).
  3. Deniz, E. & Erman, B. Long noncoding RNA (lincRNA), a new paradigm in gene expression control. Funct. Integr. Genomics 17, 135143 (2017).
  4. Deplancke, B., Alpern, D. & Gardeux, V. The genetics of transcription factor DNA binding variation. Cell 166, 538554 (2016).
  5. Manolio, T.A., Brooks, L.D. & Collins, F.S. A HapMap harvest of insights into the genetics of common disease. J. Clin. Invest. 118, 15901605 (2008).
  6. Welter, D. et al. The NHGRI GWAS Catalog, a curated resource of SNP-trait associations. Nucleic Acids Res. 42, D1001D1006 (2014).
  7. Huang, Q. Genetic study of complex diseases in the post-GWAS era. J. Genet. Genomics 42, 8798 (2015).
  8. Maurano, M.T. et al. Systematic localization of common disease-associated variation in regulatory DNA. Science 337, 11901195 (2012).
  9. Turner, B.M. Defining an epigenetic code. Nat. Cell Biol. 9, 26 (2007).
  10. Song, L. & Crawford, G.E. DNase-seq: a high-resolution technique for mapping active gene regulatory elements across the genome from mammalian cells. Cold Spring Harb. Protoc. 2010, (2010).
  11. Simon, J.M., Giresi, P.G., Davis, I.J. & Lieb, J.D. Using formaldehyde-assisted isolation of regulatory elements (FAIRE) to isolate active regulatory DNA. Nat. Protoc. 7, 256267 (2012).
  12. Buenrostro, J.D., Giresi, P.G., Zaba, L.C., Chang, H.Y. & Greenleaf, W.J. Transposition of native chromatin for fast and sensitive epigenomic profiling of open chromatin, DNA-binding proteins and nucleosome position. Nat. Methods 10, 12131218 (2013).
  13. Thurman, R.E. et al. The accessible chromatin landscape of the human genome. Nature 489, 7582 (2012).
  14. Barski, A. et al. High-resolution profiling of histone methylations in the human genome. Cell 129, 823837 (2007).
  15. Mikkelsen, T.S. et al. Genome-wide maps of chromatin state in pluripotent and lineage-committed cells. Nature 448, 553560 (2007).
  16. Wang, Z. et al. Combinatorial patterns of histone acetylations and methylations in the human genome. Nat. Genet. 40, 897903 (2008).
  17. Bell, O., Tiwari, V.K., Thomä, N.H. & Schübeler, D. Determinants and dynamics of genome accessibility. Nat. Rev. Genet. 12, 554564 (2011).
  18. Kouzarides, T. Chromatin modifications and their function. Cell 128, 693705 (2007).
  19. Roh, T.Y., Cuddapah, S. & Zhao, K. Active chromatin domains are defined by acetylation islands revealed by genome-wide mapping. Genes Dev. 19, 542552 (2005).
  20. Heintzman, N.D. et al. Distinct and predictive chromatin signatures of transcriptional promoters and enhancers in the human genome. Nat. Genet. 39, 311318 (2007).
  21. Shlyueva, D., Stampfel, G. & Stark, A. Transcriptional enhancers: from properties to genome-wide predictions. Nat. Rev. Genet. 15, 272286 (2014).
  22. Creyghton, M.P. et al. Histone H3K27ac separates active from poised enhancers and predicts developmental state. Proc. Natl. Acad. Sci. USA 107, 2193121936 (2010).
  23. Bernstein, B.E. et al. A bivalent chromatin structure marks key developmental genes in embryonic stem cells. Cell 125, 315326 (2006).
  24. Cheng, C. et al. Understanding transcriptional regulation by integrative analysis of transcription factor binding data. Genome Res. 22, 16581667 (2012).
  25. Hoffman, M.M. et al. Unsupervised pattern discovery in human chromatin structure through genomic segmentation. Nat. Methods 9, 473476 (2012).
  26. Hoffman, M.M. et al. Integrative annotation of chromatin elements from ENCODE data. Nucleic Acids Res. 41, 827841 (2013).
  27. Ernst, J. & Kellis, M. ChromHMM: automating chromatin-state discovery and characterization. Nat. Methods 9, 215216 (2012).
  28. Kundaje, A. et al. Integrative analysis of 111 reference human epigenomes. Nature 518, 317330 (2015).
  29. Romanoski, C.E., Glass, C.K., Stunnenberg, H.G., Wilson, L. & Almouzni, G. Epigenomics: roadmap for regulation. Nature 518, 314316 (2015).
  30. Fisher, W.W. et al. DNA regions bound at low occupancy by transcription factors do not drive patterned reporter gene expression in Drosophila. Proc. Natl. Acad. Sci. USA 109, 2133021335 (2012).
  31. Teytelman, L., Thurtle, D.M., Rine, J. & van Oudenaarden, A. Highly expressed loci are vulnerable to misleading ChIP localization of multiple unrelated proteins. Proc. Natl. Acad. Sci. USA 110, 1860218607 (2013).
  32. Li, X.Y. et al. Transcription factors bind thousands of active and inactive regions in the Drosophila blastoderm. PLoS Biol. 6, e27 (2008).
  33. Lizio, M. et al. Mapping mammalian cell-type-specific transcriptional regulatory networks using KD-CAGE and ChIP-seq data in the TC-YIK cell line. Front. Genet. 6, 331 (2015).
  34. Kwasnieski, J.C., Fiore, C., Chaudhari, H.G. & Cohen, B.A. High-throughput functional testing of ENCODE segmentation predictions. Genome Res. 24, 15951602 (2014).
  35. Kim, T.K. et al. Widespread transcription at neuronal activity-regulated enhancers. Nature 465, 182187 (2010).
  36. Core, L.J., Waterfall, J.J. & Lis, J.T. Nascent RNA sequencing reveals widespread pausing and divergent initiation at human promoters. Science 322, 18451848 (2008).
  37. Hah, N. et al. A rapid, extensive, and transient transcriptional response to estrogen signaling in breast cancer cells. Cell 145, 622634 (2011).
  38. Li, W. et al. Functional roles of enhancer RNAs for oestrogen-dependent transcriptional activation. Nature 498, 516520 (2013).
  39. Léveillé, N. et al. Genome-wide profiling of p53-regulated enhancer RNAs uncovers a subset of enhancers controlled by a lncRNA. Nat. Commun. 6, 6520 (2015).
  40. Wu, H. et al. Tissue-specific RNA expression marks distant-acting developmental enhancers. PLoS Genet. 10, e1004610 (2014).
  41. Shiraki, T. et al. Cap analysis gene expression for high-throughput analysis of transcriptional starting point and identification of promoter usage. Proc. Natl. Acad. Sci. USA 100, 1577615781 (2003).
  42. Hah, N., Murakami, S., Nagari, A., Danko, C.G. & Kraus, W.L. Enhancer transcripts mark active estrogen receptor binding sites. Genome Res. 23, 12101223 (2013).
  43. Melo, C.A. et al. eRNAs are required for p53-dependent enhancer activity and gene transcription. Mol. Cell 49, 524535 (2013).
  44. Andersson, R. et al. An atlas of active enhancers across human cell types and tissues. Nature 507, 455461 (2014).
  45. Wang, D. et al. Reprogramming transcription by distinct classes of enhancers functionally defined by eRNA. Nature 474, 390394 (2011).
  46. Koch, F. & Andrau, J.C. Initiating RNA polymerase II and TIPs as hallmarks of enhancer activity and tissue-specificity. Transcription 2, 263268 (2011).
  47. Koch, F. et al. Transcription initiation platforms and GTF recruitment at tissue-specific enhancers and promoters. Nat. Struct. Mol. Biol. 18, 956963 (2011).
  48. Melo, C.A., Léveillé, N. & Agami, R. eRNAs reach the heart of transcription. Cell Res. 23, 11511152 (2013).
  49. Inoue, F. & Ahituv, N. Decoding enhancers using massively parallel reporter assays. Genomics 106, 159164 (2015).
  50. Muerdter, F., Bory´n, L.M. & Arnold, C.D. STARR-seq—principles and applications. Genomics 106, 145150 (2015).
  51. Melnikov, A. et al. Systematic dissection and optimization of inducible enhancers in human cells using a massively parallel reporter assay. Nat. Biotechnol. 30, 271277 (2012).
  52. Kheradpour, P. et al. Systematic dissection of regulatory motifs in 2000 predicted human enhancers using a massively parallel reporter assay. Genome Res. 23, 800811 (2013).
  53. Birnbaum, R.Y. et al. Systematic dissection of coding exons at single nucleotide resolution supports an additional role in cell-specific transcriptional regulation. PLoS Genet. 10, e1004592 (2014).
  54. Patwardhan, R.P. et al. Massively parallel functional dissection of mammalian enhancers in vivo. Nat. Biotechnol. 30, 265270 (2012).
  55. White, M.A., Myers, C.A., Corbo, J.C. & Cohen, B.A. Massively parallel in vivo enhancer assay reveals that highly local features determine the cis-regulatory function of ChIP-seq peaks. Proc. Natl. Acad. Sci. USA 110, 1195211957 (2013).
  56. Arnold, C.D. et al. Genome-wide quantitative enhancer activity maps identified by STARR-seq. Science 339, 10741077 (2013).
  57. Patwardhan, R.P. et al. High-resolution analysis of DNA regulatory elements by synthetic saturation mutagenesis. Nat. Biotechnol. 27, 11731175 (2009).
  58. Vanhille, L. et al. High-throughput and quantitative assessment of enhancer activity in mammals by CapStarr-seq. Nat. Commun. 6, 6905 (2015).
  59. Smith, R.P. et al. Massively parallel decoding of mammalian regulatory sequences supports a flexible organizational model. Nat. Genet. 45, 10211028 (2013).
  60. Sharon, E. et al. Inferring gene regulatory logic from high-throughput measurements of thousands of systematically designed promoters. Nat. Biotechnol. 30, 521530 (2012).
  61. Kinney, J.B., Murugan, A., Callan, C.G. Jr. & Cox, E.C. Using deep sequencing to characterize the biophysical mechanism of a transcriptional regulatory sequence. Proc. Natl. Acad. Sci. USA 107, 91589163 (2010).
  62. Shen, S.Q. et al. Massively parallel cis-regulatory analysis in the mammalian central nervous system. Genome Res. 26, 238255 (2016).
  63. International HapMap Consortium. A haplotype map of the human genome. Nature 437, 12991320 (2005).
  64. Manolio, T.A. Bringing genome-wide association findings into clinical use. Nat. Rev. Genet. 14, 549558 (2013).
  65. McCarthy, M.I. et al. Genome-wide association studies for complex traits: consensus, uncertainty and challenges. Nat. Rev. Genet. 9, 356369 (2008).
  66. Schaub, M.A., Boyle, A.P., Kundaje, A., Batzoglou, S. & Snyder, M. Linking disease associations with regulatory information in the human genome. Genome Res. 22, 17481759 (2012).
  67. Cowper-Sallari, R. et al. Breast cancer risk-associated SNPs modulate the affinity of chromatin for FOXA1 and alter gene expression. Nat. Genet. 44, 11911198 (2012).
  68. Karczewski, K.J. et al. Systematic functional regulatory assessment of disease-associated variants. Proc. Natl. Acad. Sci. USA 110, 96079612 (2013).
  69. Whitington, T. et al. Gene regulatory mechanisms underpinning prostate cancer susceptibility. Nat. Genet. 48, 387397 (2016).
  70. Degner, J.F. et al. DNase I sensitivity QTLs are a major determinant of human expression variation. Nature 482, 390394 (2012).
  71. McVicker, G. et al. Identification of genetic variants that affect histone modifications in human cells. Science 342, 747749 (2013).
  72. Tehranchi, A.K. et al. Pooled ChIP-seq links variation in transcription factor binding to complex disease risk. Cell 165, 730741 (2016).
  73. Musunuru, K. et al. From noncoding variant to phenotype via SORT1 at the 1p13 cholesterol locus. Nature 466, 714719 (2010).
  74. Smemo, S. et al. Obesity-associated variants within FTO form long-range functional connections with IRX3. Nature 507, 371375 (2014).
  75. Farh, K.K. et al. Genetic and epigenetic fine mapping of causal autoimmune disease variants. Nature 518, 337343 (2015).
  76. Grubert, F. et al. Genetic control of chromatin states in humans involves local and distal chromosomal interactions. Cell 162, 10511065 (2015).
  77. Kasowski, M. et al. Variation in transcription factor binding among humans. Science 328, 232235 (2010).
  78. McDaniell, R. et al. Heritable individual-specific and allele-specific chromatin signatures in humans. Science 328, 235239 (2010).
  79. Reddy, T.E. et al. Effects of sequence variation on differential allelic transcription factor occupancy and gene expression. Genome Res. 22, 860869 (2012).
  80. Wu, T.D. & Nacu, S. Fast and SNP-tolerant detection of complex variants and splicing in short reads. Bioinformatics 26, 873881 (2010).
  81. Rozowsky, J. et al. AlleleSeq: analysis of allele-specific expression and binding in a network framework. Mol. Syst. Biol. 7, 522 (2011).
  82. van de Geijn, B., McVicker, G., Gilad, Y. & Pritchard, J.K. WASP: allele-specific software for robust molecular quantitative trait locus discovery. Nat. Methods 12, 10611063 (2015).
  83. Romanel, A., Lago, S., Prandi, D., Sboner, A. & Demichelis, F. ASEQ: fast allele-specific studies from next-generation sequencing data. BMC Med. Genomics 8, 9 (2015).
  84. Harvey, C.T. et al. QuASAR: quantitative allele-specific analysis of reads. Bioinformatics 31, 12351242 (2015).
  85. Gilad, Y., Rifkin, S.A. & Pritchard, J.K. Revealing the architecture of gene regulation: the promise of eQTL studies. Trends Genet. 24, 408415 (2008).
  86. Nica, A.C. et al. Candidate causal regulatory effects by integration of expression QTLs with complex trait genetic associations. PLoS Genet. 6, e1000895 (2010).
  87. Nicolae, D.L. et al. Trait-associated SNPs are more likely to be eQTLs: annotation to enhance discovery from GWAS. PLoS Genet. 6, e1000888 (2010).
  88. Bahcall, O.G. Human genetics: GTEx pilot quantifies eQTL variation across tissues and individuals. Nat. Rev. Genet. 16, 375 (2015).
  89. GTEx Consortium. Human genomics. The Genotype-Tissue Expression (GTEx) pilot analysis: multitissue gene regulation in humans. Science 348, 648660 (2015).
  90. Fairfax, B.P. et al. Innate immune activity conditions the effect of regulatory variants upon monocyte gene expression. Science 343, 1246949 (2014).
  91. Parker, S.C. et al. Chromatin stretch enhancer states drive cell-specific gene regulation and harbor human disease risk variants. Proc. Natl. Acad. Sci. USA 110, 1792117926 (2013).
  92. Kilpinen, H. et al. Coordinated effects of sequence variation on DNA binding, chromatin structure, and transcription. Science 342, 744747 (2013).
  93. Skelly, D.A., Johansson, M., Madeoy, J., Wakefield, J. & Akey, J.M. A powerful and flexible statistical framework for testing hypotheses of allele-specific gene expression from RNA-seq data. Genome Res. 21, 17281737 (2011).
  94. Pickrell, J.K. et al. Understanding mechanisms underlying human gene expression variation with RNA sequencing. Nature 464, 768772 (2010).
  95. Montgomery, S.B. et al. Transcriptome genetics using second generation sequencing in a Caucasian population. Nature 464, 773777 (2010).
  96. de Wit, E. & de Laat, W. A decade of 3C technologies: insights into nuclear organization. Genes Dev. 26, 1124 (2012).
  97. Dekker, J., Marti-Renom, M.A. & Mirny, L.A. Exploring the three-dimensional organization of genomes: interpreting chromatin interaction data. Nat. Rev. Genet. 14, 390403 (2013).
  98. Dixon, J.R. et al. Topological domains in mammalian genomes identified by analysis of chromatin interactions. Nature 485, 376380 (2012).
  99. Nora, E.P. et al. Spatial partitioning of the regulatory landscape of the X-inactivation centre. Nature 485, 381385 (2012).
  100. Rao, S.S. et al. A 3D map of the human genome at kilobase resolution reveals principles of chromatin looping. Cell 159, 16651680 (2014).
  101. Lieberman-Aiden, E. et al. Comprehensive mapping of long-range interactions reveals folding principles of the human genome. Science 326, 289293 (2009).
  102. Jin, F. et al. A high-resolution map of the three-dimensional chromatin interactome in human cells. Nature 503, 290294 (2013).
  103. Tang, Z. et al. CTCF-Mediated Human 3D Genome architecture reveals chromatin topology for transcription. Cell 163, 16111627 (2015).
  104. Lupiáñez, D.G. et al. Disruptions of topological chromatin domains cause pathogenic rewiring of gene-enhancer interactions. Cell 161, 10121025 (2015).
  105. Hnisz, D. et al. Activation of proto-oncogenes by disruption of chromosome neighborhoods. Science 351, 14541458 (2016).
  106. Katainen, R. et al. CTCF/cohesin-binding sites are frequently mutated in cancer. Nat. Genet. 47, 818821 (2015).
  107. Flavahan, W.A. et al. Insulator dysfunction and oncogene activation in IDH mutant gliomas. Nature 529, 110114 (2016).
  108. Grimmer, M.R. & Costello, J.F. Cancer: oncogene brought into the loop. Nature 529, 3435 (2016).
  109. Dryden, N.H. et al. Unbiased analysis of potential targets of breast cancer susceptibility loci by Capture Hi-C. Genome Res. 24, 18541868 (2014).
  110. Mifsud, B. et al. Mapping long-range promoter contacts in human cells with high-resolution capture Hi-C. Nat. Genet. 47, 598606 (2015).
  111. Fullwood, M.J. et al. An oestrogen-receptor-alpha-bound human chromatin interactome. Nature 462, 5864 (2009).
  112. Kieffer-Kwon, K.R. et al. Interactome maps of mouse gene regulatory domains reveal basic principles of transcriptional regulation. Cell 155, 15071520 (2013).
  113. Li, G. et al. Extensive promoter-centered chromatin interactions provide a topological basis for transcription regulation. Cell 148, 8498 (2012).
  114. Zhang, Y. et al. Chromatin connectivity maps reveal dynamic promoter-enhancer long-range associations. Nature 504, 306310 (2013).
  115. Mahfouz, M.M., Piatek, A. & Stewart, C.N. Jr. Genome engineering via TALENs and CRISPR/Cas9 systems: challenges and perspectives. Plant Biotechnol. J. 12, 10061014 (2014).
  116. Gaj, T., Gersbach, C.A. & Barbas, C.F., III. ZFN, TALEN, and CRISPR/Cas-based methods for genome engineering. Trends Biotechnol. 31, 397405 (2013).
  117. Hsu, P.D., Lander, E.S. & Zhang, F. Development and applications of CRISPR-Cas9 for genome engineering. Cell 157, 12621278 (2014).
  118. Sander, J.D. & Joung, J.K. CRISPR–Cas systems for editing, regulating and targeting genomes. Nat. Biotechnol. 32, 347355 (2014).
  119. Kim, H. & Kim, J.S. A guide to genome engineering with programmable nucleases. Nat. Rev. Genet. 15, 321334 (2014).
  120. Wei, C. et al. TALEN or Cas9—rapid, efficient and specific choices for genome modifications. J. Genet. Genomics 40, 281289 (2013).
  121. Boch, J. et al. Breaking the code of DNA binding specificity of TAL-type III effectors. Science 326, 15091512 (2009).
  122. Moscou, M.J. & Bogdanove, A.J. A simple cipher governs DNA recognition by TAL effectors. Science 326, 1501 (2009).
  123. Spisák, S. et al. CAUSEL: an epigenome- and genome-editing pipeline for establishing function of noncoding GWAS variants. Nat. Med. 21, 13571363 (2015).
  124. Soldner, F. et al. Parkinson-associated risk variant in distal enhancer of a-synuclein modulates target gene expression. Nature 533, 9599 (2016).
  125. Shalem, O. et al. Genome-scale CRISPR-Cas9 knockout screening in human cells. Science 343, 8487 (2014).
  126. Zhou, Y. et al. High-throughput screening of a CRISPR/Cas9 library for functional genomics in human cells. Nature 509, 487491 (2014).
  127. Parnas, O. et al. A genome-wide CRISPR screen in primary immune cells to dissect regulatory networks. Cell 162, 675686 (2015).
  128. Canver, M.C. et al. BCL11A enhancer dissection by Cas9-mediated in situ saturating mutagenesis. Nature 527, 192197 (2015).
  129. Rajagopal, N. et al. High-throughput mapping of regulatory DNA. Nat. Biotechnol. 34, 167174 (2016).
  130. Sanjana, N.E. et al. High-resolution interrogation of functional elements in the noncoding genome. Science 353, 15451549 (2016).
  131. Korkmaz, G. et al. Functional genetic screens for enhancer elements in the human genome using CRISPR-Cas9. Nat. Biotechnol. 34, 192198 (2016).
  132. Guo, Y. et al. CRISPR inversion of CTCF sites alters genome topology and enhancer/promoter function. Cell 162, 900910 (2015).
  133. Fanucchi, S., Shibayama, Y., Burd, S., Weinberg, M.S. & Mhlanga, M.M. Chromosomal contact permits transcription between coregulated genes. Cell 155, 606620 (2013).
  134. Dominguez, A.A., Lim, W.A. & Qi, L.S. Beyond editing: repurposing CRISPR-Cas9 for precision genome regulation and interrogation. Nat. Rev. Mol. Cell Biol. 17, 515 (2016).
  135. Kungulovski, G. & Jeltsch, A. Epigenome editing: state of the art, concepts, and perspectives. Trends Genet. 32, 101113 (2016).
  136. Gilbert, L.A. et al. CRISPR-mediated modular RNA-guided regulation of transcription in eukaryotes. Cell 154, 442451 (2013).
  137. Maeder, M.L. et al. Targeted DNA demethylation and activation of endogenous genes using programmable TALE-TET1 fusion proteins. Nat. Biotechnol. 31, 11371142 (2013).
  138. Perez-Pinera, P. et al. RNA-guided gene activation by CRISPR-Cas9-based transcription factors. Nat. Methods 10, 973976 (2013).
  139. Maeder, M.L. et al. CRISPR RNA-guided activation of endogenous human genes. Nat. Methods 10, 977979 (2013).
  140. Konermann, S. et al. Genome-scale transcriptional activation by an engineered CRISPR-Cas9 complex. Nature 517, 583588 (2015).
  141. Tanenbaum, M.E., Gilbert, L.A., Qi, L.S., Weissman, J.S. & Vale, R.D. A protein-tagging system for signal amplification in gene expression and fluorescence imaging. Cell 159, 635646 (2014).
  142. Gao, X. et al. Comparison of TALE designer transcription factors and the CRISPR/dCas9 in regulation of gene expression by targeting enhancers. Nucleic Acids Res. 42, e155 (2014).
  143. Chen, B. et al. Dynamic imaging of genomic loci in living human cells by an optimized CRISPR/Cas system. Cell 155, 14791491 (2013).
  144. Gilbert, L.A. et al. Genome-scale CRISPR-mediated control of gene repression and activation. Cell 159, 647661 (2014).
  145. Thakore, P.I. et al. Highly specific epigenome editing by CRISPR-Cas9 repressors for silencing of distal regulatory elements. Nat. Methods 12, 11431149 (2015).
  146. Mendenhall, E.M. et al. Locus-specific editing of histone modifications at endogenous enhancers. Nat. Biotechnol. 31, 11331136 (2013).
  147. Shi, Y. et al. Histone demethylation mediated by the nuclear amine oxidase homolog LSD1. Cell 119, 941953 (2004).
  148. Kearns, N.A. et al. Functional annotation of native enhancers with a Cas9-histone demethylase fusion. Nat. Methods 12, 401403 (2015).
  149. Hilton, I.B. et al. Epigenome editing by a CRISPR-Cas9-based acetyltransferase activates genes from promoters and enhancers. Nat. Biotechnol. 33, 510517 (2015).
  150. Fulco, C.P. et al. Systematic mapping of functional enhancer-promoter connections with CRISPR interference. Science 354, 769773 (2016).
  151. Hsu, P.D. et al. DNA targeting specificity of RNA-guided Cas9 nucleases. Nat. Biotechnol. 31, 827832 (2013).
  152. Tsai, S.Q. et al. GUIDE-seq enables genome-wide profiling of off-target cleavage by CRISPR-Cas nucleases. Nat. Biotechnol. 33, 187197 (2015).
  153. Kleinstiver, B.P. et al. Broadening the targeting range of Staphylococcus aureus CRISPR-Cas9 by modifying PAM recognition. Nat. Biotechnol. 33, 12931298 (2015).
  154. Kleinstiver, B.P. et al. Engineered CRISPR-Cas9 nucleases with altered PAM specificities. Nature 523, 481485 (2015).
  155. Haeussler, M. & Concordet, J.P. Genome editing with CRISPR-Cas9: can it get any better? J. Genet. Genomics 43, 239250 (2016).
  156. Kim, D. et al. Digenome-seq: genome-wide profiling of CRISPR-Cas9 off-target effects in human cells. Nat. Methods 12, 237243 (2015).
  157. Plank, J.L. & Dean, A. Enhancer function: mechanistic and genome-wide insights come together. Mol. Cell. 55, 514 (2014).
  158. Boyle, A.P. et al. Annotation of functional variation in personal genomes using RegulomeDB. Genome Res. 22, 17901797 (2012).
  159. Li, G. et al. ChIA-PET tool for comprehensive chromatin interaction analysis with paired-end tag sequencing. Genome Biol. 11, R22 (2010).
  160. Crawford, G.E. et al. Genome-wide mapping of DNase hypersensitive sites using massively parallel signature sequencing (MPSS). Genome Res. 16, 123131 (2006).
  161. Giresi, P.G., Kim, J., McDaniell, R.M., Iyer, V.R. & Lieb, J.D. FAIRE (Formaldehyde-Assisted Isolation of Regulatory Elements) isolates active regulatory elements from human chromatin. Genome Res. 17, 877885 (2007).
  162. Johnson, D.S., Mortazavi, A., Myers, R.M. & Wold, B. Genome-wide mapping of in vivo protein-DNA interactions. Science 316, 14971502 (2007).
  163. Li, W., Notani, D. & Rosenfeld, M.G. Enhancers as noncoding RNA transcription units: recent insights and future perspectives. Nat. Rev. Genet. 17, 207223 (2016).
  164. Danko, C.G. et al. Identification of active transcriptional regulatory elements from GRO-seq data. Nat. Methods 12, 433438 (2015).
  165. Zhang, F. et al. Efficient construction of sequence-specific TAL effectors for modulating mammalian transcription. Nat. Biotechnol. 29, 149153 (2011).
  166. Cermak, T. et al. Efficient design and assembly of custom TALEN and other TAL effector-based constructs for DNA targeting. Nucleic Acids Res. 39, e82 (2011).
  167. Miller, J.C. et al. A TALE nuclease architecture for efficient genome editing. Nat. Biotechnol. 29, 143148 (2011).
  168. Cong, L. et al. Multiplex genome engineering using CRISPR/Cas systems. Science 339, 819823 (2013).

Download references

Author information


  1. Department of Human Molecular Genetics and Biochemistry, Sackler School of Medicine, Tel Aviv, Israel.

    • Ran Elkon
  2. Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel.

    • Ran Elkon
  3. Division of Oncogenomics, The Netherlands Cancer Institute, Amsterdam, The Netherlands.

    • Reuven Agami
  4. Erasmus MC, Rotterdam University, The Netherlands.

    • Reuven Agami

Competing financial interests

The authors declare no competing financial interests.

Corresponding authors

Correspondence to:

Author details

Additional data