Computational Approaches for Understanding Sequence Variation Effects on the 3D Genome Architecture

Literature Cited

1. 1.

   Spielmann M, Lupiáñez DG, Mundlos S. 2018. Structural variation in the 3D genome. Nat. Rev. Genet. 19:7453–67
   [Google Scholar]
2. 2.

   Bonev B, Cavalli G. 2016. Organization and function of the 3D genome. Nat. Rev. Genet. 17:11661–78
   [Google Scholar]
3. 3.

   van Steensel B, Furlong EEM. 2019. The role of transcription in shaping the spatial organization of the genome. Nat. Rev. Mol. Cell Biol. 20:6327–37
   [Google Scholar]
4. 4.

   Cullen KE, Kladde MP, Seyfred MA. 1993. Interaction between transcription regulatory regions of prolactin chromatin. Science 261:5118203–6
   [Google Scholar]
5. 5.

   Dekker J, Rippe K, Dekker M, Kleckner N. 2002. Capturing chromosome conformation. Science 295:55581306–11
   [Google Scholar]
6. 6.

   Van De Werken HJG, Landan G, Holwerda SJB, Hoichman M, Klous P et al. 2012. Robust 4C-seq data analysis to screen for regulatory DNA interactions. Nat. Methods 9:10969–72
   [Google Scholar]
7. 7.

   Dostie J, Richmond TA, Arnaout RA, Selzer RR, Lee WL et al. 2006. Chromosome Conformation Capture Carbon Copy (5C): a massively parallel solution for mapping interactions between genomic elements. Genome Res 16:101299–309
   [Google Scholar]
8. 8.

   Simonis M, Klous P, Splinter E, Moshkin Y, Willemsen R et al. 2006. Nuclear organization of active and inactive chromatin domains uncovered by chromosome conformation capture-on-chip (4C). Nat. Genet. 38:111348–54
   [Google Scholar]
9. 9.

   Kempfer R, Pombo A. 2020. Methods for mapping 3D chromosome architecture. Nat. Rev. Genet. 21:4207–26
   [Google Scholar]
10. 10.

    Jerkovic I, Cavalli G. 2021. Understanding 3D genome organization by multidisciplinary methods. Nat. Rev. Mol. Cell Biol. 22:8511–28
    [Google Scholar]
11. 11.

    Hsieh T-HS, Weiner A, Lajoie B, Dekker J, Friedman N, Rando OJ. 2015. Mapping nucleosome resolution chromosome folding in yeast by Micro-C. Cell 162:1108–19
    [Google Scholar]
12. 12.

    Hsieh T-HS, Fudenberg G, Goloborodko A, Rando OJ. 2016. Micro-C XL: assaying chromosome conformation from the nucleosome to the entire genome. Nat. Methods 13:121009–11
    [Google Scholar]
13. 13.

    Krietenstein N, Abraham S, Venev SV, Abdennur N, Gibcus J et al. 2020. Ultrastructural details of mammalian chromosome architecture. Mol. Cell 78:3554–65.e7
    [Google Scholar]
14. 14.

    Akgol Oksuz B, Yang L, Abraham S, Venev SV, Krietenstein N et al. 2021. Systematic evaluation of chromosome conformation capture assays. Nat. Methods 18:91046–55
    [Google Scholar]
15. 15.

    Hua P, Badat M, Hanssen LLP, Hentges LD, Crump N et al. 2021. Defining genome architecture at base-pair resolution. Nature 595:7865125–29
    [Google Scholar]
16. 16.

    Fullwood MJ, Liu MH, Pan YF, Liu J, Xu H et al. 2009. An oestrogen-receptor-α-bound human chromatin interactome. Nature 462:726958–64
    [Google Scholar]
17. 17.

    Mumbach MR, Rubin AJ, Flynn RA, Dai C, Khavari PA et al. 2016. HiChIP: efficient and sensitive analysis of protein-directed genome architecture. Nat. Methods 13:11919–22
    [Google Scholar]
18. 18.

    Fang R, Yu M, Li G, Chee S, Liu T et al. 2016. Mapping of long-range chromatin interactions by proximity ligation-assisted ChIP-seq. Cell Res 26:121345–48
    [Google Scholar]
19. 19.

    Nagano T, Lubling Y, Yaffe E, Wingett SW, Dean W et al. 2015. Single-cell Hi-C for genome-wide detection of chromatin interactions that occur simultaneously in a single cell. Nat. Protoc. 10:121986–2003
    [Google Scholar]
20. 20.

    Nagano T, Lubling Y, Stevens TJ, Schoenfelder S, Yaffe E et al. 2013. Single-cell Hi-C reveals cell-to-cell variability in chromosome structure. Nature 502:746959–64
    [Google Scholar]
21. 21.

    Nagano T, Lubling Y, Várnai C, Dudley C, Leung W et al. 2017. Cell-cycle dynamics of chromosomal organization at single-cell resolution. Nature 547:766161–67
    [Google Scholar]
22. 22.

    Beagrie RA, Scialdone A, Schueler M, Kraemer DCA, Chotalia M et al. 2017. Complex multi-enhancer contacts captured by genome architecture mapping. Nature 543:7646519–24
    [Google Scholar]
23. 23.

    Quinodoz SA, Ollikainen N, Tabak B, Palla A, Schmidt JM et al. 2018. Higher-order inter-chromosomal hubs shape 3D genome organization in the nucleus. Cell 174:3744–57.e24
    [Google Scholar]
24. 24.

    Koch L. 2018. Chromosome interaction hubs around nuclear bodies. Nat. Rev. Genet. 19:470–71
    [Google Scholar]
25. 25.

    Fiorillo L, Musella F, Conte M, Kempfer R, Chiariello AM et al. 2021. Comparison of the Hi-C, GAM and SPRITE methods using polymer models of chromatin. Nat. Methods 18:5482–90
    [Google Scholar]
26. 26.

    Ulahannan N, Pendleton M, Deshpande A, Schwenk S, Behr JM et al. 2019. Nanopore sequencing of DNA concatemers reveals higher-order features of chromatin structure. bioRxiv 10.1101/833590. https://doi.org/10.1101/833590
    [Crossref]
27. 27.

    Sexton T, Yaffe E, Kenigsberg E, Bantignies F, Leblanc B et al. 2012. Three-dimensional folding and functional organization principles of the Drosophila genome. Cell 148:3458–72
    [Google Scholar]
28. 28.

    Nora EP, Lajoie BR, Schulz EG, Giorgetti L, Okamoto I et al. 2012. Spatial partitioning of the regulatory landscape of the X-inactivation centre. Nature 485:7398381–85
    [Google Scholar]
29. 29.

    Dixon JR, Selvaraj S, Yue F, Kim A, Li Y et al. 2012. Topological domains in mammalian genomes identified by analysis of chromatin interactions. Nature 485:7398376–80
    [Google Scholar]
30. 30.

    Norton HK, Emerson DJ, Huang H, Kim J, Titus KR et al. 2018. Detecting hierarchical genome folding with network modularity. Nat. Methods 15:2119–22
    [Google Scholar]
31. 31.

    Rao SSP, Huntley MH, Durand NC, Stamenova EK, Bochkov ID et al. 2014. A 3D map of the human genome at kilobase resolution reveals principles of chromatin looping. Cell 159:71665–80
    [Google Scholar]
32. 32.

    Nasmyth K, Haering CH. 2009. Cohesin: its roles and mechanisms. Annu. Rev. Genet. 43:525–58
    [Google Scholar]
33. 33.

    Riggs AD. 1990. DNA methylation and late replication probably aid cell memory, and type I DNA reeling could aid chromosome folding and enhancer function. Philos. Trans. R. Soc. Lond. B 326:1235285–97
    [Google Scholar]
34. 34.

    Alipour E, Marko JF. 2012. Self-organization of domain structures by DNA-loop-extruding enzymes. Nucleic Acids Res 40:2211202–12
    [Google Scholar]
35. 35.

    Sanborn AL, Rao SSP, Huang S-C, Durand NC, Huntley MH et al. 2015. Chromatin extrusion explains key features of loop and domain formation in wild-type and engineered genomes. PNAS 112:47E6456–65
    [Google Scholar]
36. 36.

    Fudenberg G, Imakaev M, Lu C, Goloborodko A, Abdennur N, Mirny LA. 2016. Formation of chromosomal domains by loop extrusion. Cell Rep 15:92038–49
    [Google Scholar]
37. 37.

    Rao SSP, Huang SC, St. Hilaire BG, Engreitz JM, Perez EM et al. 2017. Cohesin loss eliminates all loop domains. Cell 171:2305–20.e24
    [Google Scholar]
38. 38.

    Bantignies F, Roure V, Comet I, Leblanc B, Schuettengruber B et al. 2011. Polycomb-dependent regulatory contacts between distant Hox loci in Drosophila. Cell 144:2214–26
    [Google Scholar]
39. 39.

    Palstra R-J, Tolhuis B, Splinter E, Nijmeijer R, Grosveld F, de Laat W. 2003. The β-globin nuclear compartment in development and erythroid differentiation. Nat. Genet. 35:190–94
    [Google Scholar]
40. 40.

    Schoenfelder S, Sexton T, Chakalova L, Cope NF, Horton A et al. 2010. Preferential associations between co-regulated genes reveal a transcriptional interactome in erythroid cells. Nat. Genet. 42:153–61
    [Google Scholar]
41. 41.

    Pombo A, Dillon N. 2015. Three-dimensional genome architecture: players and mechanisms. Nat. Rev. Mol. Cell Biol. 16:4245–57
    [Google Scholar]
42. 42.

    Lieberman-Aiden E, van Berkum NL, Williams L, Imakaev M, Ragoczy T et al. 2009. Comprehensive mapping of long-range interactions reveals folding principles of the human genome. Science 326:5950289–93
    [Google Scholar]
43. 43.

    Kalhor R, Tjong H, Jayathilaka N, Alber F, Chen L 2011. Genome architectures revealed by tethered chromosome conformation capture and population-based modeling. Nat. Biotechnol. 30:90–98
    [Google Scholar]
44. 44.

    Wijchers PJ, Krijger PHL, Geeven G, Zhu Y, Denker A et al. 2016. Cause and consequence of tethering a subTAD to different nuclear compartments. Mol. Cell 61:3461–73
    [Google Scholar]
45. 45.

    Wang S, Su J-H, Beliveau BJ, Bintu B, Moffitt JR et al. 2016. Spatial organization of chromatin domains and compartments in single chromosomes. Science 353:6299598–602
    [Google Scholar]
46. 46.

    Gu H, Harris H, Olshansky M, Eliaz Y, Krishna A et al. 2021. Fine-mapping of nuclear compartments using ultra-deep Hi-C shows that active promoter and enhancer elements localize in the active A compartment even when adjacent sequences do not. bioRxiv 2021.10.03.462599. https://doi.org/10.1101/2021.10.03.462599
    [Crossref]
47. 47.

    Lichter P, Cremer T, Borden J, Manuelidis L, Ward DC. 1988. Delineation of individual human chromosomes in metaphase and interphase cells by in situ suppression hybridization using recombinant DNA libraries. Hum. Genet. 80:3224–34
    [Google Scholar]
48. 48.

    Cremer T, Cremer M. 2010. Chromosome territories. Cold Spring Harb. Perspect. Biol. 2:3a003889
    [Google Scholar]
49. 49.

    Sun HB, Shen J, Yokota H. 2000. Size-dependent positioning of human chromosomes in interphase nuclei. Biophys. J. 79:1184–90
    [Google Scholar]
50. 50.

    Bickmore WA, van Steensel B. 2013. Genome architecture: domain organization of interphase chromosomes. Cell 152:61270–84
    [Google Scholar]
51. 51.

    Sexton T, Cavalli G. 2015. The role of chromosome domains in shaping the functional genome. Cell 160:61049–59
    [Google Scholar]
52. 52.

    Therizols P, Illingworth RS, Courilleau C, Boyle S, Wood AJ, Bickmore WA. 2014. Chromatin decondensation is sufficient to alter nuclear organization in embryonic stem cells. Science 346:62141238–42
    [Google Scholar]
53. 53.

    Gonzalez-Sandoval A, Towbin BD, Kalck V, Cabianca DS, Gaidatzis D et al. 2015. Perinuclear anchoring of H3K9-methylated chromatin stabilizes induced cell fate in C. elegans embryos. Cell 163:61333–47
    [Google Scholar]
54. 54.

    Lupiáñez DG, Spielmann M, Mundlos S. 2016. Breaking TADs: how alterations of chromatin domains result in disease. Trends Genet 32:4225–37
    [Google Scholar]
55. 55.

    Franke M, Ibrahim DM, Andrey G, Schwarzer W, Heinrich V et al. 2016. Formation of new chromatin domains determines pathogenicity of genomic duplications. Nature 538:7624265–69
    [Google Scholar]
56. 56.

    Lettice LA, Daniels S, Sweeney E, Venkataraman S, Devenney PS et al. 2011. Enhancer-adoption as a mechanism of human developmental disease. Hum. Mutat. 32:121492–99
    [Google Scholar]
57. 57.

    Krijger PHL, de Laat W. 2016. Regulation of disease-associated gene expression in the 3D genome. Nat. Rev. Mol. Cell Biol. 17:771–82
    [Google Scholar]
58. 58.

    Alkan C, Coe BP, Eichler EE. 2011. Genome structural variation discovery and genotyping. Nat. Rev. Genet. 12:5363–76
    [Google Scholar]
59. 59.

    Ho SS, Urban AE, Mills RE. 2020. Structural variation in the sequencing era. Nat. Rev. Genet. 21:3171–89
    [Google Scholar]
60. 60.

    Feuk L, Carson AR, Scherer SW. 2006. Structural variation in the human genome. Nat. Rev. Genet. 7:285–97
    [Google Scholar]
61. 61.

    Sedlazeck FJ, Lee H, Darby CA, Schatz MC. 2018. Piercing the dark matter: bioinformatics of long-range sequencing and mapping. Nat. Rev. Genet. 19:6329–46
    [Google Scholar]
62. 62.

    Zhang F, Carvalho CMB, Lupski JR. 2009. Complex human chromosomal and genomic rearrangements. Trends Genet 25:7298–307
    [Google Scholar]
63. 63.

    Li Y, Roberts ND, Wala JA, Shapira O, Schumacher SE et al. 2020. Patterns of somatic structural variation in human cancer genomes. Nature 578:7793112–21
    [Google Scholar]
64. 64.

    Sudmant PH, Rausch T, Gardner EJ, Handsaker RE, Abyzov A et al. 2015. An integrated map of structural variation in 2,504 human genomes. Nature 526:757175–81
    [Google Scholar]
65. 65.

    Sebat J, Lakshmi B, Malhotra D, Troge J, Lese-Martin C et al. 2007. Strong association of de novo copy number mutations with autism. Science 316:5823445–49
    [Google Scholar]
66. 66.

    de Vries BBA, Pfundt R, Leisink M, Koolen DA, Vissers LELM et al. 2005. Diagnostic genome profiling in mental retardation. Am. J. Hum. Genet. 77:4606–16
    [Google Scholar]
67. 67.

    Aganezov S, Goodwin S, Sherman RM, Sedlazeck FJ, Arun G et al. 2020. Comprehensive analysis of structural variants in breast cancer genomes using single-molecule sequencing. Genome Res 30:91258–73
    [Google Scholar]
68. 68.

    Weischenfeldt J, Symmons O, Spitz F, Korbel JO. 2013. Phenotypic impact of genomic structural variation: insights from and for human disease. Nat. Rev. Genet. 14:2125–38
    [Google Scholar]
69. 69.

    Hyde CL, Nagle MW, Tian C, Chen X, Paciga SA et al. 2016. Identification of 15 genetic loci associated with risk of major depression in individuals of European descent. Nat. Genet. 48:91031–36
    [Google Scholar]
70. 70.

    Zhao W, Rasheed A, Tikkanen E, Lee J-J, Butterworth AS et al. 2017. Identification of new susceptibility loci for type 2 diabetes and shared etiological pathways with coronary heart disease. Nat. Genet. 49:101450–57
    [Google Scholar]
71. 71.

    Lander ES, Linton LM, Birren B, Nusbaum C, Zody MC et al. 2001. Initial sequencing and analysis of the human genome. Nature 409:6822860–921
    [Google Scholar]
72. 72.

    Int. HapMap Consort 2005. A haplotype map of the human genome. Nature 437:70631299–320
    [Google Scholar]
73. 73.

    Buniello A, MacArthur JAL, Cerezo M, Harris LW, Hayhurst J et al. 2019. The NHGRI-EBI GWAS Catalog of published genome-wide association studies, targeted arrays and summary statistics 2019. Nucleic Acids Res 47:D1D1005–12
    [Google Scholar]
74. 74.

    Martincorena I, Campbell PJ. 2015. Somatic mutation in cancer and normal cells. Science 349:62551483–89
    [Google Scholar]
75. 75.

    Tam V, Patel N, Turcotte M, Bossé Y, Paré G, Meyre D. 2019. Benefits and limitations of genome-wide association studies. Nat. Rev. Genet. 20:8467–84
    [Google Scholar]
76. 76.

    Lupiáñez DG, Kraft K, Heinrich V, Krawitz P, Brancati F et al. 2015. Disruptions of topological chromatin domains cause pathogenic rewiring of gene-enhancer interactions. Cell 161:51012–25
    [Google Scholar]
77. 77.

    Hnisz D, Weintraub AS, Day DS, Valton A-L, Bak RO et al. 2016. Activation of proto-oncogenes by disruption of chromosome neighborhoods. Science 351:62801454–58
    [Google Scholar]
78. 78.

    Weischenfeldt J, Dubash T, Drainas AP, Mardin BR, Chen Y et al. 2017. Pan-cancer analysis of somatic copy-number alterations implicates IRS4 and IGF2 in enhancer hijacking. Nat. Genet. 49:65–74
    [Google Scholar]
79. 79.

    Gorkin DU, Qiu Y, Hu M, Fletez-Brant K, Liu T et al. 2019. Common DNA sequence variation influences 3-dimensional conformation of the human genome. Genome Biol 20:255
    [Google Scholar]
80. 80.

    Dekker J, Marti-Renom MA, Mirny LA. 2013. Exploring the three-dimensional organization of genomes: interpreting chromatin interaction data. Nat. Rev. Genet. 14:6390–403
    [Google Scholar]
81. 81.

    Pal K, Forcato M, Ferrari F. 2019. Hi-C analysis: from data generation to integration. Biophys. Rev. 11:67–78
    [Google Scholar]
82. 82.

    Lynch M, Walsh B. 1998. Genetics and Analysis of Quantitative Traits Sunderland, MA: Sinauer
83. 83.

    Dixon JR, Xu J, Dileep V, Zhan Y, Song F et al. 2018. Integrative detection and analysis of structural variation in cancer genomes. Nat. Genet. 50:1388–98
    [Google Scholar]
84. 84.

    Wang S, Lee S, Chu C, Jain D, Kerpedjiev P et al. 2020. HiNT: a computational method for detecting copy number variations and translocations from Hi-C data. Genome Biol 21:73
    [Google Scholar]
85. 85.

    Wang X, Xu J, Zhang B, Hou Y, Song F et al. 2021. Genome-wide detection of enhancer-hijacking events from chromatin interaction data in rearranged genomes. Nat. Methods 18:6661–68
    [Google Scholar]
86. 86.

    He B, Chen C, Teng L, Tan K. 2014. Global view of enhancer-promoter interactome in human cells. PNAS 111:21E2191–99
    [Google Scholar]
87. 87.

    Roy S, Siahpirani AF, Chasman D, Knaack S, Ay F et al. 2015. A predictive modeling approach for cell line-specific long-range regulatory interactions. Nucleic Acids Res 43:188694–712
    [Google Scholar]
88. 88.

    Zhu Y, Chen Z, Zhang K, Wang M, Medovoy D et al. 2016. Constructing 3D interaction maps from 1D epigenomes. Nat. Commun. 7:10812
    [Google Scholar]
89. 89.

    Whalen S, Truty RM, Pollard KS. 2016. Enhancer-promoter interactions are encoded by complex genomic signatures on looping chromatin. Nat. Genet. 48:5488–96
    [Google Scholar]
90. 90.

    Cao Q, Anyansi C, Hu X, Xu L, Xiong L et al. 2017. Reconstruction of enhancer-target networks in 935 samples of human primary cells, tissues and cell lines. Nat. Genet. 49:101428–36
    [Google Scholar]
91. 91.

    Belokopytova PS, Nuriddinov MA, Mozheiko EA, Fishman D, Fishman V. 2020. Quantitative prediction of enhancer-promoter interactions. Genome Res 30:72–84
    [Google Scholar]
92. 92.

    Zhang S, Chasman D, Knaack S, Roy S 2019. In silico prediction of high-resolution Hi-C interaction matrices. Nat. Commun. 10:5449
    [Google Scholar]
93. 93.

    Goodfellow I, Bengio Y, Courville A. 2016. Deep Learning Cambridge, MA: MIT Press
94. 94.

    Murphy KP. 2022. Probabilistic Machine Learning: An Introduction Cambridge, MA: MIT Press
95. 95.

    Alipanahi B, Delong A, Weirauch MT, Frey BJ. 2015. Predicting the sequence specificities of DNA- and RNA-binding proteins by deep learning. Nat. Biotechnol. 33:8831–38
    [Google Scholar]
96. 96.

    Zhou J, Troyanskaya OG. 2015. Predicting effects of noncoding variants with deep learning–based sequence model. Nat. Methods 12:10931–34
    [Google Scholar]
97. 97.

    Kelley DR, Snoek J, Rinn JL. 2016. Basset: learning the regulatory code of the accessible genome with deep convolutional neural networks. Genome Res 26:7990–99
    [Google Scholar]
98. 98.

    Quang D, Xie X. 2016. DanQ: a hybrid convolutional and recurrent deep neural network for quantifying the function of DNA sequences. Nucleic Acids Res 44:11e107
    [Google Scholar]
99. 99.

    Kelley DR, Reshef YA, Bileschi M, Belanger D, McLean CY, Snoek J. 2018. Sequential regulatory activity prediction across chromosomes with convolutional neural networks. Genome Res 28:5739–50
    [Google Scholar]
100. 100.

     Jaganathan K, Kyriazopoulou Panagiotopoulou S, McRae JF, Darbandi SF, Knowles D et al. 2019. Predicting splicing from primary sequence with deep learning. Cell 176:3535–48.e24
     [Google Scholar]
101. 101.

     Avsec Ž, Weilert M, Shrikumar A, Krueger S, Alexandari A et al. 2021. Base-resolution models of transcription-factor binding reveal soft motif syntax. Nat. Genet. 53:3354–66
     [Google Scholar]
102. 102.

     Yang Y, Zhang R, Singh S, Ma J. 2017. Exploiting sequence-based features for predicting enhancer-promoter interactions. Bioinformatics 33:14i252–60
     [Google Scholar]
103. 103.

     Mao W, Kostka D, Chikina M. 2017. Modeling enhancer-promoter interactions with attention-based neural networks. bioRxiv 10.1101/219667. https://doi.org/10.1101/219667
     [Crossref]
104. 104.

     Singh S, Yang Y, Póczos B, Ma J. 2019. Predicting enhancer-promoter interaction from genomic sequence with deep neural networks. Quant. Biol. 7:2122–37
     [Google Scholar]
105. 105.

     Li W, Wong WH, Jiang R. 2019. DeepTACT: predicting 3D chromatin contacts via bootstrapping deep learning. Nucleic Acids Res 47:10e60
     [Google Scholar]
106. 106.

     Jing F, Zhang S-W, Zhang S. 2020. Prediction of enhancer-promoter interactions using the cross-cell type information and domain adversarial neural network. BMC Bioinform 21:507
     [Google Scholar]
107. 107.

     Trieu T, Martinez-Fundichely A, Khurana E. 2020. DeepMILO: a deep learning approach to predict the impact of non-coding sequence variants on 3D chromatin structure. Genome Biol 21:79
     [Google Scholar]
108. 108.

     Cao F, Zhang Y, Cai Y, Animesh S, Zhang Y et al. 2021. Chromatin Interaction Neural Network (ChINN): a machine learning-based method for predicting chromatin interactions from DNA sequences. Genome Biol. 22:226
     [Google Scholar]
109. 109.

     Schwessinger R, Gosden M, Downes D, Brown RC, Oudelaar AM et al. 2020. DeepC: predicting 3D genome folding using megabase-scale transfer learning. Nat. Methods 17:111118–24
     [Google Scholar]
110. 110.

     Fudenberg G, Kelley DR, Pollard KS. 2020. Predicting 3D genome folding from DNA sequence with Akita. Nat. Methods 17:111111–17
     [Google Scholar]
111. 111.

     Zhou J. 2021. Sequence-based modeling of genome 3D architecture from kilobase to chromosome-scale. bioRxiv 10.1101/2021.05.19.444847. https://doi.org/10.1101/2021.05.19.444847
     [Crossref]
112. 112.

     Laugsch M, Bartusel M, Rehimi R, Alirzayeva H, Karaolidou A et al. 2019. Modeling the pathological long-range regulatory effects of human structural variation with patient-specific hiPSCs. Cell Stem Cell 24:5736–52.e12
     [Google Scholar]
113. 113.

     Gröschel S, Sanders MA, Hoogenboezem R, de Wit E, Bouwman BAM et al. 2014. A single oncogenic enhancer rearrangement causes concomitant EVI1 and GATA2 deregulation in leukemia. Cell 157:2369–81
     [Google Scholar]
114. 114.

     Linder J, Bogard N, Rosenberg AB, Seelig G. 2019. Deep exploration networks for rapid engineering of functional DNA sequences. bioRxiv 864363. https://doi.org/10.1101/864363
     [Crossref]
115. 115.

     Brookes D, Park H, Listgarten J. 2019. Conditioning by adaptive sampling for robust design. arXiv:1901.10060 [cs.LG]
116. 116.

     Gupta A, Kundaje A. 2019. Targeted optimization of regulatory DNA sequences with neural editing architectures. bioRxiv 10.1101/714402. https://doi.org/10.1101/714402
     [Crossref]
117. 117.

     Schreiber J, Lu YY, Noble WS. 2020. Ledidi: designing genomic edits that induce functional activity Paper presented at Int. Conf. Mach. Learn. (ICML 2020) Virtual: Jul. 17
118. 118.

     Zrimec J, Fu X, Muhammad AS, Skrekas C, Jauniskis V et al. 2021. Supervised generative design of regulatory DNA for gene expression control. bioRxiv 10.1101/2021.07.15.452480. https://doi.org/10.1101/2021.07.15.452480
     [Crossref]