Advertisement

Computational Epigenomics and Its Application in Regulatory Genomics

  • Shalu Jhanwar
Chapter

Abstract

Over the years, with increasing knowledge of molecular mechanisms underlying the regulation of gene expression, the epigenetic landscape has undergone an evolution. In contrast to the early epigenetics that was originated exclusively in embryology and development, the modern epigenetics emphasizes on defining mechanisms of transmission of information that are not encoded in DNA. Epigenetic mechanisms such as DNA methylation and histone modifications introduce heritable changes in gene expression by regulating the wrapping of DNA inside the nucleus. The epigenetic machinery may result in either activation or repression, by acting upon euchromatin and dense heterochromatin part of chromatin, respectively. Epigenetics has provided novel understandings into the key mechanisms of development, cellular differentiation, and cell fate decisions. Moreover, recent studies have suggested their significant contribution in causing diseases such as cancer and neurodegenerative and autoimmune diseases. Recent overwhelming experimental and computational technological advancements have enabled us to resolve epigenome maps with increasing accuracy, comprehensiveness, and throughput manner across multiple cell types and tissues. This chapter provides a brief overview of the current technological advancements and resources available to perform epigenetic research with particular application in regulatory genomics.

Keywords

Enhancer Regulatory elements Epigenetics DNA methylation ChIP-seq ATAC-seq Chromatin conformation 

Notes

Acknowledgments

I thank Ajinkya Deogade and Sachin Pundhir for their useful comments while writing this book chapter. I appreciate La Caixa international Ph.D. scholarship program at the Centre for Genomic Regulation (CRG), Barcelona, Spain, for providing financial support.

References

  1. Akalin A, Kormaksson M, Li S et al (2012) MethylKit: a comprehensive R package for the analysis of genome-wide DNA methylation profiles. Genome Biol 13:R87CrossRefGoogle Scholar
  2. American Association for Cancer Research Human Epigenome Task Force, European Union, Network of Excellence, Scientific Advisory Board (2008) Moving AHEAD with an international human epigenome project. Nature 454:711–715.  https://doi.org/10.1038/454711a CrossRefGoogle Scholar
  3. Andersen MC, Engström PG, Lithwick S et al (2008) In Silico detection of sequence variations modifying transcriptional regulation. PLoS Comput Biol 4:e5.  https://doi.org/10.1371/journal.pcbi.0040005 CrossRefPubMedPubMedCentralGoogle Scholar
  4. Andersson R, Gebhard C, Miguel-Escalada I et al (2014) An atlas of active enhancers across human cell types and tissues. Nature 507:455–461.  https://doi.org/10.1038/nature12787 CrossRefPubMedPubMedCentralGoogle Scholar
  5. Andrey G, Montavon T, Mascrez B et al (2013) A switch between topological domains underlies HoxD genes collinearity in mouse limbsGoogle Scholar
  6. Aryee MJ, Jaffe AE, Corrada-Bravo H et al (2014) Minfi: a flexible and comprehensive Bioconductor package for the analysis of Infinium DNA methylation microarrays. Bioinformatics 30:1363–1369.  https://doi.org/10.1093/bioinformatics/btu049 CrossRefPubMedPubMedCentralGoogle Scholar
  7. Bailey TL, Boden M, Buske FA et al (2009) MEME SUITE: tools for motif discovery and searching. Nucleic Acids Res 37:W202–W208.  https://doi.org/10.1093/nar/gkp335 CrossRefPubMedPubMedCentralGoogle Scholar
  8. Bailey T, Krajewski P, Ladunga I et al (2013) Practical guidelines for the comprehensive analysis of ChIP-seq data. PLoS Comput Biol 9:e1003326.  https://doi.org/10.1371/journal.pcbi.1003326 CrossRefPubMedPubMedCentralGoogle Scholar
  9. Bernstein BE, Stamatoyannopoulos JA, Costello JF et al (2010) The NIH roadmap Epigenomics mapping consortium. Nat Biotechnol 28:1045–1048.  https://doi.org/10.1038/nbt1010-1045 CrossRefPubMedPubMedCentralGoogle Scholar
  10. Bock C (2012) Analysing and interpreting DNA methylation data. Nat Rev Genet 13:705–719CrossRefGoogle Scholar
  11. Boyle AP, Guinney J, Crawford GE, Furey TS (2008) F-Seq: a feature density estimator for high-throughput sequence tags. Bioinformatics 24:2537–2538.  https://doi.org/10.1093/bioinformatics/btn480 CrossRefPubMedPubMedCentralGoogle Scholar
  12. Boyle AP, Hong EL, Hariharan M et al (2012) Annotation of functional variation in personal genomes using RegulomeDB. Genome Res 22:1790–1797.  https://doi.org/10.1101/gr.137323.112 CrossRefPubMedPubMedCentralGoogle Scholar
  13. Buenrostro JD, Giresi PG, Zaba LC et al (2013) Transposition of native chromatin for fast and sensitive epigenomic profiling of open chromatin, DNA-binding proteins and nucleosome position. Nat Methods 10:1213–1218.  https://doi.org/10.1038/nmeth.2688 CrossRefPubMedPubMedCentralGoogle Scholar
  14. Choi JK (2013) “Open” chromatin: histone acetylation, linker histones & histone variants.  https://doi.org/10.14288/1.0165590
  15. Clark SJ, Lee HJ, Smallwood SA et al (2016) Single-cell epigenomics: powerful new methods for understanding gene regulation and cell identity. Genome Biol 17:72.  https://doi.org/10.1186/s13059-016-0944-x CrossRefPubMedPubMedCentralGoogle Scholar
  16. de Wit E, de Laat W (2012) A decade of 3C technologies: insights into nuclear organization. Genes Dev 26:11–24.  https://doi.org/10.1101/gad.179804.111 CrossRefPubMedPubMedCentralGoogle Scholar
  17. Degner JF, Pai AA, Pique-Regi R et al (2012) DNase I sensitivity QTLs are a major determinant of human expression variation. Nature 482:390–394.  https://doi.org/10.1038/nature10808 CrossRefPubMedPubMedCentralGoogle Scholar
  18. Dekker J, Marti-Renom MA, Mirny LA (2013) Exploring the three-dimensional organization of genomes: interpreting chromatin interaction data. Nat Rev Genet 14:390–403.  https://doi.org/10.1038/nrg3454 CrossRefPubMedPubMedCentralGoogle Scholar
  19. Dolzhenko E, Smith AD (2014) Using beta-binomial regression for high-precision differential methylation analysis in multifactor whole-genome bisulfite sequencing experiments. BMC Bioinf 15:215.  https://doi.org/10.1186/1471-2105-15-215 CrossRefGoogle Scholar
  20. Down TA, Rakyan VK, Turner DJ et al (2008) A Bayesian deconvolution strategy for immunoprecipitation-based DNA methylome analysis. Nat Biotechnol 26:779–785.  https://doi.org/10.1038/nbt1414 CrossRefPubMedPubMedCentralGoogle Scholar
  21. Dunham I, Kundaje A, Aldred SF et al (2012) An integrated encyclopedia of DNA elements in the human genome. Nature 489:57–74.  https://doi.org/10.1038/nature11247 CrossRefGoogle Scholar
  22. Ernst J, Kellis M (2012) ChromHMM: automating chromatin-state discovery and characterization. Nat Methods 9:215–216.  https://doi.org/10.1038/nmeth.1906 CrossRefPubMedPubMedCentralGoogle Scholar
  23. Erwin GD, Oksenberg N, Truty RM et al (2014) Integrating diverse datasets improves developmental enhancer prediction. PLoS Comput Biol 10:e1003677.  https://doi.org/10.1371/journal.pcbi.1003677 CrossRefPubMedPubMedCentralGoogle Scholar
  24. FANTOM Consortium and the RIKEN PMI and CLST (DGT), ARR F, Kawaji H et al (2014) A promoter-level mammalian expression atlas. Nature 507:462–470.  https://doi.org/10.1038/nature13182 CrossRefGoogle Scholar
  25. Felsenfeld G (2014) A brief history of epigenetics. Cold Spring Harb Perspect Biol.  https://doi.org/10.1101/cshperspect.a018200 CrossRefGoogle Scholar
  26. Feng H, Conneely KN, Wu H (2014) A Bayesian hierarchical model to detect differentially methylated loci from single nucleotide resolution sequencing data. Nucleic Acids Res 42:e69–e69.  https://doi.org/10.1093/nar/gku154 CrossRefPubMedPubMedCentralGoogle Scholar
  27. Fernández M, Miranda-Saavedra D (2012) Genome-wide enhancer prediction from epigenetic signatures using genetic algorithm-optimized support vector machines. Nucleic Acids Res 40:e77.  https://doi.org/10.1093/nar/gks149 CrossRefPubMedPubMedCentralGoogle Scholar
  28. Firpi HA, Ucar D, Tan K (2010) Discover regulatory DNA elements using chromatin signatures and artificial neural network. Bioinformatics 26:1579–1586.  https://doi.org/10.1093/bioinformatics/btq248 CrossRefPubMedPubMedCentralGoogle Scholar
  29. Fishilevich S, Nudel R, Rappaport N et al (2017) GeneHancer: genome-wide integration of enhancers and target genes in GeneCards. Database (Oxford).  https://doi.org/10.1093/database/bax028
  30. Forcato M, Nicoletti C, Pal K et al (2017) Comparison of computational methods for hi-C data analysis.  https://doi.org/10.1038/nmeth.4325 CrossRefGoogle Scholar
  31. Frommer M, McDonald LE, Millar DS et al (1992) A genomic sequencing protocol that yields a positive display of 5-methylcytosine residues in individual DNA strands. Proc Natl Acad Sci U S A 89:1827–1831.  https://doi.org/10.1073/PNAS.89.5.1827 CrossRefPubMedPubMedCentralGoogle Scholar
  32. Fu Y, Liu Z, Lou S et al (2014) FunSeq2: a framework for prioritizing noncoding regulatory variants in cancer. Genome Biol 15:480.  https://doi.org/10.1186/s13059-014-0480-5 CrossRefPubMedPubMedCentralGoogle Scholar
  33. Fujita PA, Rhead B, Zweig AS et al (2011) The UCSC genome browser database: update 2011. Nucleic Acids Res 39:D876–D882.  https://doi.org/10.1093/nar/gkq963 CrossRefPubMedGoogle Scholar
  34. Garraway LA, Lander ES (2013) Lessons from the Cancer genome. Cell 153:17–37.  https://doi.org/10.1016/J.CELL.2013.03.002 CrossRefPubMedGoogle Scholar
  35. Giresi PG, Kim J, McDaniell RM et al (2007) FAIRE (formaldehyde-assisted isolation of regulatory elements) isolates active regulatory elements from human chromatin. Genome Res 17:877–885.  https://doi.org/10.1101/gr.5533506 CrossRefPubMedPubMedCentralGoogle Scholar
  36. Hansen KD, Langmead B, Irizarry RA (2012) BSmooth: from whole genome bisulfite sequencing reads to differentially methylated regions. Genome Biol 13:R83CrossRefGoogle Scholar
  37. Hebestreit K, Dugas M, Klein H-U (2013) Detection of significantly differentially methylated regions in targeted bisulfite sequencing data. Bioinformatics 29:1647–1653.  https://doi.org/10.1093/bioinformatics/btt263 CrossRefPubMedGoogle Scholar
  38. Heintzman ND, Hon GC, Hawkins RD et al (2009) Histone modifications at human enhancers reflect global cell-type-specific gene expression. Nature 459:108–112.  https://doi.org/10.1038/nature07829 CrossRefPubMedPubMedCentralGoogle Scholar
  39. Heinz S, Benner C, Spann N et al (2010) Simple combinations of lineage-determining transcription factors prime cis-regulatory elements required for macrophage and B cell identities. Mol Cell 38:576–589.  https://doi.org/10.1016/j.molcel.2010.05.004 CrossRefPubMedPubMedCentralGoogle Scholar
  40. Henry VJ, Bandrowski AE, Pepin A-S et al (2014) OMICtools: an informative directory for multi-omic data analysis. Database (Oxford).  https://doi.org/10.1093/database/bau069 CrossRefGoogle Scholar
  41. Hindorff LA, Sethupathy P, Junkins HA et al (2009) Potential etiologic and functional implications of genome-wide association loci for human diseases and traits. Proc Natl Acad Sci U S A 106:9362–9367.  https://doi.org/10.1073/pnas.0903103106 CrossRefPubMedPubMedCentralGoogle Scholar
  42. Hoffman MM, Buske OJ, Wang J et al (2012) Unsupervised pattern discovery in human chromatin structure through genomic segmentation. Nat Methods 9:473–476.  https://doi.org/10.1038/nmeth.1937 CrossRefPubMedPubMedCentralGoogle Scholar
  43. Hotchkiss RD (1948) The quantitative separation of purines, pyrimidines, and nucleosides by paper chromatography. J Biol Chem 175:315–332PubMedGoogle Scholar
  44. Hudson TJ, Anderson W, Aretz A et al (2010) International network of cancer genome projects. Nature 464:993–998.  https://doi.org/10.1038/nature08987 CrossRefPubMedGoogle Scholar
  45. Human Genome Sequencing Consortium I (2004) Finishing the euchromatic sequence of the human genome. Nature 431:931–945.  https://doi.org/10.1038/nature03001 CrossRefGoogle Scholar
  46. Jaffe AE, Murakami P, Lee H et al (2012) Bump hunting to identify differentially methylated regions in epigenetic epidemiology studies. Int J Epidemiol 41:200–209.  https://doi.org/10.1093/ije/dyr238 CrossRefPubMedPubMedCentralGoogle Scholar
  47. Ji H, Jiang H, Ma W et al (2008) An integrated software system for analyzing ChIP-chip and ChIP-seq data. Nat Biotechnol 26:1293–1300.  https://doi.org/10.1038/nbt.1505 CrossRefPubMedPubMedCentralGoogle Scholar
  48. John S, Sabo PJ, Thurman RE et al (2011) Chromatin accessibility pre-determines glucocorticoid receptor binding patterns. Nat Genet 43:264–268.  https://doi.org/10.1038/ng.759 CrossRefPubMedGoogle Scholar
  49. Kharchenko PV, Tolstorukov MY, Park PJ (2008) Design and analysis of ChIP-seq experiments for DNA-binding proteins. Nat Biotechnol 26:1351–1359.  https://doi.org/10.1038/nbt.1508 CrossRefPubMedPubMedCentralGoogle Scholar
  50. Kircher M, Witten DM, Jain P et al (2014) A general framework for estimating the relative pathogenicity of human genetic variants. Nat Genet 46:310–315.  https://doi.org/10.1038/ng.2892 CrossRefPubMedPubMedCentralGoogle Scholar
  51. Kleftogiannis D, Kalnis P, Bajic VB (2015) DEEP: a general computational framework for predicting enhancers. Nucleic Acids Res 43:e6.  https://doi.org/10.1093/nar/gku1058 CrossRefPubMedGoogle Scholar
  52. Krueger F, Kreck B, Franke A, Andrews SR (2012) DNA methylome analysis using short bisulfite sequencing data. Nat Methods 9:145–151.  https://doi.org/10.1038/nmeth.1828 CrossRefPubMedGoogle Scholar
  53. Ku CS, Naidoo N, Wu M, Soong R (2011) Studying the epigenome using next generation sequencing. J Med Genet 48:721–730.  https://doi.org/10.1136/jmedgenet-2011-100242 CrossRefPubMedGoogle Scholar
  54. Landt SG, Marinov GK, Kundaje A et al (2012) ChIP-seq guidelines and practices of the ENCODE and modENCODE consortia. Genome Res 22:1813–1831.  https://doi.org/10.1101/gr.136184.111 CrossRefPubMedPubMedCentralGoogle Scholar
  55. Li H, Homer N (2010) A survey of sequence alignment algorithms for next-generation sequencing. Brief Bioinform 11:473–483.  https://doi.org/10.1093/bib/bbq015 CrossRefPubMedPubMedCentralGoogle Scholar
  56. Li H, Handsaker B, Wysoker A et al (2009) The sequence alignment/map format and SAMtools. Bioinformatics 25:2078–2079.  https://doi.org/10.1093/bioinformatics/btp352 CrossRefPubMedPubMedCentralGoogle Scholar
  57. Li Q, Brown JB, Huang H, Bickel PJ (2011) Measuring reproducibility of high-throughput experiments. Ann Appl Stat 5:1752–1779.  https://doi.org/10.1214/11-AOAS466 CrossRefGoogle Scholar
  58. Li MJ, Wang LY, Xia Z et al (2013) GWAS3D: detecting human regulatory variants by integrative analysis of genome-wide associations, chromosome interactions and histone modifications. Nucleic Acids Res 41:W150–W158.  https://doi.org/10.1093/nar/gkt456 CrossRefPubMedPubMedCentralGoogle Scholar
  59. Martens JHA, Stunnenberg HG (2013) BLUEPRINT: mapping human blood cell epigenomes. Haematologica 98:1487–1489.  https://doi.org/10.3324/haematol.2013.094243 CrossRefPubMedPubMedCentralGoogle Scholar
  60. Marx V (2012) READING THE SECOND GENOMIC CODEGoogle Scholar
  61. Maston GA, Evans SK, Green MR (2006) Transcriptional regulatory elements in the human genome. Annu Rev Genomics Hum Genet 7:29–59.  https://doi.org/10.1146/annurev.genom.7.080505.115623 CrossRefPubMedGoogle Scholar
  62. Maunakea AK, Nagarajan RP, Bilenky M et al (2010) Conserved role of intragenic DNA methylation in regulating alternative promoters. Nature 466:253–257.  https://doi.org/10.1038/nature09165 CrossRefPubMedPubMedCentralGoogle Scholar
  63. McLean CY, Bristor D, Hiller M et al (2010) GREAT improves functional interpretation of cis-regulatory regions. Nat Biotechnol 28:495–501.  https://doi.org/10.1038/nbt.1630 CrossRefPubMedPubMedCentralGoogle Scholar
  64. Montefiori L, Hernandez L, Zhang Z et al (2017) Reducing mitochondrial reads in ATAC-seq using CRISPR/Cas9. Sci Rep 7:2451.  https://doi.org/10.1038/s41598-017-02547-w CrossRefPubMedPubMedCentralGoogle Scholar
  65. Park Y, Figueroa ME, Rozek LS, Sartor MA (2014) MethylSig: a whole genome DNA methylation analysis pipeline. Bioinformatics 30:2414–2422.  https://doi.org/10.1093/bioinformatics/btu339 CrossRefPubMedPubMedCentralGoogle Scholar
  66. Pique-Regi R, Degner JF, Pai AA et al (2011) Accurate inference of transcription factor binding from DNA sequence and chromatin accessibility data. Genome Res 21:447–455.  https://doi.org/10.1101/gr.112623.110 CrossRefPubMedPubMedCentralGoogle Scholar
  67. Plank JL, Dean A (2014) Enhancer function: mechanistic and genome-wide insights come together. Mol Cell 55:5–14.  https://doi.org/10.1016/j.molcel.2014.06.015 CrossRefPubMedGoogle Scholar
  68. Rajagopal N, Xie W, Li Y et al (2013) RFECS: a random-forest based algorithm for enhancer identification from chromatin state. PLoS Comput Biol 9:e1002968.  https://doi.org/10.1371/journal.pcbi.1002968 CrossRefPubMedPubMedCentralGoogle Scholar
  69. Rashid NU, Giresi PG, Ibrahim JG et al (2011) ZINBA integrates local covariates with DNA-seq data to identify broad and narrow regions of enrichment, even within amplified genomic regions. Genome Biol 12:R67.  https://doi.org/10.1186/gb-2011-12-7-r67 CrossRefPubMedPubMedCentralGoogle Scholar
  70. Risca VI, Greenleaf WJ (2015) Unraveling the 3D genome: genomics tools for multiscale exploration. Trends Genet 31:357–372.  https://doi.org/10.1016/j.tig.2015.03.010 CrossRefPubMedPubMedCentralGoogle Scholar
  71. Ritchie GRS, Dunham I, Zeggini E, Flicek P (2014) Functional annotation of noncoding sequence variants. Nat Methods 11:294–296.  https://doi.org/10.1038/nmeth.2832 CrossRefPubMedPubMedCentralGoogle Scholar
  72. Rivera CM, Ren B (2013) Mapping human epigenomes. Cell 155:39–55.  https://doi.org/10.1016/j.cell.2013.09.011 CrossRefPubMedGoogle Scholar
  73. Robertson KD (2005) DNA methylation and human disease. Nat Rev Genet 6:597–610.  https://doi.org/10.1038/nrg1655 CrossRefPubMedGoogle Scholar
  74. Robinson MD, Strbenac D, Stirzaker C et al (2012) Copy-number-aware differential analysis of quantitative DNA sequencing data. Genome Res 22:2489–2496.  https://doi.org/10.1101/gr.139055.112 CrossRefPubMedPubMedCentralGoogle Scholar
  75. Robinson MD, Kahraman A, Law CW et al (2014) Statistical methods for detecting differentially methylated loci and regions. Front Genet 5:324.  https://doi.org/10.3389/fgene.2014.00324 CrossRefPubMedPubMedCentralGoogle Scholar
  76. Ross-Innes CS, Stark R, Teschendorff AE et al (2012) Differential oestrogen receptor binding is associated with clinical outcome in breast cancer. Nature 481:389–393.  https://doi.org/10.1038/nature10730 CrossRefPubMedPubMedCentralGoogle Scholar
  77. Schep A, Buenrostro JD, Denny SK et al (2015) Structured nucleosome fingerprints enable high-resolution mapping of chromatin architecture within regulatory regions. bioR xiv 16642. doi:  https://doi.org/10.1101/016642
  78. Schones DE, Cui K, Cuddapah S et al (2008) Dynamic regulation of nucleosome positioning in the human genome. Cell 132:887–898.  https://doi.org/10.1016/j.cell.2008.02.022 CrossRefPubMedGoogle Scholar
  79. Sheffield NC, Furey TS (2012) Identifying and characterizing regulatory sequences in the human genome with chromatin accessibility assays. Genes (Basel) 3:651–670.  https://doi.org/10.3390/genes3040651 CrossRefGoogle Scholar
  80. Sherwood RI, Hashimoto T, O’Donnell CW et al (2014) Discovery of directional and nondirectional pioneer transcription factors by modeling DNase profile magnitude and shape. Nat Biotechnol 32:171–178.  https://doi.org/10.1038/nbt.2798 CrossRefPubMedPubMedCentralGoogle Scholar
  81. Shlyueva D, Stampfel G, Stark A (2014) Transcriptional enhancers: from properties to genome-wide predictions. Nat Rev Genet 15:272–286.  https://doi.org/10.1038/nrg3682 CrossRefPubMedGoogle Scholar
  82. Song L, Crawford GE (2010) DNase-seq: a high-resolution technique for mapping active gene regulatory elements across the genome from mammalian cells. Cold Spring Harb Protoc 2010:pdb.prot5384.  https://doi.org/10.1101/pdb.prot5384 CrossRefGoogle Scholar
  83. Strahl BD, Allis CD (2000) The language of covalent histone modifications. Nature 403:41–45.  https://doi.org/10.1038/47412 CrossRefPubMedGoogle Scholar
  84. Stockwell PA, Chatterjee A, Rodger EJ, Morison IM (2014) DMAP: differential methylation analysis package for RRBS and WGBS data. Bioinformatics 30:1814–1822.  https://doi.org/10.1093/bioinformatics/btu126 CrossRefPubMedGoogle Scholar
  85. Sun D, Xi Y, Rodriguez B et al (2014) MOABS: model based analysis of bisulfite sequencing data. Genome Biol 15:R38.  https://doi.org/10.1186/gb-2014-15-2-r38 CrossRefPubMedPubMedCentralGoogle Scholar
  86. Tan M, Luo H, Lee S et al (2011) Identification of 67 histone marks and histone lysine crotonylation as a new type of histone modification. Cell 146:1016–1028.  https://doi.org/10.1016/j.cell.2011.08.008 CrossRefPubMedPubMedCentralGoogle Scholar
  87. Teng M, Ichikawa S, Padgett LR et al (2012) regSNPs: a strategy for prioritizing regulatory single nucleotide substitutions. Bioinformatics 28:1879–1886.  https://doi.org/10.1093/bioinformatics/bts275 CrossRefPubMedPubMedCentralGoogle Scholar
  88. Thorvaldsdóttir H, Robinson JT, Mesirov JP (2013) Integrative genomics viewer (IGV): high-performance genomics data visualization and exploration. Brief Bioinform 14:178–192.  https://doi.org/10.1093/bib/bbs017 CrossRefPubMedGoogle Scholar
  89. Tsompana M, Buck MJ (2014) Chromatin accessibility: a window into the genome. Epigenetics Chromatin 7:33.  https://doi.org/10.1186/1756-8935-7-33 CrossRefPubMedPubMedCentralGoogle Scholar
  90. Waddington CH (2012) The epigenotype. 1942. Int J Epidemiol 41:10–13.  https://doi.org/10.1093/ije/dyr184 CrossRefPubMedGoogle Scholar
  91. Wang Q, Carroll JS, Brown M (2005) Spatial and temporal recruitment of androgen receptor and its coactivators involves chromosomal looping and polymerase tracking. Mol Cell 19:631–642.  https://doi.org/10.1016/j.molcel.2005.07.018 CrossRefPubMedGoogle Scholar
  92. Wang D, Yan L, Hu Q et al (2012) IMA: an R package for high-throughput analysis of Illumina’s 450K Infinium methylation data. Bioinformatics 28:729–730.  https://doi.org/10.1093/bioinformatics/bts013 CrossRefPubMedPubMedCentralGoogle Scholar
  93. Warden CD, Lee H, Tompkins JD et al (2013) COHCAP: an integrative genomic pipeline for single-nucleotide resolution DNA methylation analysis. Nucleic Acids Res 41:e117–e117.  https://doi.org/10.1093/nar/gkt242 CrossRefPubMedPubMedCentralGoogle Scholar
  94. Weingarten-Gabbay S, Segal E (2014) A shared architecture for promoters and enhancers. Nat Genet 46:1253–1254.  https://doi.org/10.1038/ng.3152 CrossRefPubMedGoogle Scholar
  95. Whitaker JW, Nguyen TT, Zhu Y et al (2015) Computational schemes for the prediction and annotation of enhancers from epigenomic assays. Methods 72:86–94.  https://doi.org/10.1016/j.ymeth.2014.10.008 CrossRefPubMedGoogle Scholar
  96. Wu C, Morris JR (2001) Genes, genetics, and epigenetics: a correspondence. Science 293:1103–1105.  https://doi.org/10.1126/science.293.5532.1103 CrossRefGoogle Scholar
  97. Won K-J, Zhang X, Wang T et al (2013) Comparative annotation of functional regions in the human genome using epigenomic data. Nucleic Acids Res 41:4423–4432.  https://doi.org/10.1093/nar/gkt143 CrossRefPubMedPubMedCentralGoogle Scholar
  98. Xu F, Zhang K, Grunstein M et al (2005) Acetylation in histone H3 globular domain regulates gene expression in yeast. Cell 121:375–385.  https://doi.org/10.1016/j.cell.2005.03.011 CrossRefPubMedGoogle Scholar
  99. Zhang HZH, Fiume E, Ms OC (2002) shape matching of 3D contours using normalized Fourier descriptors. Proc SMI Shape Model Int 2002:3–6.  https://doi.org/10.1109/SMI.2002.1003554
  100. Zhang Y, Liu T, Meyer CA et al (2008) Model-based analysis of ChIP-Seq (MACS). Genome Biol 9:R137.  https://doi.org/10.1186/gb-2008-9-9-r137 CrossRefPubMedPubMedCentralGoogle Scholar
  101. Zhang B, Zhou Y, Lin N et al (2013) Functional DNA methylation differences between tissues, cell types, and across individuals discovered using the M&M algorithm. Genome Res 23:1522–1540.  https://doi.org/10.1101/gr.156539.113 CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  1. 1.Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and TechnologyUniversitat Pompeu Fabra (UPF)BarcelonaSpain
  2. 2.Developmental Genetics, Department of BiomedicineUniversity of BaselBaselSwitzerland

Personalised recommendations