Skip to main content
SpringerLink
Log in

Computational Epigenetics in Rice Research

  • Chapter
  • First Online:
Applications of Bioinformatics in Rice Research

  • 462 Accesses

Abstract

The epigenetic study is now most widely used in plant research to identify the mutation as well as modifications present at the genome level. Modern advanced technologies enable genome-wide DNA and histone modifications to be assessed, which in turn help us to understand how they regulate the functions, and this might also be an aid in classifying plant regulation mechanisms on a higher level than the nucleotide sequence. Rice is an important food grain and is an ideal example for monocotyledons because it has a smaller genome size and a completely sequenced, well-annotated genome. Computational methods and bioinformatics tools are used in epigenomics studies, particularly during experimental design, data analysis, hypothesis confirmation, and results interpretation, due to the large quantity of data produced by high-throughput sequencing. High-throughput sequencing approaches are widely used to identify such modifications in rice that have an effect on growth, development as well as biotic and abiotic stress in the genome region. DNA methylation is regulated in rice by several regulators that are involved in both adaptation and agronomic efficiency. This, in turn, indicates that a detailed study of epigenetic regulators in rice may benefit development. Considering the above information in the present chapter, the author made an attempt to discuss various epigenetic modifications that occur in rice plant and their mode of expression function in a different area.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 149.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 199.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 199.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Abbreviations

5hmC:

5-hydroxymethylcytosine

5mC:

5-methylcytosine

ANN:

Artificial neural networks

APR:

Adult plant resistance

CGIs:

CpG islands

ChIP-Seq:

Chromatin immunoprecipitation with high-throughput sequencing

DML:

Differentially methylated loci

DNMTs:

DNA methyltransferase enzymes

DRD1:

RNA-directed DNA methylation 1

HKMT:

Histone lysine methyltransferases

KTF1:

KOW domain transcription factor 1

MBD-seq:

Methyl CpG binding domain coupled with high-throughput sequencing

MeDIP:

Methylated DNA immunoprecipitation

MeDIP-seq:

Methylated DNA immunoprecipitation sequencing

MSAP:

Methylation-sensitive amplified polymorphism

NGS:

Next generation sequencing

NLR:

Nucleotide-binding leucine-rich repeat

PRMT:

Protein arginine methyltransferases

PTM:

Posttranslational modifications

RdDM:

RNA-directed DNA methylation

RRBS:

Reduced representation bisulfite sequencing

SAH:

S-adenosyl-homocysteine

SAM:

S-adenosyl-methionine

SVM:

Support vector machines

WGBS:

Whole-genome bisulfite sequencing

References

  1. Mirouze M, Paszkowski J. Epigenetic contribution to stress adaptation in plants. Curr Opin Plant Biol. 2011;14:267–74.

    Article  CAS  PubMed  Google Scholar 

  2. Alberts B, Johnson A, Lewis J, Raff M, Roberts K, Walter P. Molecular biology of the cell. 4th ed. New York: Garland Science; 2002.

    Google Scholar 

  3. Vaillant I, Paszkowski J. Role of histone and DNA methylation in gene regulation. Curr Opin Plant Biol. 2007;10:528–33.

    Article  CAS  PubMed  Google Scholar 

  4. Meyer P. Epigenetic variation and environmental change. J Exp Bot. 2015;66:3541–8.

    Article  CAS  PubMed  Google Scholar 

  5. Chen X, Zhou D-X. Rice epigenomics and epigenetics: challenges and opportunities. Curr Opin Plant Biol. 2013;16:164–9.

    Article  CAS  PubMed  Google Scholar 

  6. Yan H, Kikuchi S, Neumann P, Zhang W, Wu Y, Chen F, et al. Genome-wide mapping of cytosine methylation revealed dynamic DNA methylation patterns associated with genes and centromeres in rice. Plant J. 2010;63:353–65.

    Article  CAS  PubMed  Google Scholar 

  7. Zemach A, Kim MY, Silva P, Rodrigues JA, Dotson B, Brooks MD, et al. Local DNA hypomethylation activates genes in rice endosperm. Proc Natl Acad Sci U SA. 2010;107:18729–34.

    Article  CAS  Google Scholar 

  8. Chodavarapu RK, Feng S, Ding B, Simon SA, Lopez D, Jia Y, et al. Transcriptome and methylome interactions in rice hybrids. Proc Natl Acad Sci U S A. 2012;109:12040–5.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Li X, Zhu J, Hu F, Ge S, Ye M, Xiang H, et al. Single-base resolution maps of cultivated and wild rice methylomes and regulatory roles of DNA methylation in plant gene expression. BMC Genomics. 2012;13:300.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. He G, Zhu X, Elling AA, Chen L, Wang X, Guo L, et al. Global epigenetic and transcriptional trends among two rice subspecies and their reciprocal hybrids. Plant Cell. 2010;22:17–33.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Malone BM, Tan F, Bridges SM, Peng Z. Comparison of four ChIP-Seq analytical algorithms using rice endosperm H3K27 trimethylation profiling data. PLoS One. 2011;6:e25260.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Du Z, Li H, Wei Q, Zhao X, Wang C, Zhu Q, et al. Genome-wide analysis of histone modifications: H3K4me2, H3K4me3, H3K9ac, and H3K27ac in Oryza sativa L. Japonica Mol Plant. 2013;6:1463–72.

    Article  CAS  PubMed  Google Scholar 

  13. Waddington CH. The epigenotype. Endeavour. 1942;1:18–20.

    Google Scholar 

  14. Riggs A. Epigenetic mechanisms of gene regulation. Cold Spring Harbor, NY: Cold Spring Harbor Laboratory; 1996.

    Google Scholar 

  15. Holliday R, Ho T. DNA methylation and epigenetic inheritance. Methods San Diego Calif. 2002;27:179–83.

    Article  CAS  Google Scholar 

  16. Reik W, Dean W, Walter J. Epigenetic reprogramming in mammalian development. Science. 2001;293:1089–93.

    Article  CAS  PubMed  Google Scholar 

  17. Jablonka E, Lamb MJ. The inheritance of acquired epigenetic variations. J Theor Biol. 1989;139:69–83.

    Article  CAS  PubMed  Google Scholar 

  18. Durrant A, Nicholas DB. An unstable gene in flax. Heredity. 1970;25:513–27.

    Article  Google Scholar 

  19. Cullis CA, Kolodynska K. Variation in the isozymes of flax (Linum usitatissimum) genotrophs. Biochem Genet. 1975;13:687–97.

    Article  CAS  PubMed  Google Scholar 

  20. Hill J. Environmental induction of heritable changes in Nicotiana rustica. Nature. 1965;207:732–4.

    Article  Google Scholar 

  21. Deng X, Song X, Wei L, Liu C, Cao X. Epigenetic regulation and epigenomic landscape in rice. Natl Sci Rev. 2016;3:309–27.

    Article  CAS  Google Scholar 

  22. Finnegan EJ, Genger RK, Peacock WJ, Dennis ES. Dna methylation in plants. Annu Rev Plant Physiol Plant Mol Biol. 1998;49:223–47.

    Article  CAS  PubMed  Google Scholar 

  23. Jin B, Li Y, Robertson KD. DNA methylation. Genes Cancer. 2011;2:607–17.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Moore LD, Le T, Fan G. DNA methylation and its basic function. Neuropsychopharmacology. 2013;38:23–38.

    Article  CAS  PubMed  Google Scholar 

  25. Du J, Johnson LM, Jacobsen SE, Patel DJ. DNA methylation pathways and their crosstalk with histone methylation. Nat Rev Mol Cell Biol. 2015;16:519–32.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Mazin AL, Vaniushin BF. Possible origin and evolution of enzymatic methylation of eukaryotic DNA. Methylation of cytosine residues in 3 palindromic families: RYRY, YYRR, and YYRYRR. Mol Biol (Mosk). 1990;24:23–43.

    CAS  Google Scholar 

  27. El-Osta A, Wolffe AP. DNA methylation and histone deacetylation in the control of gene expression: basic biochemistry to human development and disease. Gene Expr. 2000;9:63–75.

    Article  CAS  PubMed  Google Scholar 

  28. Kwok JB. Role of epigenetics in Alzheimer’s and Parkinson’s disease. Epigenomics. 2010;2:671–82.

    Article  CAS  PubMed  Google Scholar 

  29. Fraga MF, Ballestar E, Paz MF, Ropero S, Setien F, Ballestar ML, et al. Epigenetic differences arise during the lifetime of monozygotic twins. Proc Natl Acad Sci U S A. 2005;102:10604–9.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Esteller M, Fraga MF, Paz MF, Campo E, Colomer D, Novo FJ, et al. Cancer epigenetics and methylation. Science. 2002;297:1807–9.

    Article  PubMed  Google Scholar 

  31. Oda M, Yamagiwa A, Yamamoto S, Nakayama T, Tsumura A, Sasaki H, et al. DNA methylation regulates long-range gene silencing of an X-linked homeobox gene cluster in a lineage-specific manner. Genes Dev. 2006;20:3382–94.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Jirtle RL, Skinner MK. Environmental epigenomics and disease susceptibility. Nat Rev Genet. 2007;8:253–62.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Kangaspeska S, Stride B, Métivier R, Polycarpou-Schwarz M, Ibberson D, Carmouche RP, et al. Transient cyclical methylation of promoter DNA. Nature. 2008;452:112–5.

    Article  CAS  PubMed  Google Scholar 

  34. Métivier R, Gallais R, Tiffoche C, Le Péron C, Jurkowska RZ, Carmouche RP, et al. Cyclical DNA methylation of a transcriptionally active promoter. Nature. 2008;452:45–50.

    Article  PubMed  CAS  Google Scholar 

  35. Bird AP. DNA methylation versus gene expression. Development. 1984;83:31–40.

    Article  CAS  Google Scholar 

  36. Medvedeva YA, Fridman MV, Oparina NJ, Malko DB, Ermakova EO, Kulakovskiy IV, et al. Intergenic, gene terminal, and intragenic CpG islands in the human genome. BMC Genomics. 2010;11:48.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  37. Pardo LM, Rizzu P, Francescatto M, Vitezic M, Leday GGR, Sanchez JS, et al. Regional differences in gene expression and promoter usage in aged human brains. Neurobiol Aging. 2013;34:1825–36.

    Article  CAS  PubMed  Google Scholar 

  38. Rishi V, Bhattacharya P, Chatterjee R, Rozenberg J, Zhao J, Glass K, et al. CpG methylation of half-CRE sequences creates C/EBPα binding sites that activate some tissue-specific genes. Proc Natl Acad Sci U S A. 2010;107:20311–6.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Cokus SJ, Feng S, Zhang X, Chen Z, Merriman B, Haudenschild CD, et al. Shotgun bisulphite sequencing of the Arabidopsis genome reveals DNA methylation patterning. Nature. 2008;452:215–9.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Corbin KR, Lopez CMR. Library preparation for whole genome bisulfite sequencing of plant genomes. DNA Methylation Mech [Internet]. IntechOpen; 2020 [cited 2020 Sep 26]. Available from: https://www.intechopen.com/books/dna-methylation-mechanism/library-preparation-for-whole-genome-bisulfite-sequencing-of-plant-genomes.

  41. Warnecke PM, Stirzaker C, Melki JR, Millar DS, Paul CL, Clark SJ. Detection and measurement of PCR bias in quantitative methylation analysis of bisulphite-treated DNA. Nucleic Acids Res. 1997;25:4422–6.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Kawakatsu T. Whole-genome bisulfite sequencing and epigenetic variation in cereal methylomes. In: Vaschetto LM, editor. Cereal genomics methods protoc [Internet]. New York, NY: Springer US; 2020 [cited 2020 Sep 20]. p. 119–28. Available from: https://doi.org/10.1007/978-1-4939-9865-4_10.

  43. Ji L, Sasaki T, Sun X, Ma P, Lewis ZA, Schmitz RJ. Methylated DNA is over-represented in whole-genome bisulfite sequencing data. Front Genet [Internet]. Frontiers; 2014 [cited 2020 Sep 20];5. Available from: https://www.frontiersin.org/articles/10.3389/fgene.2014.00341/full.

  44. Mehmet K, Ayse GI. Primer pairs for rice (Oryza sativa L.) bisulfite sequencing studies. J Plant Sci Phytopathol. 2018;2:91–8.

    Article  Google Scholar 

  45. Li Q, Hermanson PJ, Springer NM. Detection of DNA methylation by whole-genome bisulfite sequencing. Methods Mol Biol. 2018;1676:185–96.

    Article  CAS  PubMed  Google Scholar 

  46. Liu H, Wu Y, Cao A, Mao B, Zhao B, Wang J. Genome-wide analysis of DNA methylation during ovule development of female-sterile rice fsv1. G3 Genes Genomes Genet. 2017;7:3621–35.

    CAS  Google Scholar 

  47. Wang Y, Lin H, Tong X, Hou Y, Chang Y, Zhang J. DNA demethylation activates genes in seed maternal integument development in rice (Oryza sativa L.). Plant Physiol Biochem. 2017;120:169–78.

    Article  CAS  PubMed  Google Scholar 

  48. Li N, Xu C, Zhang A, Lv R, Meng X, Lin X, et al. DNA methylation repatterning accompanying hybridization, whole genome doubling and homoeolog exchange in nascent segmental rice allotetraploids. New Phytol. 2019;223:979–92.

    Article  CAS  PubMed  Google Scholar 

  49. Wang W, Qin Q, Sun F, Wang Y, Xu D, Li Z, et al. Genome-wide differences in DNA methylation changes in two contrasting rice genotypes in response to drought conditions. Front Plant Sci [Internet]. Frontiers; 2016 [cited 2020 Sep 21];7. Available from: https://www.frontiersin.org/articles/10.3389/fpls.2016.01675/full.

  50. Sun Y, Fan M, He Y. DNA methylation analysis of the Citrullus lanatus response to cucumber green mottle mosaic virus infection by whole-genome bisulfite sequencing. Genes. 2019;10:344.

    Article  CAS  PubMed Central  Google Scholar 

  51. Li R, Hu F, Li B, Zhang Y, Chen M, Fan T, et al. Whole genome bisulfite sequencing methylome analysis of mulberry (Morus alba) reveals epigenome modifications in response to drought stress. Sci Rep. 2020;10:8013.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Bernstein AI, Peng J. Epigenetic technological applications [Internet]. Elsevier; 2015 [cited 2020 Sep 26]. Available from: https://linkinghub.elsevier.com/retrieve/pii/C2013016062X.

  53. Bossdorf O, Richards CL, Pigliucci M. Epigenetics for ecologists. Ecol Lett. 2008;11:106–15.

    PubMed  Google Scholar 

  54. Gu H, Smith ZD, Bock C, Boyle P, Gnirke A, Meissner A. Preparation of reduced representation bisulfite sequencing libraries for genome-scale DNA methylation profiling. Nat Protoc. 2011;6:468–81.

    Article  CAS  PubMed  Google Scholar 

  55. Schmidt M, Van Bel M, Woloszynska M, Slabbinck B, Martens C, De Block M, et al. Plant-RRBS, a bisulfite and next-generation sequencing-based methylome profiling method enriching for coverage of cytosine positions. BMC Plant Biol. 2017;17:115.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  56. Malinowska M, Nagy I, Wagemaker CAM, Ruud AK, Svane SF, Thorup-Kristensen K, et al. The cytosine methylation landscape of spring barley revealed by a new reduced representation bisulfite sequencing pipeline, WellMeth. Plant Genome. 2020;13:e20049.

    Article  CAS  PubMed  Google Scholar 

  57. Li D, Zhang B, Xing X, Wang T. Combining MeDIP-seq and MRE-seq to investigate genome-wide CpG methylation. Methods. 2015;72:29–40.

    Article  CAS  PubMed  Google Scholar 

  58. Li N, Ye M, Li Y, Yan Z, Butcher LM, Sun J, et al. Whole genome DNA methylation analysis based on high throughput sequencing technology. Methods. 2010;52:203–12.

    Article  PubMed  CAS  Google Scholar 

  59. Taiwo O, Wilson GA, Morris T, Seisenberger S, Reik W, Pearce D, et al. Methylome analysis using MeDIP-seq with low DNA concentrations. Nat Protoc. 2012;7:617–36.

    Article  CAS  PubMed  Google Scholar 

  60. Xiong LZ, Xu CG, Saghai Maroof MA, Zhang Q. Patterns of cytosine methylation in an elite rice hybrid and its parental lines, detected by a methylation-sensitive amplification polymorphism technique. Mol Gen Genet MGG. 1999;261:439–46.

    Article  CAS  PubMed  Google Scholar 

  61. Lu YC, Feng SJ, Zhang JJ, Luo F, Zhang S, Yang H. Genome-wide identification of DNA methylation provides insights into the association of gene expression in rice exposed to pesticide atrazine. Sci Rep. 2016;6:18985.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  62. Xing M-Q, Zhang Y-J, Zhou S-R, Hu W-Y, Wu X-T, Ye Y-J, et al. Global analysis reveals the crucial roles of DNA methylation during rice seed development. Plant Physiol. 2015;168:1417–32.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  63. Alokail MS, Alenad AM. DNA methylation. Concise Rev Mol Pathol Breast Cancer [Internet]. IntechOpen; 2015 [cited 2021 Mar 14]. Available from: https://www.intechopen.com/books/a-concise-review-of-molecular-pathology-of-breast-cancer/dna-methylation.

  64. Hu J, Chen X, Zhang H, Ding Y. Genome-wide analysis of DNA methylation in photoperiod- and thermo-sensitive male sterile rice Peiai 64S. BMC Genomics. 2015;16:102.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  65. Roudier F, Ahmed I, Bérard C, Sarazin A, Mary-Huard T, Cortijo S, et al. Integrative epigenomic mapping defines four main chromatin states in Arabidopsis. EMBO J. 2011;30:1928–38.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  66. He G, Elling AA, Deng XW. The epigenome and plant development. Annu Rev Plant Biol. 2011;62:411–35.

    Article  CAS  PubMed  Google Scholar 

  67. Lauria M, Rossi V. Epigenetic control of gene regulation in plants. Biochim Biophys Acta. 2011;1809:369–78.

    Article  CAS  PubMed  Google Scholar 

  68. Fuchs J, Demidov D, Houben A, Schubert I. Chromosomal histone modification patterns—from conservation to diversity. Trends Plant Sci. 2006;11:199–208.

    Article  CAS  PubMed  Google Scholar 

  69. Shahbazian MD, Grunstein M. Functions of site-specific histone acetylation and deacetylation. Annu Rev Biochem. 2007;76:75–100.

    Article  CAS  PubMed  Google Scholar 

  70. Tamaru H, Selker EU. A histone H3 methyltransferase controls DNA methylation in Neurospora crassa. Nature. 2001;414:277–83.

    Article  CAS  PubMed  Google Scholar 

  71. Kanno T, Bucher E, Daxinger L, Huettel B, Kreil DP, Breinig F, et al. RNA-directed DNA methylation and plant development require an IWR1-type transcription factor. EMBO Rep. 2010;11:65–71.

    Article  CAS  PubMed  Google Scholar 

  72. He X-J, Hsu Y-F, Zhu S, Wierzbicki AT, Pontes O, Pikaard CS, et al. An effector of RNA-directed DNA methylation in Arabidopsis is an ARGONAUTE 4- and RNA-binding protein. Cell. 2009;137:498–508.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  73. Naumann U, Daxinger L, Kanno T, Eun C, Long Q, Lorkovic ZJ, et al. Genetic evidence that DNA methyltransferase DRM2 has a direct catalytic role in RNA-directed DNA methylation in Arabidopsis thaliana. Genetics. 2011;187:977–9.

    Article  PubMed  PubMed Central  Google Scholar 

  74. Tang N, Ma S, Zong W, Yang N, Lv Y, Yan C, et al. MODD mediates deactivation and degradation of OsbZIP46 to negatively regulate ABA signaling and drought resistance in rice. Plant Cell. 2016;28:2161–77.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  75. Sofer T, Schifano ED, Hoppin JA, Hou L, Baccarelli AA. A-clustering: a novel method for the detection of co-regulated methylation regions, and regions associated with exposure. Bioinformatics. 2013;29:2884–91.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  76. Akalin A, Kormaksson M, Li S, Garrett-Bakelman FE, Figueroa ME, Melnick A, et al. methylKit: a comprehensive R package for the analysis of genome-wide DNA methylation profiles. Genome Biol. 2012;13:R87.

    Article  PubMed  PubMed Central  Google Scholar 

  77. Lienhard M, Grimm C, Morkel M, Herwig R, Chavez L. MEDIPS: genome-wide differential coverage analysis of sequencing data derived from DNA enrichment experiments. Bioinformatics. 2014;30:284–6.

    Article  CAS  PubMed  Google Scholar 

  78. Ramírez F, Ryan DP, Grüning B, Bhardwaj V, Kilpert F, Richter AS, et al. deepTools2: a next generation web server for deep-sequencing data analysis. Nucleic Acids Res. 2016;44:W160–5.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  79. Love MI, Huber W, Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 2014;15:550.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  80. Aryee MJ, Jaffe AE, Corrada-Bravo H, Ladd-Acosta C, Feinberg AP, Hansen KD, et al. Minfi: a flexible and comprehensive Bioconductor package for the analysis of Infinium DNA methylation microarrays. Bioinformatics. 2014;30:1363–9.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  81. Peters TJ, Buckley MJ, Statham AL, Pidsley R, Samaras K, Lord RV, et al. De novo identification of differentially methylated regions in the human genome. Epigenet Chromatin. 2015;8:6.

    Article  CAS  Google Scholar 

  82. Morris TJ, Butcher LM, Feber A, Teschendorff AE, Chakravarthy AR, Wojdacz TK, et al. ChAMP: 450k chip analysis methylation pipeline. Bioinformatics. 2014;30:428–30.

    Article  CAS  PubMed  Google Scholar 

  83. Ashoor H, Louis-Brennetot C, Janoueix-Lerosey I, Bajic VB, Boeva V. HMCan-diff: a method to detect changes in histone modifications in cells with different genetic characteristics. Nucleic Acids Res. 2017;45:e58.

    PubMed  PubMed Central  Google Scholar 

  84. Boeva V, Louis-Brennetot C, Peltier A, Durand S, Pierre-Eugène C, Raynal V, et al. Heterogeneity of neuroblastoma cell identity defined by transcriptional circuitries. Nat Genet. 2017;49:1408–13.

    Article  CAS  PubMed  Google Scholar 

  85. Li H, Durbin R. Fast and accurate long-read alignment with Burrows-Wheeler transform. Bioinformatics. 2010;26:589–95.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  86. Wang L, Huang H, Dougherty G, Zhao Y, Hossain A, Kocher J-PA. Epidaurus: aggregation and integration analysis of prostate cancer epigenome. Nucleic Acids Res. 2015;43:e7.

    Article  PubMed  CAS  Google Scholar 

  87. Silva TC, Coetzee SG, Gull N, Yao L, Hazelett DJ, Noushmehr H, et al. ELMER v.2: an R/Bioconductor package to reconstruct gene regulatory networks from DNA methylation and transcriptome profiles. Bioinformatics. 2019;35:1974–7.

    Article  CAS  PubMed  Google Scholar 

  88. Bock C, Reither S, Mikeska T, Paulsen M, Walter J, Lengauer T. BiQ analyzer: visualization and quality control for DNA methylation data from bisulfite sequencing. Bioinformatics. 2005;21:4067–8.

    Article  CAS  PubMed  Google Scholar 

  89. Li D, Hsu S, Purushotham D, Sears RL, Wang T. WashU epigenome browser update 2019. Nucleic Acids Res. 2019;47:W158–65.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  90. Mi H, Muruganujan A, Huang X, Ebert D, Mills C, Guo X, et al. Protocol update for large-scale genome and gene function analysis with PANTHER classification system (v.14.0). Nat Protoc. 2019;14:703–21.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  91. Sun D, Xi Y, Rodriguez B, Park HJ, Tong P, Meong M, et al. MOABS: model based analysis of bisulfite sequencing data. Genome Biol. 2014;15:R38.

    Article  PubMed  PubMed Central  Google Scholar 

  92. Bock C, Halachev K, Büch J, Lengauer T. EpiGRAPH: user-friendly software for statistical analysis and prediction of (epi)genomic data. Genome Biol. 2009;10:R14.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  93. Klein H-U, Schäfer M, Porse BT, Hasemann MS, Ickstadt K, Dugas M. Integrative analysis of histone ChIP-seq and transcription data using Bayesian mixture models. Bioinformatics. 2014;30:1154–62.

    Article  CAS  PubMed  Google Scholar 

  94. Barazandeh A, Mohammadabadi MR, Ghaderi-Zefrehei M, Nezamabadi-Pour H. Genome-wide analysis of CpG islands in some livestock genomes and their relationship with genomic features. Czech J Anim Sci. 2016;61:487–95.

    Article  Google Scholar 

  95. Hackenberg M, Barturen G, Carpena P, Luque-Escamilla PL, Previti C, Oliver JL. Prediction of CpG-island function: CpG clustering vs sliding-window methods. BMC Genomics. 2010;11:327.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  96. Gardiner-Garden M, Frommer M. CpG Islands in vertebrate genomes. J Mol Biol. 1987;196:261–82.

    Article  CAS  PubMed  Google Scholar 

  97. Marchevsky AM, Tsou JA, Laird-Offringa IA. Classification of individual lung cancer cell lines based on DNA methylation markers: use of linear discriminant analysis and artificial neural networks. J Mol Diagn. 2004;6:28–36.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  98. Das R, Dimitrova N, Xuan Z, Rollins RA, Haghighi F, Edwards JR, et al. Computational prediction of methylation status in human genomic sequences. Proc Natl Acad Sci U S A. 2006;103:10713–6.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  99. Bhasin M, Zhang H, Reinherz EL, Reche PA. Prediction of methylated CpGs in DNA sequences using a support vector machine. FEBS Lett. 2005;579:4302–8.

    Article  CAS  PubMed  Google Scholar 

  100. Chen H, Xue Y, Huang N, Yao X, Sun Z. MeMo: a web tool for prediction of protein methylation modifications. Nucleic Acids Res. 2006;34:W249–53.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  101. Weber M, Hellmann I, Stadler MB, Ramos L, Pääbo S, Rebhan M, et al. Distribution, silencing potential and evolutionary impact of promoter DNA methylation in the human genome. Nat Genet. 2007;39:457–66.

    Article  CAS  PubMed  Google Scholar 

  102. Yamada Y, Watanabe H, Miura F, Soejima H, Uchiyama M, Iwasaka T, et al. A comprehensive analysis of allelic methylation status of CpG Islands on human chromosome 21q. Genome Res. 2004;14:247–66.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  103. Bock C, Walter J, Paulsen M, Lengauer T. CpG island mapping by epigenome prediction. PLoS Comput Biol. 2007;3:e110.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  104. Bhutani N, Burns DM, Blau HM. DNA demethylation dynamics. Cell. 2011;146:866–72.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  105. Wu H, Zhang Y. Charting oxidized methylcytosines at base resolution. Nat Struct Mol Biol. 2015;22:656–61.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  106. Kroeze LI, van der Reijden BA, Jansen JH. 5-Hydroxymethylcytosine: an epigenetic mark frequently deregulated in cancer. Biochim Biophys Acta. 2015;1855:144–54.

    CAS  PubMed  Google Scholar 

  107. Guo JU, Su Y, Zhong C, Ming G, Song H. Emerging roles of TET proteins and 5-hydroxymethylcytosines in active DNA demethylation and beyond. Cell Cycle. 2011;10:2662–8.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  108. Drew HR, Wing RM, Takano T, Broka C, Tanaka S, Itakura K, et al. Structure of a B-DNA dodecamer: conformation and dynamics. Proc Natl Acad Sci U S A. 1981;78:2179–83.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  109. Lercher L, McDonough MA, El-Sagheer AH, Thalhammer A, Kriaucionis S, Brown T, et al. Structural insights into how 5-hydroxymethylation influences transcription factor binding. Chem Commun. 2014;50:1794–6.

    Article  CAS  Google Scholar 

  110. Krawczyk K, Demharter S, Knapp B, Deane CM, Minary P. In silico structural modeling of multiple epigenetic marks on DNA. Bioinformatics. 2018;34:41–8.

    Article  CAS  PubMed  Google Scholar 

  111. Dodd IB, Micheelsen MA, Sneppen K, Thon G. Theoretical analysis of epigenetic cell memory by nucleosome modification. Cell. 2007;129:813–22.

    Article  CAS  PubMed  Google Scholar 

  112. Schübeler D, MacAlpine DM, Scalzo D, Wirbelauer C, Kooperberg C, van Leeuwen F, et al. The histone modification pattern of active genes revealed through genome-wide chromatin analysis of a higher eukaryote. Genes Dev. 2004;18:1263–71.

    Article  PubMed  PubMed Central  Google Scholar 

  113. Roh T-Y, Cuddapah S, Zhao K. Active chromatin domains are defined by acetylation islands revealed by genome-wide mapping. Genes Dev. 2005;19:542–52.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  114. Xu H, Wei C-L, Lin F, Sung W-K. An HMM approach to genome-wide identification of differential histone modification sites from ChIP-seq data. Bioinformatics. 2008;24:2344–9.

    Article  CAS  PubMed  Google Scholar 

  115. Won K-J, Chepelev I, Ren B, Wang W. Prediction of regulatory elements in mammalian genomes using chromatin signatures. BMC Bioinform. 2008;9:547.

    Article  CAS  Google Scholar 

  116. Kouskoumvekaki I, Hansen NT, Björkling F, Vadlamudi SM, Jónsdóttir SÓ. Prediction of pH-dependent aqueous solubility of histone deacetylase (HDAC) inhibitors. SAR QSAR Environ Res. 2008;19:167–77.

    Article  CAS  PubMed  Google Scholar 

  117. Thurman RE, Day N, Noble WS, Stamatoyannopoulos JA. Identification of higher-order functional domains in the human ENCODE regions. Genome Res. 2007;17:917–27.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  118. Benveniste D, Sonntag H-J, Sanguinetti G, Sproul D. Transcription factor binding predicts histone modifications in human cell lines. Proc Natl Acad Sci U S A. 2014;111:13367–72.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  119. Juvale DC, Kulkarni VV, Deokar HS, Wagh NK, Padhye SB, Kulkarni VM. 3D-QSAR of histone deacetylase inhibitors: hydroxamate analogues. Org Biomol Chem. 2006;4:2858–68.

    Article  CAS  PubMed  Google Scholar 

  120. Lin Y-C, Lin J-H, Chou C-W, Chang Y-F, Yeh S-H, Chen C-C. Statins increase p21 through Inhibition of histone deacetylase activity and release of promoter-associated HDAC1/2. Cancer Res. 2008;68:2375–83.

    Article  CAS  PubMed  Google Scholar 

  121. Roudbar MA, Mohammadabadi M, Salmani V. Epigenetics: a new challenge in animal breeding. Gen Third Millennium. 2014;12:3900–14.

    Google Scholar 

  122. Strahl BD, Allis CD. The language of covalent histone modifications. Nature. 2000;403:41–5.

    Article  CAS  PubMed  Google Scholar 

  123. Hon G, Ren B, Wang W. ChromaSig: a probabilistic approach to finding common chromatin signatures in the human genome. PLoS Comput Biol. 2008;4:e1000201.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  124. Ernst J, Kellis M. ChromHMM: automating chromatin-state discovery and characterization. Nat Methods. 2012;9:215–6.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  125. Zhou J, Troyanskaya OG. Global quantitative modeling of chromatin factor interactions. PLoS Comput Biol. 2014;10:e1003525.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  126. Reyna-López GE, Simpson J, Ruiz-Herrera J. Differences in DNA methylation patterns are detectable during the dimorphic transition of fungi by amplification of restriction polymorphisms. Mol Gen Genet MGG. 1997;253:703–10.

    Article  PubMed  Google Scholar 

  127. Ashikawa I. Surveying CpG methylation at 5′-CCGG in the genomes of rice cultivars. Plant Mol Biol. 2001;45:31–9.

    Article  CAS  PubMed  Google Scholar 

  128. Wang W, Zhao X, Pan Y, Zhu L, Fu B, Li Z. DNA methylation changes detected by methylation-sensitive amplified polymorphism in two contrasting rice genotypes under salt stress. J Genet Genomics. 2011;38:419–24.

    Article  CAS  PubMed  Google Scholar 

  129. Karan R, DeLeon T, Biradar H, Subudhi PK. Salt stress induced variation in DNA methylation pattern and its influence on gene expression in contrasting rice genotypes. PLoS One. 2012;7:e40203.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  130. Zheng X, Chen L, Li M, Lou Q, Xia H, Wang P, et al. Transgenerational variations in DNA methylation induced by drought stress in two rice varieties with distinguished difference to drought resistance. PLoS One. 2013;8:e80253.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  131. Sha AH, Lin XH, Huang JB, Zhang DP. Analysis of DNA methylation related to rice adult plant resistance to bacterial blight based on methylation-sensitive AFLP (MSAP) analysis. Mol Gen Genomics. 2005;273:484–90.

    Article  CAS  Google Scholar 

  132. Garg R, Narayana Chevala V, Shankar R, Jain M. Divergent DNA methylation patterns associated with gene expression in rice cultivars with contrasting drought and salinity stress response. Sci Rep. 2015;5:14922.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  133. Higo A, Saihara N, Miura F, Higashi Y, Yamada M, Tamaki S, et al. DNA methylation is reconfigured at the onset of reproduction in rice shoot apical meristem. Nat Commun. 2020;11:4079.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  134. Tamaki S, Tsuji H, Matsumoto A, Fujita A, Shimatani Z, Terada R, et al. FT-like proteins induce transposon silencing in the shoot apex during floral induction in rice. Proc Natl Acad Sci U S A. 2015;112:E901–10.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  135. Zhang JJ, Zhou ZS, Song JB, Liu ZP, Yang H. Molecular dissection of atrazine-responsive transcriptome and gene networks in rice by high-throughput sequencing. J Hazard Mater. 2012;219–220:57–68.

    Article  PubMed  CAS  Google Scholar 

  136. Feng SJ, Liu XS, Tao H, Tan SK, Chu SS, Oono Y, et al. Variation of DNA methylation patterns associated with gene expression in rice (Oryza sativa) exposed to cadmium. Plant Cell Environ. 2016;39:2629–49.

    Article  CAS  PubMed  Google Scholar 

  137. Pan Y, Wang W, Zhao X, Zhu L, Fu B, Li Z. DNA methylation alterations of rice in response to cold stress. Plant Omics. 2011;4(7):364–9.

    CAS  Google Scholar 

  138. Xie H, Han Y, Li X, Dai W, Song X, Olsen KM, et al. Climate-dependent variation in cold tolerance of weedy rice and rice mediated by OsICE1 promoter methylation. Mol Ecol. 2020;29:121–37.

    Article  CAS  PubMed  Google Scholar 

  139. Ding J, Shen J, Mao H, Xie W, Li X, Zhang Q. RNA-directed DNA methylation is involved in regulating photoperiod-sensitive male sterility in Rice. Mol Plant. 2012;5:1210–6.

    Article  CAS  PubMed  Google Scholar 

  140. Luan X, Liu S, Ke S, Dai H, Xie X-M, Hsieh T-F, et al. Epigenetic modification of ESP, encoding a putative long noncoding RNA, affects panicle architecture in rice. Rice. 2019;12:20.

    Article  PubMed  PubMed Central  Google Scholar 

  141. Yu H, Dai Z. SNNRice6mA: a deep learning method for predicting DNA N6-methyladenine sites in rice genome. Front Genet [Internet]. Frontiers; 2019 [cited 2021 Feb 17];10. Available from: https://www.frontiersin.org/articles/10.3389/fgene.2019.01071/full.

  142. Lv H, Dao F-Y, Guan Z-X, Zhang D, Tan J-X, Zhang Y, et al. iDNA6mA-rice: a computational tool for detecting N6-methyladenine sites in rice. Front Genet [Internet]. Frontiers; 2019 [cited 2021 Feb 17];10. Available from: https://www.frontiersin.org/articles/10.3389/fgene.2019.00793/full.

  143. Feng P, Yang H, Ding H, Lin H, Chen W, Chou K-C. iDNA6mA-PseKNC: identifying DNA N6-methyladenosine sites by incorporating nucleotide physicochemical properties into PseKNC. Genomics. 2019;111:96–102.

    Article  CAS  PubMed  Google Scholar 

  144. Tahir M, Tayara H, Chong KT. iDNA6mA (5-step rule): identification of DNA N6-methyladenine sites in the rice genome by intelligent computational model via Chou’s 5-step rule. Chemom Intell Lab Syst. 2019;189:96–101.

    Article  CAS  Google Scholar 

  145. Chen W, Lv H, Nie F, Lin H. i6mA-Pred: identifying DNA N6-methyladenine sites in the rice genome. Bioinformatics. 2019;35:2796–800.

    Article  CAS  PubMed  Google Scholar 

  146. Basith S, Manavalan B, Shin TH, Lee G. SDM6A: a web-based integrative machine-learning framework for predicting 6mA sites in the rice genome. Mol Ther Nucleic Acids. 2019;18:131–41.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  147. Amin R, Rahman CR, Toaha MSI, Shatabda S. i6mA-CNN: a convolution based computational approach towards identification of DNA N6-methyladenine sites in rice genome. ArXiv200710458 Cs Q-Bio [Internet]. 2020 [cited 2021 Feb 17]. Available from: http://arxiv.org/abs/2007.10458.

  148. Huang Q, Zhang J, Wei L, Guo F, Zou Q. 6mA-RicePred: a method for identifying DNA N6-methyladenine sites in the rice genome based on feature fusion. Front Plant Sci [Internet]. Frontiers; 2020 [cited 2021 Feb 17];11. Available from: https://www.frontiersin.org/articles/10.3389/fpls.2020.00004/full.

  149. Dowen RH, Pelizzola M, Schmitz RJ, Lister R, Dowen JM, Nery JR, et al. Widespread dynamic DNA methylation in response to biotic stress. Proc Natl Acad Sci U S A. 2012;109:E2183–91.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  150. Song W-Y, Wang G-L, Chen L-L, Kim H-S, Pi L-Y, Holsten T, et al. A receptor kinase-like protein encoded by the rice disease resistance gene, Xa21. Science. 1995;270:1804–6.

    Article  CAS  PubMed  Google Scholar 

  151. Akimoto K, Katakami H, Kim H-J, Ogawa E, Sano CM, Wada Y, et al. Epigenetic inheritance in rice plants. Ann Bot. 2007;100:205–17.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  152. Dong ZY, Wang YM, Zhang ZJ, Shen Y, Lin XY, Ou XF, et al. Extent and pattern of DNA methylation alteration in rice lines derived from introgressive hybridization of rice and Zizania latifolia Griseb. Theor Appl Genet. 2006;113:196–205.

    Article  CAS  PubMed  Google Scholar 

  153. Yan H, Jin W, Nagaki K, Tian S, Ouyang S, Buell CR, et al. Transcription and histone modifications in the recombination-free region spanning a rice centromere. Plant Cell. 2005;17:3227–38.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  154. Yu Y, Bu Z, Shen W-H, Dong A. An update on histone lysine methylation in plants. Prog Nat Sci. 2009;19:407–13.

    Article  CAS  Google Scholar 

  155. Sui P, Jin J, Ye S, Mu C, Gao J, Feng H, et al. H3K36 methylation is critical for brassinosteroid-regulated plant growth and development in rice. Plant J. 2012;70:340–7.

    Article  CAS  PubMed  Google Scholar 

  156. Nallamilli BRR, Edelmann MJ, Zhong X, Tan F, Mujahid H, Zhang J, et al. Global analysis of lysine acetylation suggests the involvement of protein acetylation in diverse biological processes in rice (Oryza sativa). PLoS One. 2014;9:e89283.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  157. Li C, Huang L, Xu C, Zhao Y, Zhou D-X. Altered levels of histone deacetylase OsHDT1 affect differential gene expression patterns in hybrid rice. PLoS One. 2011;6:e21789.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  158. Li W, Han Y, Tao F, Chong K. Knockdown of SAMS genes encoding S-adenosyl-l-methionine synthetases causes methylation alterations of DNAs and histones and leads to late flowering in rice. J Plant Physiol. 2011;168:1837–43.

    Article  CAS  PubMed  Google Scholar 

  159. Molitor A, Shen W-H. The Polycomb complex PRC1: composition and function in plants. J Genet Genomics. 2013;40:231–8.

    Article  CAS  PubMed  Google Scholar 

  160. Feng J, Shen WH. Dynamic regulation and function of histone monoubiquitination in plants. Front Plant Sci [Internet]. Frontiers; 2014 [cited 2021 Mar 5];5. Available from: https://www.frontiersin.org/articles/10.3389/fpls.2014.00083/full.

Download references

Author information

Author notes

  1. Ravindra Donde and S. Sabarinathan contributed equally with all other contributors.

Authors and Affiliations

  1. Crop Improvement Division, ICAR-National Rice Research Institute, Cuttack, Odisha, India

    Gayatri Gouda, Manoj Kumar Gupta, Ravindra Donde, S. Sabarinathan & Lambodar Behera

  2. Department of Seed Science and Technology, College of Agriculture, Odisha University of Agriculture and Technology, Bhubaneswar, Odisha, India

    S. Sabarinathan

  3. Department of Biotechnology & Bioinformatics, Yogi Vemana University, Kadapa, Andhra Pradesh, India

    Ramakrishna Vadde

  4. Secretary (DARE), and Director General (ICAR), Government of India, New Delhi, India

    Trilochan Mohapatra

Authors
  1. Gayatri Gouda
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Manoj Kumar Gupta
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ravindra Donde
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. S. Sabarinathan
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Ramakrishna Vadde
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Lambodar Behera
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Trilochan Mohapatra
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Crop Improvement Division, ICAR-National Rice Research Institute, Cuttack, Odisha, India

    Manoj Kumar Gupta

  2. Crop Improvement Division, ICAR-National Rice Research Institute, Cuttack, Odisha, India

    Lambodar Behera

Ethics declarations

None.

Additional Information

Fig. 6.1 (CC BY 3.0) [63] has been reused under Creative Commons Attribution Licenses.

Rights and permissions

Reprints and permissions

Copyright information

© 2021 The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Gouda, G. et al. (2021). Computational Epigenetics in Rice Research. In: Gupta, M.K., Behera, L. (eds) Applications of Bioinformatics in Rice Research. Springer, Singapore. https://doi.org/10.1007/978-981-16-3997-5_6

Download citation

Publish with us

Policies and ethics

Search

Navigation