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DNA Methylation and Transcriptomic Next-Generation Technologies in Cereal Genomics

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Cereal Genomics

Part of the book series: Methods in Molecular Biology ((MIMB,volume 2072))

Abstract

RNA sequencing (RNA-seq) coupled to DNA methylation strategies enables the detection and characterization of genes which expression levels might be mediated by DNA methylation. Here we describe a bioinformatics protocol to analyze gene expression levels using RNA-seq data that allow us to identify candidate genes to be tested by bisulfite assays. The candidate methylated genes are usually those that are low expressed in a particular condition or developmental stage.

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References

  1. FAO (2019) Cereal supply and demand brief. World food situation. www.fao.org/worldfoodsituation/csdb/en. Accessed 24 Feb 2019

  2. International Rice Genome Sequencing Project (2005) The map-based sequence of the rice genome. Nature 436:793–800

    Article  Google Scholar 

  3. Ohyanagi H (2006) The Rice Annotation Project Database (RAP-DB): hub for Oryza sativa ssp. japonica genome information. Nucleic Acids Res 34:D741–D744

    Article  CAS  PubMed  Google Scholar 

  4. Ouyang S, Zhu W, Hamilton J et al (2007) The TIGR Rice genome annotation resource: improvements and new features. Nucleic Acids Res 35:D883–D887

    Article  CAS  PubMed  Google Scholar 

  5. Kawahara Y, de la Bastide M, Hamilton JP et al (2013) Improvement of the Oryza sativa Nipponbare reference genome using next generation sequence and optical map data. Rice 6:4

    Article  PubMed  PubMed Central  Google Scholar 

  6. Schnable PS, Ware D, Fulton RS et al (2009) The B73 maize genome: complexity, diversity, and dynamics. Science 326:1112–1115

    Article  CAS  PubMed  Google Scholar 

  7. Vielle-Calzada J-P, Martínez de la Vega O, Hernández-Guzmán G et al (2009) The Palomero genome suggests metal effects on domestication. Science 326:1078

    Article  CAS  PubMed  Google Scholar 

  8. Paterson AH, Bowers JE, Bruggmann R et al (2009) The Sorghum bicolor genome and the diversification of grasses. Nature 457:551

    Article  CAS  PubMed  Google Scholar 

  9. McCormick RF, Truong SK, Sreedasyam A et al (2018) The Sorghum bicolor reference genome: improved assembly, gene annotations, a transcriptome atlas, and signatures of genome organization. Plant J 93:338–354

    Article  CAS  PubMed  Google Scholar 

  10. Brenchley R, Spannagl M, Pfeifer M et al (2012) Analysis of the bread wheat genome using whole-genome shotgun sequencing. Nature 491:705–710

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. The International Barley Genome Sequencing Consortium (2012) A physical, genetic and functional sequence assembly of the barley genome. Nature 491:711–716

    Article  Google Scholar 

  12. Schlueter J (2019) The oat genome project. AVENA GENOME. avenagenome.org. Accessed 20 Feb 2019

  13. Gupta PK, Varshney RK (eds) (2005) Cereal genomics. Springer, Dordrecht

    Google Scholar 

  14. Shendure J, Balasubramanian S, Church GM et al (2017) DNA sequencing at 40: past, present and future. Nature 550:345–353

    Article  CAS  PubMed  Google Scholar 

  15. Jiao Y, Peluso P, Shi J et al (2017) Improved maize reference genome with single-molecule technologies. Nature 546:524

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Edwards D, Batley J (2010) Plant genome sequencing: applications for crop improvement: plant genome sequencing: applications for crop improvement. Plant Biotechnol J 8:2–9

    Article  CAS  PubMed  Google Scholar 

  17. Visendi P, Batley J, Edwards D (2013) Next generation characterisation of cereal genomes for marker discovery. Biology 2:1357–1377

    Article  PubMed  PubMed Central  Google Scholar 

  18. Rival A, Beulé T, Aberlenc Bertossi F et al (2010) Plant epigenetics: from genomes to epigenomes. Not Bot Hortic Agrobot Cluj-Napoca 38:09–15

    Article  CAS  Google Scholar 

  19. Edwards MA, Henry RJ (2011) DNA sequencing methods contributing to new directions in cereal research. J Cereal Sci 54:395–400

    Article  CAS  Google Scholar 

  20. Duarte-Aké F, Castillo-Castro E, Pool FB et al (2016) Physiological differences and changes in global DNA methylation levels in Agave angustifolia Haw. albino variant somaclones during the micropropagation process. Plant Cell Rep 35:2489–2502

    Article  PubMed  Google Scholar 

  21. Lira-Medeiros CF, Parisod C, Fernandes RA et al (2010) Epigenetic variation in mangrove plants occurring in contrasting natural environment. PLoS One 5:e10326

    Article  PubMed  PubMed Central  Google Scholar 

  22. Langdale JA, Taylor WC, Nelson T (1991) Cell-specific accumulation of maize phosphoenolpyruvate carboxylase is correlated with demethylation at a specific site >3 kb upstream of the gene. Mol Gen Genet 225:49–55

    Article  CAS  PubMed  Google Scholar 

  23. Mager S, Schönberger B, Ludewig U (2018) The transcriptome of zinc deficient maize roots and its relationship to DNA methylation loss. BMC Plant Biol 18:372

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Portwood JL, Woodhouse MR, Cannon EK et al (2019) MaizeGDB 2018: the maize multi-genome genetics and genomics database. Nucleic Acids Res 47:D1146–D1154

    Article  PubMed  Google Scholar 

  25. Diepenbrock CH, Kandianis CB, Lipka AE et al (2017) Novel loci underlie natural variation in vitamin E levels in maize grain. Plant Cell 29:2374

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Atkinson L (2019) Open source initiative. March 2019 license-discuss summary. opensource.org. Accessed 16 Apr 2019

  27. SRA Toolkit Development Team SRA-Tools. SRA Toolkit Documentation. ncbi.github.io/sra-tools/. Accessed 14 Feb 2019

  28. Andrews S (2010) FastQC. A quality control tool for high throughput sequence data. bioinformatics.babraham.ac.uk/projects/fastqc. Accessed 10 Apr 2018

  29. Bolger AM, Lohse M, Usadel B (2014) Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 30:2114–2120

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Langmead B, Salzberg SL (2012) Fast gapped-read alignment with bowtie 2. Nat Methods 9:357–359

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Anders S, Pyl PT, Huber W (2015) HTSeq--a python framework to work with high-throughput sequencing data. Bioinformatics 31:166–169

    Article  CAS  PubMed  Google Scholar 

  32. R Core Team (2013) R: A language and environment for statistical computing. https://www.r-project.org/. Accessed 2 Feb 2019

  33. Góngora-Castillo E, Buell CR (2013) Bioinformatics challenges in de novo transcriptome assembly using short read sequences in the absence of a reference genome sequence. Nat Prod Rep 30:490–500

    Article  PubMed  Google Scholar 

  34. Góngora-Castillo E, Fedewa G, Yeo Y et al (2012) Genomic approaches for interrogating the biochemistry of medicinal plant species. Methods Enzymol 517:139–159

    Article  PubMed  PubMed Central  Google Scholar 

  35. Sims D, Sudbery I, Ilott NE et al (2014) Sequencing depth and coverage: key considerations in genomic analyses. Nat Rev Genet 15:121–132

    Article  CAS  PubMed  Google Scholar 

  36. Conesa A, Madrigal P, Tarazona S et al (2016) A survey of best practices for RNA-seq data analysis. Genome Biol 17:13. https://doi.org/10.1186/s13059-016-0881-8

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Andrews S (2016) Loss of base call accuracy with increasing sequencing cycles. https://sequencing.qcfail.com/. Accessed 14 Feb 2019

  38. Langmead B, Wilks C, Antonescu V et al (2019) Scaling read aligners to hundreds of threads on general-purpose processors. Bioinformatics 35:421–432

    Article  CAS  PubMed  Google Scholar 

  39. Li H, Handsaker B, Wysoker A et al (2009) The sequence alignment/map format and SAMtools. Bioinformatics 25:2078–2079

    Article  PubMed  PubMed Central  Google Scholar 

  40. Hwang B, Lee JH, Bang D (2018) Single-cell RNA sequencing technologies and bioinformatics pipelines. Exp Mol Med 50:96

    Article  PubMed Central  Google Scholar 

  41. Bullard JH, Purdom E, Hansen KD et al (2010) Evaluation of statistical methods for normalization and differential expression in mRNA-Seq experiments. BMC Bioinformatics 11:94

    Article  PubMed  PubMed Central  Google Scholar 

  42. Dillies M-A, Rau A, Aubert J et al (2013) A comprehensive evaluation of normalization methods for Illumina high-throughput RNA sequencing data analysis. Brief Bioinform 14:671–683

    Article  CAS  PubMed  Google Scholar 

  43. Evans C, Hardin J, Stoebel DM (2018) Selecting between-sample RNA-Seq normalization methods from the perspective of their assumptions. Brief Bioinform 19:776–792

    Article  CAS  PubMed  Google Scholar 

  44. StatQuest (2015) RPKM, FPKM and TPM, Clearly explained. https://www.rna-seqblog.com/rpkm-fpkm-and-tpm-clearly-explained/. Accessed 20 Feb 2019

  45. Wickham H (2016) Elegant graphics for data analysis. In: Springer (ed) ggplot2, 2nd edn. Verlag, New York

    Chapter  Google Scholar 

  46. Zhao S, Guo Y, Sheng Q et al (2014) Advanced heat map and clustering analysis using Heatmap3. Biomed Res Int 2014:986048. https://doi.org/10.1155/2014/986048

    Article  PubMed  PubMed Central  Google Scholar 

  47. 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 89:1827–1831

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Gruntman M, Novoplansky A (2004) Physiologically mediated self/non-self discrimination in roots. Proc Natl Acad Sci U S A 101:3863–3867

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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Acknowledgments

The authors work was supported by two grants received from the National Council for Science and Technology (CB2016-285898, CB2016-286368 and INFR-2016-01-269833) and Cátedras Marcos Moshinsky 2017.

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Correspondence to Elsa Góngora-Castillo .

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Soto-Cardinault, C.G., Duarte-Aké, F., De-la-Peña, C., Góngora-Castillo, E. (2020). DNA Methylation and Transcriptomic Next-Generation Technologies in Cereal Genomics. In: Vaschetto, L. (eds) Cereal Genomics. Methods in Molecular Biology, vol 2072. Humana, New York, NY. https://doi.org/10.1007/978-1-4939-9865-4_7

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  • DOI: https://doi.org/10.1007/978-1-4939-9865-4_7

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  • Publisher Name: Humana, New York, NY

  • Print ISBN: 978-1-4939-9864-7

  • Online ISBN: 978-1-4939-9865-4

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