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Shotgun bisulphite sequencing of the Arabidopsis genome reveals DNA methylation patterning

Author

Listed:
  • Shawn J. Cokus

    (Cell, and Developmental Biology)

  • Suhua Feng

    (Cell, and Developmental Biology
    Howard Hughes Medical Institute,)

  • Xiaoyu Zhang

    (Cell, and Developmental Biology
    Present address: Department of Plant Biology, University of Georgia, Athens, Georgia 30602, USA.)

  • Zugen Chen

    (David Geffen School of Medicine, University of California at Los Angeles, Los Angeles, California 90095, USA)

  • Barry Merriman

    (David Geffen School of Medicine, University of California at Los Angeles, Los Angeles, California 90095, USA)

  • Christian D. Haudenschild

    (Illumina Inc., Hayward, California 94545, USA)

  • Sriharsa Pradhan

    (New England BioLabs, Ipswich, Massachusetts 01938, USA)

  • Stanley F. Nelson

    (David Geffen School of Medicine, University of California at Los Angeles, Los Angeles, California 90095, USA)

  • Matteo Pellegrini

    (Cell, and Developmental Biology)

  • Steven E. Jacobsen

    (Cell, and Developmental Biology
    Howard Hughes Medical Institute,)

Abstract

Mapping the methylome A newly developed method of characterizing an organism's 'methylome', that is the pattern of DNA methylation in the genome, has been used to generate a map of methylated cytosines in Arabidopsis to single base-pair resolution. The procedure, termed BS-Seq, combines bisulphite treatment of genomic DNA with ultra-high-throughput DNA sequencing to achieve a more precise and comprehensive result than previously possible. DNA methylation is an important factor in regulating gene expression, and this method, which can be applied to larger genomes like the mouse as well as to Arabidopsis, could prove a significant advance in the study of this form of gene regulation.

Suggested Citation

  • Shawn J. Cokus & Suhua Feng & Xiaoyu Zhang & Zugen Chen & Barry Merriman & Christian D. Haudenschild & Sriharsa Pradhan & Stanley F. Nelson & Matteo Pellegrini & Steven E. Jacobsen, 2008. "Shotgun bisulphite sequencing of the Arabidopsis genome reveals DNA methylation patterning," Nature, Nature, vol. 452(7184), pages 215-219, March.
    • Handle: RePEc:nat:nature:v:452:y:2008:i:7184:d:10.1038_nature06745
    DOI: 10.1038/nature06745
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    Cited by:

    1. Nanshan, Muye & Zhang, Nan & Xun, Xiaolei & Cao, Jiguo, 2022. "Dynamical modeling for non-Gaussian data with high-dimensional sparse ordinary differential equations," Computational Statistics & Data Analysis, Elsevier, vol. 173(C).
    2. Peng Ni & Neng Huang & Fan Nie & Jun Zhang & Zhi Zhang & Bo Wu & Lu Bai & Wende Liu & Chuan-Le Xiao & Feng Luo & Jianxin Wang, 2021. "Genome-wide detection of cytosine methylations in plant from Nanopore data using deep learning," Nature Communications, Nature, vol. 12(1), pages 1-11, December.
    3. Zhaohui Qin & Ben Li & Karen N. Conneely & Hao Wu & Ming Hu & Deepak Ayyala & Yongseok Park & Victor X. Jin & Fangyuan Zhang & Han Zhang & Li Li & Shili Lin, 2016. "Statistical Challenges in Analyzing Methylation and Long-Range Chromosomal Interaction Data," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 8(2), pages 284-309, October.
    4. Sun Shuying & Yu Xiaoqing, 2016. "HMM-Fisher: identifying differential methylation using a hidden Markov model and Fisher’s exact test," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 15(1), pages 55-67, March.
    5. Jian Fang & Jianjun Jiang & Sarah M. Leichter & Jie Liu & Mahamaya Biswal & Nelli Khudaverdyan & Xuehua Zhong & Jikui Song, 2022. "Mechanistic basis for maintenance of CHG DNA methylation in plants," Nature Communications, Nature, vol. 13(1), pages 1-12, December.
    6. Yinwen Zhang & Hosung Jang & Rui Xiao & Ioanna Kakoulidou & Robert S. Piecyk & Frank Johannes & Robert J. Schmitz, 2021. "Heterochromatin is a quantitative trait associated with spontaneous epiallele formation," Nature Communications, Nature, vol. 12(1), pages 1-13, December.
    7. Lijun Wang & Xiuling You & Dengfeng Ruan & Rui Shao & Hai-Qiang Dai & Weiliang Shen & Guo-Liang Xu & Wanlu Liu & Weiguo Zou, 2022. "TET enzymes regulate skeletal development through increasing chromatin accessibility of RUNX2 target genes," Nature Communications, Nature, vol. 13(1), pages 1-15, December.
    8. Olbricht Gayla R. & Craig Bruce A. & Doerge Rebecca W., 2012. "Incorporating Genomic Annotation into a Hidden Markov Model for DNA Methylation Tiling Array Data," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 11(5), pages 1-37, November.
    9. Jiyuan Hu & Tengfei Li & Zidi Xiu & Hong Zhang, 2015. "MAFsnp: A Multi-Sample Accurate and Flexible SNP Caller Using Next-Generation Sequencing Data," PLOS ONE, Public Library of Science, vol. 10(8), pages 1-15, August.

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