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Modeling, simulation and analysis of methylation profiles from reduced representation bisulfite sequencing experiments

Author

Listed:
  • Lacey Michelle R.

    (Department of Mathematics, Tulane University, New Orleans, LA, USA Tulane Cancer Center, Tulane Health Sciences Center, New Orleans, LA, USA)

  • Baribault Carl

    (Tulane Cancer Center, Tulane Health Sciences Center, New Orleans, LA, USA)

  • Ehrlich Melanie

    (Tulane Cancer Center, Tulane Health Sciences Center, New Orleans, LA, USA Program in Human Genetics, Tulane Health Sciences Center, New Orleans, LA, USA)

Abstract

The ENCODE project has funded the generation of a diverse collection of methylation profiles using reduced representation bisulfite sequencing (RRBS) technology, enabling the analysis of epigenetic variation on a genomic scale at single-site resolution. A standard application of RRBS experiments is in the location of differentially methylated regions (DMRs) between two sets of samples. Despite numerous publications reporting DMRs identified from RRBS datasets, there have been no formal analyses of the effects of experimental and biological factors on the performance of existing or newly developed analytical methods. These factors include variable read coverage, differing group sample sizes across genomic regions, uneven spacing between CpG dinucleotide sites, and correlation in methylation levels among sites in close proximity. To better understand the interplay among technical and biological variables in the analysis of RRBS methylation profiles, we have developed an algorithm for the generation of experimentally realistic RRBS datasets. Applying insights derived from our simulation studies, we present a novel procedure that can identify DMRs spanning as few as three CpG sites with both high sensitivity and specificity. Using RRBS data from muscle vs. non-muscle cell cultures as an example, we demonstrate that our method reveals many more DMRs that are likely to be of biological significance than previous methods.

Suggested Citation

  • Lacey Michelle R. & Baribault Carl & Ehrlich Melanie, 2013. "Modeling, simulation and analysis of methylation profiles from reduced representation bisulfite sequencing experiments," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 12(6), pages 723-742, December.
    • Handle: RePEc:bpj:sagmbi:v:12:y:2013:i:6:p:723-742:n:5
    DOI: 10.1515/sagmb-2013-0027
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    References listed on IDEAS

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    1. Ryan Lister & Mattia Pelizzola & Robert H. Dowen & R. David Hawkins & Gary Hon & Julian Tonti-Filippini & Joseph R. Nery & Leonard Lee & Zhen Ye & Que-Minh Ngo & Lee Edsall & Jessica Antosiewicz-Bourg, 2009. "Human DNA methylomes at base resolution show widespread epigenomic differences," Nature, Nature, vol. 462(7271), pages 315-322, November.
    2. Kojadinovic, Ivan & Yan, Jun, 2010. "Modeling Multivariate Distributions with Continuous Margins Using the copula R Package," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 34(i09).
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