Karina Dalsgaard Sørensen

epiG: statistical inference and profiling of DNA methylation from whole-genome bisulfite sequencing data

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  • Martin Vincent, Department of Mathematical Sciences, University of Copenhagen, Copenhagen, 2100, Denmark.
  • ,
  • Kamilla Mundbjerg, USC Norris Comprehensive Cancer Center, Keck School of Medicine, Los Angeles, 90089-9176, CA, USA.
  • ,
  • Jakob Skou Pedersen
  • Gangning Liang, Department of Urology, Biochemistry and Molecular Biology, USC/Norris Comprehensive Cancer Center, Keck School of Medicine, University of Southern California, Los Angeles, California 90089-9181, USA
  • ,
  • Peter A Jones, Van Andel Research Institute, Grand Rapids, MI, 49503, USA.
  • ,
  • Torben Falck Ørntoft
  • Karina Dalsgaard Sørensen
  • Carsten Wiuf, Department of Mathematical Sciences, University of Copenhagen, Copenhagen, 2100, Denmark. wiuf@math.ku.dk.

The study of epigenetic heterogeneity at the level of individual cells and in whole populations is the key to understanding cellular differentiation, organismal development, and the evolution of cancer. We develop a statistical method, epiG, to infer and differentiate between different epi-allelic haplotypes, annotated with CpG methylation status and DNA polymorphisms, from whole-genome bisulfite sequencing data, and nucleosome occupancy from NOMe-seq data. We demonstrate the capabilities of the method by inferring allele-specific methylation and nucleosome occupancy in cell lines, and colon and tumor samples, and by benchmarking the method against independent experimental data.

Original languageEnglish
JournalGenome Biology (Online Edition)
Volume18
Issue1
Pages (from-to)38
ISSN1474-7596
DOIs
Publication statusPublished - 21 Feb 2017

    Research areas

  • Journal Article

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