Article open access publication

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

Genome Biology, Springer Nature, ISSN 1465-6906

Volume 18, 1, 2017

DOI:10.1186/s13059-017-1168-4, Dimensions: pub.1083854070, PMC: PMC5320668, PMID: 28222791,



  1. (1) University of Copenhagen, grid.5254.6, KU
  2. (2) University of Southern California, grid.42505.36
  3. (3) Aarhus University Hospital, grid.154185.c, Central Denmark Region
  4. (4) Van Andel Institute, grid.251017.0


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.


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Times Cited: 5

Field Citation Ratio (FCR): 1.04

Relative Citation ratio (RCR): 0.21

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