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

Genome Biol. 2017 Feb 21;18(1):38. doi: 10.1186/s13059-017-1168-4.

Abstract

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.

Keywords: CpG methylation; Epi-allele; Epi-allelic haplotype; Epigenetic state; GpC methylation; NOMe-seq.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Alleles
  • CpG Islands
  • DNA Methylation*
  • Epigenomics / methods*
  • Gene Expression Profiling
  • Genotype
  • High-Throughput Nucleotide Sequencing*
  • Nucleosomes / metabolism
  • Polymorphism, Single Nucleotide
  • Protein Binding
  • Reproducibility of Results
  • Sequence Analysis, DNA*
  • Software*

Substances

  • Nucleosomes