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

被引:6
|
作者
Vincent, Martin [1 ]
Mundbjerg, Kamilla [2 ]
Pedersen, Jakob Skou [3 ]
Liang, Gangning [4 ]
Jones, Peter A. [5 ]
Orntoft, Torben Falck [3 ]
Sorensen, Karina Dalsgaard [3 ]
Wiuf, Carsten [1 ]
机构
[1] Univ Copenhagen, Dept Math Sci, DK-2100 Copenhagen, Denmark
[2] Keck Sch Med, USC Norris Comprehens Canc Ctr, Los Angeles, CA 90089 USA
[3] Aarhus Univ Hosp, Dept Mol Med, DK-8200 Aarhus, Denmark
[4] Univ Southern Calif, Keck Sch Med, Dept Urol, Los Angeles, CA 90089 USA
[5] Van Andel Res Inst, Grand Rapids, MI 49503 USA
来源
GENOME BIOLOGY | 2017年 / 18卷
关键词
CpG methylation; GpC methylation; NOMe-seq; Epi-allelic haplotype; Epi-allele; Epigenetic state; ALLELE-SPECIFIC METHYLATION; EXPRESSION; LANDSCAPE; ENHANCERS; GENES;
D O I
10.1186/s13059-017-1168-4
中图分类号
Q81 [生物工程学(生物技术)]; Q93 [微生物学];
学科分类号
071005 ; 0836 ; 090102 ; 100705 ;
摘要
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.
引用
收藏
页数:16
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