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Differential methylation analysis for BS-seq data under general experimental design
Author(s) -
Yongseok Park,
Hao Wu
Publication year - 2016
Publication title -
bioinformatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.599
H-Index - 390
eISSN - 1367-4811
pISSN - 1367-4803
DOI - 10.1093/bioinformatics/btw026
Subject(s) - bioconductor , bisulfite sequencing , dna methylation , computer science , negative binomial distribution , epigenetics , computational biology , data mining , algorithm , biology , mathematics , statistics , genetics , gene , gene expression , poisson distribution
DNA methylation is an epigenetic modification with important roles in many biological processes and diseases. Bisulfite sequencing (BS-seq) has emerged recently as the technology of choice to profile DNA methylation because of its accuracy, genome coverage and higher resolution. Current statistical methods to identify differential methylation mainly focus on comparing two treatment groups. With an increasing number of experiments performed under a general and multiple-factor design, particularly in reduced representation bisulfite sequencing, there is a need to develop more flexible, powerful and computationally efficient methods.

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