A full Bayesian partition model for identifying hypo- and hyper-methylated loci from single nucleotide resolution sequencing data

BMC Bioinformatics
Henan WangJing Qiu

Abstract

DNA methylation is an epigenetic modification that plays important roles on gene regulation. Study of whole-genome bisulfite sequencing and reduced representation bisulfite sequencing brings the availability of DNA methylation at single CpG resolution. The main interest of study on DNA methylation data is to test the methylation difference under two conditions of biological samples. However, the high cost and complexity of this sequencing experiment limits the number of biological replicates, which brings challenges to the development of statistical methods. Bayesian modeling is well known to be able to borrow strength across the genome, and hence is a powerful tool for high-dimensional-low-sample-size data. In order to provide accurate identification of methylation loci, especially for low coverage data, we propose a full Bayesian partition model to detect differentially methylated loci under two conditions of scientific study. Since hypo-methylation and hyper-methylation have distinct biological implication, it is desirable to differentiate these two types of differential methylation. The advantage of our Bayesian model is that it can produce one-step output of each locus being either equal-, hypo- or hyper-methylated locus w...Continue Reading

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Citations

Aug 25, 2016·Journal of Theoretical Biology·Lei YangYingli Lv
Jul 31, 2020·BMC Genetics·Manthan PatelUmashankar Singh

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Methods Mentioned

BETA
BS-seq

Software Mentioned

DSS
Methylkit
BSmooth

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