A full Bayesian partition model for identifying hypo- and hyper-methylated loci from single nucleotide resolution sequencing data. Issue 1 (January 2016)

• Record Type: Journal Article
• Authors: Wang, Henan; He, Chong; Kushwaha, Garima; Xu, Dong; Qiu, Jing
• Is Part Of: BMC bioinformatics. Volume 17:Issue 1(2016)
• Page Start: 71
• Page End: 83
• Publication Date: 2016-01
• Subjects: DNA methylation -- Full Bayesian partition model -- Hypo-methylation -- Hyper-methylation; Bioinformatics -- Periodicals; Computational biology -- Periodicals; 570.285
• DOI: 10.1186/s12859-015-0850-3
• Languages: English
• ISSNs: 1471-2105

Abstract

Abstract Backgroud 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. Results 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 without further post-hoc analysis. An R package named as MethyBayes implementing the proposed full Bayesian partition model will be submitted to the bioconductor website upon publication of the manuscript. Conclusions The proposed full Bayesian partition model outperforms existing methods in terms of power while maintaining a low false discovery rate based on simulation studies and real data analysis including bioinformatics analysis.