Methylation-level inferences and detection of differential methylation with MeDIP-seq data

Yan Zhou, Jiadi Zhu, Mingtao Zhao, Baoxue Zhang, Chunfu Jiang and Xiyan Yang

PLOS ONE, 2018, vol. 13, issue 8, 1-13

Abstract: DNA methylation is an essential epigenetic modification involved in regulating the expression of mammalian genomes. A variety of experimental approaches to generate genome-wide or whole-genome DNA methylation data have emerged in recent years. Methylated DNA immunoprecipitation followed by sequencing (MeDIP-seq) is one of the major tools used in whole-genome epigenetic studies. However, analyzing this data in terms of accuracy, sensitivity, and speed still remains an important challenge. Existing methods, such as BATMAN and MEDIPS, analyze MeDIP-seq data by dividing the whole genome into equal length windows and assume that each CpG of the same window has the same methylation level. More precise work is necessary to estimate the methylation level of each CpG site in the whole genome. In this paper, we propose a Statistical Inferences with MeDIP-seq Data (SIMD) to infer the methylation level for each CpG site. In addition, we analyze a real dataset for DNA methylation. The results show that our method displays improved precision in detecting differentially methylated CpG sites compared to the existing method. To meet the demands of the application, we have developed an R package called “SIMD”, which is freely available in https://github.com/FocusPaka/SIMD.

Date: 2018
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0201586

DOI: 10.1371/journal.pone.0201586

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