Identification of methylated regions with peak search based on Poisson model from massively parallel methylated DNA immunoprecipitation-sequencing data.

DNA methylation is one of the most important epigenetic modification types, which plays a critical role in gene expression. High efficient surveying of whole genome DNA methylation has been aims of many researchers for long. Recently, the rapidly developed massively parallel DNA-sequencing technologies open the floodgates to vast volumes of sequence data, enabling a paradigm shift in profiling the whole genome methylation. Here, we describe a strategy, combining methylated DNA immunoprecipitation sequencing with peak search to identify methylated regions on a whole-genome scale. Massively parallel methylated DNA immunoprecipitation sequencing combined with methylation DNA immunoprecipitation was adopted to obtain methylated DNA sequence data from human leukemia cell line K562, and the methylated regions were identified by peak search based on Poison model. From our result, 140 958 non-overlapping methylated regions have been identified in the whole genome. Also, the credibility of result has been proved by its strong correlation with bisulfite-sequencing data (Pearson R(2)=0.92). It suggests that this method provides a reliable and high-throughput strategy for whole genome methylation identification.