Model-based and context-specific background correction and differential methylation testing for MBDCap-seq

DNA methylation in promoter regions has long been considered an essential mechanism of transcriptional regulation, and it has been demonstrated to be involved in cell development, tumor progression and aging. Methyl-CpG binding domain-based capture followed by high throughput sequencing (MBDCap-seq) is widely used to examine DNA methylation pattern genome-wide. Current MBDCap-seq data analysis approaches focus on measurement of methylated CpG sequence reads, without considering genomic characteristics and tissue-specific context and their impact to the amount of methylated DNA measurement (signal) and background fluctuation (noise). Therefore, specific software needs to be developed to process MBDCap-seq datasets. Here we presented a novel algorithm, termed MBDDiff, implemented as an R package that is designed specifically for processing MBDCap-seq datasets. MBDDiff contains three modules: quality assessment of datasets and quantification of DNA methylation; determination of differential methylation of promoter regions; and visualization functionalities. Simulation studies were carried out to demonstrate the accuracy of MBDDiff algorithm in detecting differential methylation in promoter regions. We also tested functionalities of MBDDiff to a set of in-house prostate cancer samples and a set of public-domain triple negative breast cancer samples profiled with MBDCap-seq protocol, and demonstrated the capability of identifying differential methylation of promoter regions of genes that might contribute to cancer development and progression.