Background: DNA methylation of dinucleotide CpG is an essential epigenetic modification that plays a key role in transcription. Bisulfite conversion method is a gold standard for DNA methylation profiling that provides single nucleotide resolution. However, whole-genome bisulfite conversion is very expensive. Alternatively, DNA enrichment-based methods offer high coverage of methylated CpG dinucleotides with the lowest cost per CpG covered genome-wide and have been used widely. They measure the DNA enrichment of methyl-CpG binding, therefore do not directly provide absolute methylation levels. Further, the enrichment is influenced by confounding factors besides the methylation status, e.g., CpG density. Computational models that can accurately derive the absolute methylation levels from the enrichment data are necessary. Results: We present MeDEStrand, a method uses sigmoid function to estimate and correct the CpG bias from the numbers of reads that fell within bins that divide the genome. In addition, unlike the previous methods, which estimate CpG bias based on reads mapped at the same genomic loci, MeDEStrand processes the reads for the positive and negative DNA strands separately. We compare the performance of MeDEStrand with three other state-of-the-art methods MEDIPS, BayMeth and QSEA on four independent datasets generated using immortalized cell lines (GM12878 and K562) and human patient primary cells (foreskin fibroblast and mammary epithelial). Based on the comparison between the inferred absolute methylation levels from MeDIP-seq and the corresponding RRBS data, MeDEStrand shows the best performance at high resolution of 25, 50 and 100 base pairs. Conclusions: MeDEStrand benefits from the estimation of CpG bias with a sigmoid function and the procedure to process reads mapped to the positive and negative DNA strands separately. MeDEStrand is a tool to infer whole-genome absolute DNA methylation level at the cost of enrichment-based methods with adequate accuracy and resolution. R package MeDEStrand and its tutorial is freely available for download at https://github.com/jxu1234/MeDEStrand.git.
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