Background: Protein pulldown using Methyl-CpG binding domain (MBD) proteins followed by high-throughput\nsequencing is a common method to determine DNA methylation. Algorithms have been developed to estimate\nabsolute methylation level from read coverage generated by affinity enrichment-based techniques, but the most\naccurate one for MBD-seq data requires additional data from an SssI-treated Control experiment.\nResults: Using our previous characterizations of Methyl-CpG/MBD2 binding in the context of an MBD pulldown\nexperiment, we build a model of expected MBD pulldown reads as drawn from SssI-treated DNA. We use the program\nBayMeth to evaluate the effectiveness of this model by substituting calculated SssI Control data for the observed SssI\nControl data. By comparing methylation predictions against those from an RRBS data set, we find that BayMeth run\nwith our modeled SssI Control data performs better than BayMeth run with observed SssI Control data, on both 100\nbp and 10 bp windows. Adapting the model to an external data set solely by changing the average fragment length,\nour calculated data still informs the BayMeth program to a similar level as observed data in predicting methylation\nstate on a pulldown data set with matching WGBS estimates.\nConclusion: In both internal and external MBD pulldown data sets tested in this study, BayMeth used with our\nmodeled pulldown coverage performs better than BayMeth run without the inclusion of any estimate of SssI Control\npulldown, and is comparable to - and in some cases better than -using observed SssI Control data with the BayMeth\nprogram. Thus, our MBD pulldown alignment model can improve methylation predictions without the need to\nperform additional control experiments.
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