MOTIVATION: Both β-value and M-value have been used as metrics to measure methylation levels. The M-value is more statistically valid for the differential analysis of methylation levels. However, the β-value is much more biologically interpretable and needs to be reported when M-value method is used for conducting differential methylation analysis. There is an urgent need to know how to interpret the degree of differential methylation from the M-value. In M-value linear regression model, differential methylation M-value ΔM can be easily obtained from the coefficient estimate, but it is not straightforward to get the differential methylation β-value, Δβ since it cannot be obtained from the coefficient alone. RESULTS: To fill the gap, we have built a bridge to connect the statistically sound M-value linear regression model and the biologically interpretable Δβ. In this article, three methods were proposed to calculate differential methylation values, Δβ from M-value linear regression model and compared with the Δβ directly obtained from β-value linear regression model. We showed that under the condition that M-value linear regression model is correct, the method M-model-coef is the best among the four methods. M-model-M-mean method works very well too. If the coefficients α0, α2,…αp are not given (as 'MethLAB' package), the M-model-M-mean method should be used. The Δβ directly obtained from β-value linear regression model can give very biased results, especially when M-values are not in (-2, 2) or β-values are not in (0.2, 0.8). AVAILABILITY AND IMPLEMENTATION: The dataset for example is available at the National Center for Biotechnology Information Gene Expression Omnibus repository, GSE104778. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

MOTIVATION: 5-Methylcytosine (5mC) and 5-hydroxymethylcytosine (5hmC) are important epigenetic regulators of gene expression. 5mC and 5hmC levels can be computationally inferred at single base resolution using sequencing or array data from paired DNA samples that have undergone bisulfite and oxidative bisulfite conversion. Current estimation methods have been shown to produce irregular estimates of 5hmC level or are extremely computation intensive. RESULTS: We developed an efficient method oxBS-MLE based on binomial modeling of paired bisulfite and oxidative bisulfite data from sequencing or array analysis. Evaluation in several datasets showed that it outperformed alternative methods in estimate accuracy and computation speed. AVAILABILITY AND IMPLEMENTATION: oxBS-MLE is implemented in Bioconductor package ENmix. CONTACT: [email protected] information: Supplementary data are available at Bioinformatics online.