Motivation: High-throughput measurements of DNA methylation are increasingly becoming a mainstay of biomedical investigations. While the methylation status of individual cytosines can sometimes be informative, several recent papers have shown that the functional role of DNA methylation is better captured by a quantitative analysis of the spatial variation of methylation across a genomic region.
Results: Here we present BPRMeth, a Bioconductor package which quantifies methylation profiles by generalised linear model regression (Kapourani and Sanguinetti, 2016). The original implementation has been enhanced in two important ways: we introduced a fast, variational inference approach which enables the quantification of Bayesian posterior confidence measures on the model, and we adapted the method to use several observation models, making it suitable for a diverse range of platforms including single-cell analyses and methylation arrays.
Availability: http://bioconductor.org/packages/BPRMeth or https://github.com/andreaskapou/BPRMeth.
Supplementary information: Supplementary material are available at Bioinformatics online.
- Journal Article