BPRMeth: modelling DNA methylation profiles

BioC status DOI

The aim of BPRMeth is to extract higher order features associated with the shape of methylation profiles across a defined genomic region. Using these higher order features across promoter-proximal regions, BPRMeth provides a powerful machine learning predictor of gene expression. Check the vignette on how to use the package. Modelling details for the different models can be found online:

The original implementation has now 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 and bulk sequencing experiments and methylation arrays.


To get the latest development version from Github:

# install.packages("devtools")
devtools::install_github("andreaskapou/BPRMeth", build_vignettes = TRUE)

Or install from the stable release version from Bioconductor

## try http:// if https:// URLs are not supported
if (!requireNamespace("BiocManager", quietly=TRUE))

You can the check the vignette on how to use the package:


Clang / fopenmp error for Mac users

If you get the following error when installing the package:

clang: error: unsupported option '-fopenmp'

try the following:

brew install llvm
brew install boost
brew install homebrew/science/hdf5 --enable-cxx

mkdir -p ~/.R
vim ~/.R/Makevars

## Paste the following commands
# The following statements are required to use the clang4 binary
# End clang4 inclusion statements

These commands will point R to the new version of clang.

BPRMeth workflow

The diagram below shows an overview of the pre-processing and analysis workflow in BPRMeth, together with example output graphs.

Diagram outlining the schematic workflow of BPRMeth (left) with example output graphs (right).


Kapourani, C.-A. and Sanguinetti, G. (2016). Higher order methylation features for clustering and prediction in epigenomic studies. Bioinformatics 32 (17), i405-i412. (Best Paper Award in ECCB 2016).

Try the BPRMeth package in your browser

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BPRMeth documentation built on Nov. 8, 2020, 5:54 p.m.