# BPRMeth: modelling DNA methylation profiles
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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: [http://rpubs.com/cakapourani](http://rpubs.com/cakapourani).
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:
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
## 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).](inst/figures/bprmeth-workflow.png)
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)**.