methyMnM was developed for jointly analyzing MeDIP-seq and MRE-seq data, which were derived from methylated DNA immunoprecipitation (MeDIP) experiments [Weber et al., 2005] followed by sequencing (MeDIP-seq) and methyl-sensitive restriction enzymes experiments for unmethylated CpGs (MRE-seq). We have implemented the methylMnM method via a set of R functions with the computational intensive parts written in C. The method consists three steps:

  1. Data Pre-processing:
    • Calculate the CpG count of each window.
    • Calculate the MRE CpG count of each window.
    • Calculate MeDIP-seq tag count of each window of control and treatment samples.
    • Calculate MRE-seq tag count of each window of control and treatment samples.
  2. Calculating p-values of each window by the methylMnM test.
  3. FDR control.

We used a real dataset to illustrate the usage of the methylMnM package. The program performs best under any Linux system.


Before installing methylMnM package, the user have to install another two required R packages, which can be done using the following commands.

> source("")
> biocLite("edgeR")
> biocLite("statmod")

It is worth noting that, currently, the package "edgeR" only runs under R version between 2.12.0 and 2.14.0.

Next, to install the methylMnM package into your R environment, start R and enter:

> source("")
> biocLite("methylMnM")

Then, the methylMnM package is ready to load.

> library(methylMnM)

Tutorial and Source Code

The manual of methylMnM is available here.

A tutorial of how to using methylMnM package is available here.

Download the source code of methylMnM package here.

DMRs in Roadmap Epigenomics project

Want to explore the Differential Methylated Regions(DMRs) discovered by methylMnM? Click here


Functional DNA methylation differences between tissues, cell types, and across individuals discovered using the M&M algorithm. Genome Res. 2013.

Genome CpG infomration

The CpG site and MRE-CpG site are also available for the following species: