diffMONT

diffMONT is a python-based tool for predicting methylation-specific PCR (MSP) primers, based on Nanopore sequencing data. Given a merged bedmethyl file, diffMONT detects methylation-specific PCR primer regions, which can distinguish between two groups of samples (originally cancer patients vs. healthy controls).

Install:

We recommend to use linux and miniconda for the enviroment management

1. Download and install Conda.

2. Download the enviroment yml file diffMONT.yml

3. Create the Conda environment.

   conda env create -f diffMONT.yml

4. Activate the Conda environment. You will need to activate the Conda environment in each terminal in which you want to use diffMONT.

   conda activate diffMONT

Run:

Input for diffMONT is a bedmethyl file, sorted by genomic position, with an additional column containing the sample name. This file can be generated using the script preprocess.sh, which extracts the sample name from the file name. The most basic usage of diffMONT is via:

python diffMONT.py --bedmethylFile mergedMethylation.bed --controls ctr_1 ctr_2 --tumors tmr_1 tmr_2 --outfolder results/

The output of diffMONT is a list of predicted MSP regions, containing information for the forward and reverse primer. This list might contain overlapping MSP regions, which can be collapsed with the script groupPCRproducts.py.

Required parameters:

diffMONT has four required parameters.

• --bedmethylFile
  The merged bedmethyl file should follow the bedmethyl bed9+2 format, with an additional last column specifying the sample names.
• --controls
  The sample names of the control group, separated by space.
• --tumors
  The sample names of the non-control group, separated by space.
• outfolder Path to the where the results will be stored.

Optional parameters:

Optional parameters exist to adapt the requirements for the predicted MSP regions based on the user's requirements.

command	function
--maxMethControl	maximum average methylation allowed for the control sample
--minCpGs	minimum amount of differentially methylated cytosines in primer
--minPrimerLength	minimum length required for primers
--maxPrimerLength	maximum length required for primers
--minAmpliconLength	minimum length required for MSP regions
--maxAmpliconLength	maximum length required for MSP regions

Additionally, with the optional parameters --boxplotData and --primerData it is possible to continue the work from intermediate results. Further functionalities can be used by adding --annotation followed by an Ensembl annotation file, which will be screened for genes overlapping the predicted MSP regions (this will result in an additional output column). If the parameter --reference is set, followed by the reference genome, additional statistics like GC-content will be calculated and reported in additional output columns.

Output of diffMONT:

The output file pcrProducts.tsv contains data for both predicted primers (fw and rev), and the total MSP region. The result data is stored in the following output columns:

column name	meaning
chromosome	chromosome on which the MSP region was identified
strand	strand on which the MSP region was identified
start_fw	first poistion of the first primer in pair
end_fw	second poistion of the first primer in pair
CGs_fw	number of CpGs of interest in the first primer in pair
length_fw	length of the first primer in pair
score_fw	score of the first primer in pair
start_rev	first poistion of the second primer in pair
end_rev	second poistion of the second primer in pair
CGs_rev	number of CpGs of interest in the second primer in pair
length_rev	length of the second primer in pair
score_rev	score of the second primer in pair
length_product	length of the predicted MSP region (end_rev-start_fw)
score_product	MSP region score (score_fw + score_rev)
cov_contr	mean coverage of control samples over both primers
cov_tumor	mean coverage of control samples over both primers
meth_contr	mean methylation of control samples over both primers
meth_tumor	mean methylation of control samples over both primers

Exemplary data for testing:

diffMONT can be either used on publicly available data, e.g. the Benchmark Dataset RRMS 2022.07 from Oxford Nanopore Technologies (ONT) available under AWS (s3://ont-open-data/rrms_2022.07/) and described by ONT. Additionally, a minimal example of a bedmethyl file is available in the folder /example_data/.

Application and Publication

For a detailed description and benchmarking of diffMONT, see our preprint on BioRxiv (2025).
For the application of diffMONT on clinical data, see our publication in Clinical Epigenetics (2025).

Citation

When using diffMONT helps you, please cite:
Meyer et al., "Nanopore sequencing-derived methylation biomarker prediction for methylation-specific PCR in patients with head and neck squamous cell carcinoma", Clinical Epigenetics (2025)