# diffMONT: predicting methylation-specific PCR biomarkers based on nanopore sequencing data for clinical application

## TL;DR

diffMONT is a new tool that predicts DNA methylation regions suitable for PCR-based cancer diagnostics using nanopore sequencing data.

## Contribution

diffMONT is the first tool tailored for predicting methylation-specific PCR biomarkers directly from nanopore sequencing data.

## Key findings

- diffMONT predicts more specific hypermethylated regions compared to existing tools like metilene and DSS.
- The tool considers primer length, amplicon size, and methylation status to improve clinical relevance.
- diffMONT bridges the gap between methylation research and practical diagnostic applications.

## Abstract

DNA methylation serves as a key biomarker in clinical diagnostics, especially in cancer detection. With methylation-specific PCR (MSP), a widely used approach, patient samples can be screened fast and efficiently for differential methylation. During MSP, methylated regions are selectively amplified with specific primers. With nanopore sequencing, knowledge about DNA methylation is generated during direct DNA sequencing without needing pretreatment of the DNA. Multiple methods, mainly developed for whole-genome bisulfite sequencing (WGBS) data, exist to predict differentially methylated regions (DMRs) in the genome. However, the predicted DMRs are often very large and not sufficiently discriminating to generate meaningful results in MSP, creating a gap between theoretical cancer marker research and practical application, as no tool currently provides methylation difference predictions tailored for PCR-based diagnostics.

Here, we present diffMONT, a tool that predicts differentially methylated regions specifically suited for MSP primer design, enabling rapid translation into practical applications. diffMONT takes into account (i) the specific length of primer and amplicon regions, (ii) the fact that one condition should be unmethylated, and (iii) a minimal required amount of differentially methylated cytosines within the primer regions. We compared the results of diffMONT to metilene and DSS based on a publicly available nanopore sequencing dataset and show that the regions predicted by diffMONT are more specific toward hypermethylated regions. diffMONT accelerates the design of methylation-specific diagnostic assays, bridging the gap between theoretical research and clinical application.

The source code for diffMONT, an open-source Python-based tool, is available at https://github.com/rnajena/diffMONT/, with an archived release under https://zenodo.org/records/17641031.