# MethylModes: computationally efficient detection of multimodal distributions in DNA methylation data

## TL;DR

MethylModes is a tool that efficiently detects multimodal DNA methylation patterns at individual CpG sites, helping researchers identify potential confounding factors in methylation data.

## Contribution

MethylModes introduces a computationally efficient algorithm for detecting multimodal distributions in DNA methylation data using kernel smoothing and parallel processing.

## Key findings

- MethylModes can be easily integrated into existing DNA methylation quality control pipelines.
- The algorithm efficiently identifies peaks in methylation data at genome scale through parallel processing.
- A case study demonstrates MethylModes' implementation in large-scale health studies.

## Abstract

MethylModes is an R package and Shiny application to identify multimodal distributions in human DNA methylation at individual CpG sites. Multimodal distributions, which can be the result of nearby genetic variation, environmental exposures, or assay artifacts, are susceptible to confounding and important to identify for methylation analysis. MethylModes is easily incorporated into existing quality control pipelines of array-based DNA methylation data. The underlying algorithm uses kernel smoothing of probe-level data to locate the number and location of peaks. The algorithm can be parallelized across probes for efficient implementation at genome-scale. We provide a case study implementation of MethylModes in the Health and Retirement Study as well as the Airwave Health Monitoring Study.

MethylModes is available on GitHub at https://github.com/lutiffan/methylModes as an R package wrapping an R Shiny application. We include a toy dataset to validate installation. The codebase is also published on Zenodo at https://doi.org/10.5281/zenodo.17448517.