Getting Started

Please see our paper for an overview of the method. See the WIMSi User Guide for installation and operation instructions (or the README files included in the download package). We have included a test dataset with the package to walk you through an initial analysis. Note that this implementation requires MATLAB and a Linux environment. A full list of prerequisites can be found in the User Guide or in the included README files.

Download the latest version here.

An Overview of the WIMSi Method

Genome-wide DNA methylation analyses find only modest correlations between differential methylation at gene promoters and expression. We hypothesized that stronger correlations have not been observed because existing methods oversimplify their representation of methylation data. We created a new approach to methylation analysis based on shape-similarity.

The Discovery Tool
WIMSi is a tool for discovering patterns of methylation that correlate with differential expression, with minimal prior assumptions about what patterns should exist in the data. We first represent differential methylation at each promoter as an interpolated curve, or methylation signature. This form allows us to compare methylation signatures using shape-similarity techniques. For regions with missing data, the interpolation also provides a reasonable guess for the methylation level. Using a shape-similarity metric known as the coupling distance (a discrete version of the Fréchet distance), we identify groups of genes with similar methylation signatures that also have corresponding expression changes.

Initial Results
We used WIMSi on a variety of publically-available datasets for which genome-wide, high-resolution methylation and expression data are available. We found a diversity of correlative patterns and strong relationships between methylation and expression.

The Gene List Tool
A primary goal of genome-wide DNA methylation studies is to generate a list of genes for which methylation change putatively correlates with expression change. We created an extension of the pattern discovery tool designed to generate high quality gene lists from a new sample pair. Compared to existing approaches, our method generates a longer list of genes with higher quality associations between differential methylation and expression. An implementation of both the discovery and gene list tools can be downloaded here.

Contact Us

  • For further information or questions, please send email to
  • To join our mailing list for announcements concerning future updates please sign up here.

Quick Links

External Links

Citation Information

If you use this work in your research, please cite our publication:

VanderKraats ND, Decker KF, Hiken JF, Edwards JR. (2013) “Method to discover high-resolution patterns of differential DNA methylation that associate with transcription changes.” Nucleic Acids Res. [Epub ahed of print] doi: 10.1093/nar/gkt482

Contact Information

Please send questions, comments, or bug reports to