Our paper contains a complete overview of WIMSi:

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. 41:6816-6827. Full Text.

Download the latest version here.

 

WIMSi Basics

Background

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. 

 

Getting Started

Please see the WIMSi User Guide for installation and operation instructions (or the README files included in the download package). The package includes a test dataset to walk you through an initial analysis. Note that this implementation requires MATLAB and a Linux environment. We have developed, but not fully tested an R implementation that does not require MATLAB, please contact us if you are interested. A full list of prerequisites can be found in the User Guide or in the included README files. Download the latest version here.

 

Contact Us

  • For further information or questions, please send email to This email address is being protected from spambots. You need JavaScript enabled to view it.
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Copyright Information

Copyright 2013. The WIMSi package is copyright of Washington University, St. Louis, Missouri. All Rights Reserved.

 

Copying Permission Statement

WIMSi is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.

WIMSi is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.

You should have received a copy of the GNU General Public License along with WIMSi. If not, see http://www.gnu.org/licenses/.

 

If you are interested in other licensing options, please contact:

Office of Technology Management
Washington University in St. Louis
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Phone: +1 314.747.0920