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Ranking genomic features using an information-theoretic measure of epigenetic discordance
Garrett Jenkinson1,2,3, Jordi Abante1, Michael A Koldobskiy2,4
1Whitaker Biomedical Engineering Institute, Johns Hopkins University, Baltimore, MD, USA.
BMC Bioinformatics
|April 10, 2019
Summary
This study introduces a new computational method to identify and rank genomic features with significant DNA methylation differences between conditions. The approach rigorously accounts for various data complexities, enabling robust epigenetic dysregulation analysis in diseases like cancer.
Area of Science:
- Epigenetics
- Genomics
- Computational Biology
Background:
- DNA methylation is a key epigenetic regulator of gene expression, crucial for cellular function.
- Disruptions in DNA methylation patterns are linked to various human diseases, including cancer.
- Existing methods struggle to identify statistically significant genomic features with variable lengths and missing data.
Purpose of the Study:
- To develop a novel computational approach for statistically assessing and ranking DNA methylation discordance in genomic features.
- To address limitations of previous methods by handling variable feature lengths and missing data.
- To provide a tool for identifying epigenetically dysregulated features in comparative studies.
Main Methods:
- A hypothesis testing framework is established to compute p- and q-values for genomic features.
- Mutual information and Jensen-Shannon distances are used to derive a novel test statistic.
- Generalized additive regression models estimate the null distribution of the test statistic based on feature length, accommodating data variability.
Main Results:
- The new method successfully computes statistical significance for DNA methylation discordance across diverse genomic features.
- It effectively handles biological, statistical, and technical variability, alongside variable feature lengths and missing data.
- Application to normal/cancer data from healthy fetal tissue and pediatric high-grade glioma demonstrates its utility in exploratory methylation studies.
Conclusions:
- This work presents the first computational tool for statistically testing and ranking genomic features based on DNA methylation discordance.
- The approach rigorously accounts for multiple sources of variability, including feature length and missing data.
- It facilitates the identification of epigenetically dysregulated features in comparative studies, aiding clinical and biological research.
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