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Enhanced Reduced Representation Bisulfite Sequencing for Assessment of DNA Methylation at Base Pair Resolution

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A streamlined workflow for long-read DNA methylation analysis with NanoMethViz and Bioconductor.

Shian Su¹, Lucinda Xiao¹, James Lancaster¹

¹Walter and Eliza Hall Institute of Medical Research, Parkville, Victoria, Australia.

|March 17, 2025

Summary

NanoMethViz simplifies long-read DNA methylation analysis, enabling visualization and discovery of differentially methylated regions. This tool facilitates epigenetic research by processing modBAM files for gene regulation and disease studies.

Keywords:

Bioconductor DNA methylation Differential methylation Epigenetics Long-read sequencing

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Area of Science:

Epigenetics
Bioinformatics
Genomics

Background:

Long-read sequencing advances epigenetics with single-base resolution DNA modification detection.
DNA methylation plays crucial roles in gene regulation, imprinting, and disease.
Specialized software is needed for analyzing unique long-read data characteristics like modBAM format and extended read lengths.

Purpose of the Study:

To present NanoMethViz, a package for analyzing long-read DNA methylation data.
To demonstrate a workflow for converting modBAM files for downstream analysis.
To showcase exploratory data analysis and differentially methylated region (DMR) discovery using NanoMethViz and dmrseq.

Main Methods:

Loading and visualizing long-read methylation data with NanoMethViz.
Converting modBAM files for compatibility with Bioconductor tools (bsseq, DSS, dmrseq, edgeR).
Applying dmrseq for DMR identification and integrating results with gene-level visualizations.

Main Results:

Exploratory analysis revealed sample differences and aggregate methylation profiles.
DMR analysis identified regions associated with known imprinted genes.
Visualizations provided single-read resolution of methylation patterns on parental alleles.

Conclusions:

NanoMethViz offers a streamlined workflow for long-read methylation data analysis.
The package supports flexible integration with various statistical tools for DMR discovery.
This approach enhances the study of DNA methylation in gene regulation, imprinting, and disease.