NanoMethViz: an R/Bioconductor package for visualizing long-read methylation data

Shian Su, Quentin Gouil, Marnie E. Blewitt, Dianne Cook, Peter F. Hickey, Matthew E. Ritchie

Research output: Contribution to journalArticleResearchpeer-review

6 Citations (Scopus)


A key benefit of long-read nanopore sequencing technology is the ability to detect modified DNA bases, such as 5-methylcytosine. The lack of R/Bioconductor tools for the effective visualization of nanopore methylation profiles between samples from different experimental groups led us to develop the NanoMethViz R package. Our software can handle methylation output generated from a range of different methylation callers and manages large datasets using a compressed data format. To fully explore the methylation patterns in a dataset, NanoMethViz allows plotting of data at various resolutions. At the sample-level, we use dimensionality reduction to look at the relationships between methylation profiles in an unsupervised way. We visualize methylation profiles of classes of features such as genes or CpG islands by scaling them to relative positions and aggregating their profiles. At the finest resolution, we visualize methylation patterns across individual reads along the genome using the spaghetti plot and heatmaps, allowing users to explore particular genes or genomic regions of interest. In summary, our software makes the handling of methylation signal more convenient, expands upon the visualization options for nanopore data and works seamlessly with existing methylation analysis tools available in the Bioconductor project. Our software is available at

Original languageEnglish
Article numbere1009524
Number of pages9
JournalPLoS Computational Biology
Issue number10
Publication statusPublished - Oct 2021

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