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

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...

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Detalles Bibliográficos
Autores principales: Su, Shian, Gouil, Quentin, Blewitt, Marnie E., Cook, Dianne, Hickey, Peter F., Ritchie, Matthew E.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8568149/
https://www.ncbi.nlm.nih.gov/pubmed/34695109
http://dx.doi.org/10.1371/journal.pcbi.1009524
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author Su, Shian
Gouil, Quentin
Blewitt, Marnie E.
Cook, Dianne
Hickey, Peter F.
Ritchie, Matthew E.
author_facet Su, Shian
Gouil, Quentin
Blewitt, Marnie E.
Cook, Dianne
Hickey, Peter F.
Ritchie, Matthew E.
author_sort Su, Shian
collection PubMed
description 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 https://bioconductor.org/packages/NanoMethViz.
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spelling pubmed-85681492021-11-05 NanoMethViz: An R/Bioconductor package for visualizing long-read methylation data Su, Shian Gouil, Quentin Blewitt, Marnie E. Cook, Dianne Hickey, Peter F. Ritchie, Matthew E. PLoS Comput Biol Research Article 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 https://bioconductor.org/packages/NanoMethViz. Public Library of Science 2021-10-25 /pmc/articles/PMC8568149/ /pubmed/34695109 http://dx.doi.org/10.1371/journal.pcbi.1009524 Text en © 2021 Su et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Su, Shian
Gouil, Quentin
Blewitt, Marnie E.
Cook, Dianne
Hickey, Peter F.
Ritchie, Matthew E.
NanoMethViz: An R/Bioconductor package for visualizing long-read methylation data
title NanoMethViz: An R/Bioconductor package for visualizing long-read methylation data
title_full NanoMethViz: An R/Bioconductor package for visualizing long-read methylation data
title_fullStr NanoMethViz: An R/Bioconductor package for visualizing long-read methylation data
title_full_unstemmed NanoMethViz: An R/Bioconductor package for visualizing long-read methylation data
title_short NanoMethViz: An R/Bioconductor package for visualizing long-read methylation data
title_sort nanomethviz: an r/bioconductor package for visualizing long-read methylation data
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8568149/
https://www.ncbi.nlm.nih.gov/pubmed/34695109
http://dx.doi.org/10.1371/journal.pcbi.1009524
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