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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Autores principales:	Shian Su, Quentin Gouil, Marnie E Blewitt, Dianne Cook, Peter F Hickey, Matthew E Ritchie
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/e66b1c33de7c465ab6b5201c09ba4d01
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spelling	oai:doaj.org-article:e66b1c33de7c465ab6b5201c09ba4d012021-12-02T19:57:41ZNanoMethViz: An R/Bioconductor package for visualizing long-read methylation data.1553-734X1553-735810.1371/journal.pcbi.1009524https://doaj.org/article/e66b1c33de7c465ab6b5201c09ba4d012021-10-01T00:00:00Zhttps://doi.org/10.1371/journal.pcbi.1009524https://doaj.org/toc/1553-734Xhttps://doaj.org/toc/1553-7358A 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.Shian SuQuentin GouilMarnie E BlewittDianne CookPeter F HickeyMatthew E RitchiePublic Library of Science (PLoS)articleBiology (General)QH301-705.5ENPLoS Computational Biology, Vol 17, Iss 10, p e1009524 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Biology (General) QH301-705.5
spellingShingle	Biology (General) QH301-705.5 Shian Su Quentin Gouil Marnie E Blewitt Dianne Cook Peter F Hickey Matthew E Ritchie NanoMethViz: An R/Bioconductor package for visualizing long-read methylation data.
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.
format	article
author	Shian Su Quentin Gouil Marnie E Blewitt Dianne Cook Peter F Hickey Matthew E Ritchie
author_facet	Shian Su Quentin Gouil Marnie E Blewitt Dianne Cook Peter F Hickey Matthew E Ritchie
author_sort	Shian Su
title	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_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_sort	nanomethviz: an r/bioconductor package for visualizing long-read methylation data.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/e66b1c33de7c465ab6b5201c09ba4d01
work_keys_str_mv	AT shiansu nanomethvizanrbioconductorpackageforvisualizinglongreadmethylationdata AT quentingouil nanomethvizanrbioconductorpackageforvisualizinglongreadmethylationdata AT marnieeblewitt nanomethvizanrbioconductorpackageforvisualizinglongreadmethylationdata AT diannecook nanomethvizanrbioconductorpackageforvisualizinglongreadmethylationdata AT peterfhickey nanomethvizanrbioconductorpackageforvisualizinglongreadmethylationdata AT mattheweritchie nanomethvizanrbioconductorpackageforvisualizinglongreadmethylationdata
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NanoMethViz: An R/Bioconductor package for visualizing long-read methylation data.

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