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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...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2021
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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. |
format | Online Article Text |
id | pubmed-8568149 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
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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