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A tool for rapid, automated characterization of population epigenomics in plants
Epigenetic variation in plant populations is an important factor in determining phenotype and adaptation to the environment. However, while advances have been made in the molecular and computational methods to analyze the methylation status of a given sample of DNA, tools to profile and compare the...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10435466/ https://www.ncbi.nlm.nih.gov/pubmed/37591855 http://dx.doi.org/10.1038/s41598-023-38356-7 |
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author | Colicchio, Jack M. Amstutz, Cynthia L. Garcia, Nelson Prabhu, Keerthana N. Cairns, Thomas M. Akman, Melis Gottilla, Thomas Gollery, Twyla Stricklin, Shawn L. Bayer, Travis S. |
author_facet | Colicchio, Jack M. Amstutz, Cynthia L. Garcia, Nelson Prabhu, Keerthana N. Cairns, Thomas M. Akman, Melis Gottilla, Thomas Gollery, Twyla Stricklin, Shawn L. Bayer, Travis S. |
author_sort | Colicchio, Jack M. |
collection | PubMed |
description | Epigenetic variation in plant populations is an important factor in determining phenotype and adaptation to the environment. However, while advances have been made in the molecular and computational methods to analyze the methylation status of a given sample of DNA, tools to profile and compare the methylomes of multiple individual plants or groups of plants at high resolution and low cost are lacking. Here, we describe a computational approach and R package (sounDMR) that leverages the benefits of long read nanopore sequencing to enable robust identification of differential methylation from complex experimental designs, as well as assess the variability within treatment groups and identify individual plants of interest. We demonstrate the utility of this approach by profiling a population of Arabidopsis thaliana exposed to a demethylating agent and identify genomic regions of high epigenetic variability between individuals. Given the low cost of nanopore sequencing devices and the ease of sample preparation, these results show that high resolution epigenetic profiling of plant populations can be made more broadly accessible in plant breeding and biotechnology. |
format | Online Article Text |
id | pubmed-10435466 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-104354662023-08-19 A tool for rapid, automated characterization of population epigenomics in plants Colicchio, Jack M. Amstutz, Cynthia L. Garcia, Nelson Prabhu, Keerthana N. Cairns, Thomas M. Akman, Melis Gottilla, Thomas Gollery, Twyla Stricklin, Shawn L. Bayer, Travis S. Sci Rep Article Epigenetic variation in plant populations is an important factor in determining phenotype and adaptation to the environment. However, while advances have been made in the molecular and computational methods to analyze the methylation status of a given sample of DNA, tools to profile and compare the methylomes of multiple individual plants or groups of plants at high resolution and low cost are lacking. Here, we describe a computational approach and R package (sounDMR) that leverages the benefits of long read nanopore sequencing to enable robust identification of differential methylation from complex experimental designs, as well as assess the variability within treatment groups and identify individual plants of interest. We demonstrate the utility of this approach by profiling a population of Arabidopsis thaliana exposed to a demethylating agent and identify genomic regions of high epigenetic variability between individuals. Given the low cost of nanopore sequencing devices and the ease of sample preparation, these results show that high resolution epigenetic profiling of plant populations can be made more broadly accessible in plant breeding and biotechnology. Nature Publishing Group UK 2023-08-17 /pmc/articles/PMC10435466/ /pubmed/37591855 http://dx.doi.org/10.1038/s41598-023-38356-7 Text en © The Author(s) 2023, corrected publication 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Colicchio, Jack M. Amstutz, Cynthia L. Garcia, Nelson Prabhu, Keerthana N. Cairns, Thomas M. Akman, Melis Gottilla, Thomas Gollery, Twyla Stricklin, Shawn L. Bayer, Travis S. A tool for rapid, automated characterization of population epigenomics in plants |
title | A tool for rapid, automated characterization of population epigenomics in plants |
title_full | A tool for rapid, automated characterization of population epigenomics in plants |
title_fullStr | A tool for rapid, automated characterization of population epigenomics in plants |
title_full_unstemmed | A tool for rapid, automated characterization of population epigenomics in plants |
title_short | A tool for rapid, automated characterization of population epigenomics in plants |
title_sort | tool for rapid, automated characterization of population epigenomics in plants |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10435466/ https://www.ncbi.nlm.nih.gov/pubmed/37591855 http://dx.doi.org/10.1038/s41598-023-38356-7 |
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