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rosettR: protocol and software for seedling area and growth analysis
BACKGROUND: Growth is an important parameter to consider when studying the impact of treatments or mutations on plant physiology. Leaf area and growth rates can be estimated efficiently from images of plants, but the experiment setup, image analysis, and statistical evaluation can be laborious, ofte...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5353781/ https://www.ncbi.nlm.nih.gov/pubmed/28331535 http://dx.doi.org/10.1186/s13007-017-0163-9 |
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author | Tomé, Filipa Jansseune, Karel Saey, Bernadette Grundy, Jack Vandenbroucke, Korneel Hannah, Matthew A. Redestig, Henning |
author_facet | Tomé, Filipa Jansseune, Karel Saey, Bernadette Grundy, Jack Vandenbroucke, Korneel Hannah, Matthew A. Redestig, Henning |
author_sort | Tomé, Filipa |
collection | PubMed |
description | BACKGROUND: Growth is an important parameter to consider when studying the impact of treatments or mutations on plant physiology. Leaf area and growth rates can be estimated efficiently from images of plants, but the experiment setup, image analysis, and statistical evaluation can be laborious, often requiring substantial manual effort and programming skills. RESULTS: Here we present rosettR, a non-destructive and high-throughput phenotyping protocol for the measurement of total rosette area of seedlings grown in plates in sterile conditions. We demonstrate that our protocol can be used to accurately detect growth differences among different genotypes and in response to light regimes and osmotic stress. rosettR is implemented as a package for the statistical computing software R and provides easy to use functions to design an experiment, analyze the images, and generate reports on quality control as well as a final comparison across genotypes and applied treatments. Experiment procedures are included as part of the package documentation. CONCLUSIONS: Using rosettR it is straight-forward to perform accurate, reproducible measurements of rosette area and relative growth rate with high-throughput using inexpensive equipment. Suitable applications include screening mutant populations for growth phenotypes visible at early growth stages and profiling different genotypes in a wide variety of treatments. |
format | Online Article Text |
id | pubmed-5353781 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-53537812017-03-22 rosettR: protocol and software for seedling area and growth analysis Tomé, Filipa Jansseune, Karel Saey, Bernadette Grundy, Jack Vandenbroucke, Korneel Hannah, Matthew A. Redestig, Henning Plant Methods Methodology BACKGROUND: Growth is an important parameter to consider when studying the impact of treatments or mutations on plant physiology. Leaf area and growth rates can be estimated efficiently from images of plants, but the experiment setup, image analysis, and statistical evaluation can be laborious, often requiring substantial manual effort and programming skills. RESULTS: Here we present rosettR, a non-destructive and high-throughput phenotyping protocol for the measurement of total rosette area of seedlings grown in plates in sterile conditions. We demonstrate that our protocol can be used to accurately detect growth differences among different genotypes and in response to light regimes and osmotic stress. rosettR is implemented as a package for the statistical computing software R and provides easy to use functions to design an experiment, analyze the images, and generate reports on quality control as well as a final comparison across genotypes and applied treatments. Experiment procedures are included as part of the package documentation. CONCLUSIONS: Using rosettR it is straight-forward to perform accurate, reproducible measurements of rosette area and relative growth rate with high-throughput using inexpensive equipment. Suitable applications include screening mutant populations for growth phenotypes visible at early growth stages and profiling different genotypes in a wide variety of treatments. BioMed Central 2017-03-15 /pmc/articles/PMC5353781/ /pubmed/28331535 http://dx.doi.org/10.1186/s13007-017-0163-9 Text en © The Author(s) 2017 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Methodology Tomé, Filipa Jansseune, Karel Saey, Bernadette Grundy, Jack Vandenbroucke, Korneel Hannah, Matthew A. Redestig, Henning rosettR: protocol and software for seedling area and growth analysis |
title | rosettR: protocol and software for seedling area and growth analysis |
title_full | rosettR: protocol and software for seedling area and growth analysis |
title_fullStr | rosettR: protocol and software for seedling area and growth analysis |
title_full_unstemmed | rosettR: protocol and software for seedling area and growth analysis |
title_short | rosettR: protocol and software for seedling area and growth analysis |
title_sort | rosettr: protocol and software for seedling area and growth analysis |
topic | Methodology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5353781/ https://www.ncbi.nlm.nih.gov/pubmed/28331535 http://dx.doi.org/10.1186/s13007-017-0163-9 |
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