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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...

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Autores principales: Tomé, Filipa, Jansseune, Karel, Saey, Bernadette, Grundy, Jack, Vandenbroucke, Korneel, Hannah, Matthew A., Redestig, Henning
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2017
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.
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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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