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Design and Sampling Plan Optimization for RT-qPCR Experiments in Plants: A Case Study in Blueberry

The qPCR assay has become a routine technology in plant biotechnology and agricultural research. It is unlikely to be technically improved, but there are still challenges which center around minimizing the variability in results and transparency when reporting technical data in support of the conclu...

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Autores principales: Die, Jose V., Roman, Belen, Flores, Fernando, Rowland, Lisa J.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4779984/
https://www.ncbi.nlm.nih.gov/pubmed/27014296
http://dx.doi.org/10.3389/fpls.2016.00271
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author Die, Jose V.
Roman, Belen
Flores, Fernando
Rowland, Lisa J.
author_facet Die, Jose V.
Roman, Belen
Flores, Fernando
Rowland, Lisa J.
author_sort Die, Jose V.
collection PubMed
description The qPCR assay has become a routine technology in plant biotechnology and agricultural research. It is unlikely to be technically improved, but there are still challenges which center around minimizing the variability in results and transparency when reporting technical data in support of the conclusions of a study. There are a number of aspects of the pre- and post-assay workflow that contribute to variability of results. Here, through the study of the introduction of error in qPCR measurements at different stages of the workflow, we describe the most important causes of technical variability in a case study using blueberry. In this study, we found that the stage for which increasing the number of replicates would be the most beneficial depends on the tissue used. For example, we would recommend the use of more RT replicates when working with leaf tissue, while the use of more sampling (RNA extraction) replicates would be recommended when working with stems or fruits to obtain the most optimal results. The use of more qPCR replicates provides the least benefit as it is the most reproducible step. By knowing the distribution of error over an entire experiment and the costs at each step, we have developed a script to identify the optimal sampling plan within the limits of a given budget. These findings should help plant scientists improve the design of qPCR experiments and refine their laboratory practices in order to conduct qPCR assays in a more reliable-manner to produce more consistent and reproducible data.
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spelling pubmed-47799842016-03-24 Design and Sampling Plan Optimization for RT-qPCR Experiments in Plants: A Case Study in Blueberry Die, Jose V. Roman, Belen Flores, Fernando Rowland, Lisa J. Front Plant Sci Plant Science The qPCR assay has become a routine technology in plant biotechnology and agricultural research. It is unlikely to be technically improved, but there are still challenges which center around minimizing the variability in results and transparency when reporting technical data in support of the conclusions of a study. There are a number of aspects of the pre- and post-assay workflow that contribute to variability of results. Here, through the study of the introduction of error in qPCR measurements at different stages of the workflow, we describe the most important causes of technical variability in a case study using blueberry. In this study, we found that the stage for which increasing the number of replicates would be the most beneficial depends on the tissue used. For example, we would recommend the use of more RT replicates when working with leaf tissue, while the use of more sampling (RNA extraction) replicates would be recommended when working with stems or fruits to obtain the most optimal results. The use of more qPCR replicates provides the least benefit as it is the most reproducible step. By knowing the distribution of error over an entire experiment and the costs at each step, we have developed a script to identify the optimal sampling plan within the limits of a given budget. These findings should help plant scientists improve the design of qPCR experiments and refine their laboratory practices in order to conduct qPCR assays in a more reliable-manner to produce more consistent and reproducible data. Frontiers Media S.A. 2016-03-07 /pmc/articles/PMC4779984/ /pubmed/27014296 http://dx.doi.org/10.3389/fpls.2016.00271 Text en Copyright © 2016 Die, Roman, Flores and Rowland. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Plant Science
Die, Jose V.
Roman, Belen
Flores, Fernando
Rowland, Lisa J.
Design and Sampling Plan Optimization for RT-qPCR Experiments in Plants: A Case Study in Blueberry
title Design and Sampling Plan Optimization for RT-qPCR Experiments in Plants: A Case Study in Blueberry
title_full Design and Sampling Plan Optimization for RT-qPCR Experiments in Plants: A Case Study in Blueberry
title_fullStr Design and Sampling Plan Optimization for RT-qPCR Experiments in Plants: A Case Study in Blueberry
title_full_unstemmed Design and Sampling Plan Optimization for RT-qPCR Experiments in Plants: A Case Study in Blueberry
title_short Design and Sampling Plan Optimization for RT-qPCR Experiments in Plants: A Case Study in Blueberry
title_sort design and sampling plan optimization for rt-qpcr experiments in plants: a case study in blueberry
topic Plant Science
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4779984/
https://www.ncbi.nlm.nih.gov/pubmed/27014296
http://dx.doi.org/10.3389/fpls.2016.00271
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