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...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2016-03-01
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| Series: | Frontiers in Plant Science |
| Subjects: | |
| Online Access: | http://journal.frontiersin.org/Journal/10.3389/fpls.2016.00271/full |
| _version_ | 1818853110347464704 |
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| author | Jose V Die Belen eRoman Fernando eFlores Lisa Jeannine Rowland |
| author_facet | Jose V Die Belen eRoman Fernando eFlores Lisa Jeannine Rowland |
| author_sort | Jose V Die |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-12-19T07:31:36Z |
| format | Article |
| id | doaj.art-51b486ed09b34775a4721a8ab9c8504b |
| institution | Directory Open Access Journal |
| issn | 1664-462X |
| language | English |
| last_indexed | 2024-12-19T07:31:36Z |
| publishDate | 2016-03-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Plant Science |
| spelling | doaj.art-51b486ed09b34775a4721a8ab9c8504b2022-12-21T20:30:40ZengFrontiers Media S.A.Frontiers in Plant Science1664-462X2016-03-01710.3389/fpls.2016.00271173549Design and sampling plan optimization for RT-qPCR experiments in plants: a case study in blueberryJose V Die0Belen eRoman1Fernando eFlores2Lisa Jeannine Rowland3USDA-ARSIFAPAUniversidad de HuelvaUSDA-ARSThe 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.http://journal.frontiersin.org/Journal/10.3389/fpls.2016.00271/fullqPCRBlueberryReplicatesConfounding variationRT variability |
| spellingShingle | Jose V Die Belen eRoman Fernando eFlores Lisa Jeannine Rowland Design and sampling plan optimization for RT-qPCR experiments in plants: a case study in blueberry Frontiers in Plant Science qPCR Blueberry Replicates Confounding variation RT variability |
| 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 | qPCR Blueberry Replicates Confounding variation RT variability |
| url | http://journal.frontiersin.org/Journal/10.3389/fpls.2016.00271/full |
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