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dc.contributor.authorDie, Jose V.
dc.contributor.authorRomán, Belén
dc.contributor.authorFlores Gil, Fernando 
dc.contributor.authorRowland, Lisa Jeannine
dc.date.accessioned2016-04-08T07:55:02Z
dc.date.available2016-04-08T07:55:02Z
dc.date.issued2016
dc.identifier.citationDie, Jose V., Román, B., Flores Gil, F., Rowland, L.J.: "Design and sampling plan optimization for RT-qPCR experiments in plants : a case study in blueberry". Frontiers in Plant Science. Vol. 7, art. 271, (2016). DOI: 10.3389/fpls.2016.00271en_US
dc.identifier.issn1664-462X
dc.identifier.urihttp://hdl.handle.net/10272/11808
dc.description.abstractThe 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. [This Document is Protected by copyright and was first published by Frontiers. All rights reserved. it is reproduced with permission.]en_US
dc.language.isoengen_US
dc.publisherFrontiers Mediaen_US
dc.rightsAtribución-NoComercial-SinDerivadas 3.0 España*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/*
dc.subject.otherBlueberryen_US
dc.subject.otherConfounding variationen_US
dc.subject.otherqPCRen_US
dc.subject.otherReplicatesen_US
dc.subject.otherRT variabilityen_US
dc.titleDesign and sampling plan optimization for RT-qPCR experiments in plants : a case study in blueberryen_US
dc.typeinfo:eu-repo/semantics/articleen_US
dc.identifier.doi10.3389/fpls.2016.00271
dc.rights.accessRightsinfo:eu-repo/semantics/openAccessen_US


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