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A Whole-Transcriptome Approach to Evaluating Reference Genes for Quantitative Gene Expression Studies: A Case Study in Mimulus

While quantitative PCR (qPCR) is widely recognized as being among the most accurate methods for quantifying gene expression, it is highly dependent on the use of reliable, stably expressed reference genes. With the increased availability of high-throughput methods for measuring gene expression, whol...

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Detalles Bibliográficos
Autores principales: Stanton, Kimmy A., Edger, Patrick P., Puzey, Joshua R., Kinser, Taliesin, Cheng, Philip, Vernon, Daniel M., Forsthoefel, Nancy R., Cooley, Arielle M.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Genetics Society of America 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5386857/
https://www.ncbi.nlm.nih.gov/pubmed/28258113
http://dx.doi.org/10.1534/g3.116.038075
Descripción
Sumario:While quantitative PCR (qPCR) is widely recognized as being among the most accurate methods for quantifying gene expression, it is highly dependent on the use of reliable, stably expressed reference genes. With the increased availability of high-throughput methods for measuring gene expression, whole-transcriptome approaches may be increasingly utilized for reference gene selection and validation. In this study, RNA-seq was used to identify a set of novel qPCR reference genes and evaluate a panel of traditional “housekeeping” reference genes in two species of the evolutionary model plant genus Mimulus. More broadly, the methods proposed in this study can be used to harness the power of transcriptomes to identify appropriate reference genes for qPCR in any study organism, including emerging and nonmodel systems. We find that RNA-seq accurately estimates gene expression means in comparison to qPCR, and that expression means are robust to moderate environmental and genetic variation. However, measures of expression variability were only in agreement with qPCR for samples obtained from a shared environment. This result, along with transcriptome-wide comparisons, suggests that environmental changes have greater impacts on expression variability than on expression means. We discuss how this issue can be addressed through experimental design, and suggest that the ever-expanding pool of published transcriptomes represents a rich and low-cost resource for developing better reference genes for qPCR.