Identification of two putative reference genes from grapevine suitable for gene expression analysis in berry and related tissues derived from RNA-Seq data

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Abstract

Background: Data normalization is a key step in gene expression analysis by qPCR. Endogenous control genes are used to estimate variations and experimental errors occurring during sample preparation and expression measurements. However, the transcription level of the most commonly used reference genes can vary considerably in samples obtained from different individuals, tissues, developmental stages and under variable physiological conditions, resulting in a misinterpretation of the performance of the target gene(s). This issue has been scarcely approached in woody species such as grapevine. Results: A statistical criterion was applied to select a sub-set of 19 candidate reference genes from a total of 242 non-differentially expressed (NDE) genes derived from a RNA-Seq experiment comprising ca. 500 million reads obtained from 14 table-grape genotypes sampled at four phenological stages. From the 19 candidate reference genes, VvAIG1 (AvrRpt2-induced gene) and VvTCPB (T-complex 1 beta-like protein) were found to be the most stable ones after comparing the complete set of genotypes and phenological stages studied. This result was further validated by qPCR and geNorm analyses. Conclusions: Based on the evidence presented in this work, we propose to use the grapevine genes VvAIG1 or VvTCPB or both as a reference tool to normalize RNA expression in qPCR assays or other quantitative method intended to measure gene expression in berries and other tissues of this fruit crop, sampled at different developmental stages and physiological conditions. © 2013 González-Agüero et al.; licensee BioMed Central Ltd.

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González-Agüero, M., García-Rojas, M., Di Genova, A., Correa, J., Maass, A., Orellana, A., & Hinrichsen, P. (2013). Identification of two putative reference genes from grapevine suitable for gene expression analysis in berry and related tissues derived from RNA-Seq data. BMC Genomics, 14(1). https://doi.org/10.1186/1471-2164-14-878

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