Quantitative real-time PCR (qPCR) is generally used for measuring transcripts abundance due to its high sensitivity, specificity and broad quantification range for high throughput and accurate expression profiling of selected genes
. Also, qPCR analysis has become the most common method for verification of microarrays and RNA-Seq results
[2–4]. Besides being a powerful technique, qPCR has certain disadvantages such as the difficulties associated to the inappropriate data normalization, one of the most important aspects to solve
 in order to fit this technique for the study of a new organism, organ or tissue. The data normalization is a key stage to control the artifacts and experimental error occurring during sample preparation and the following experimental steps, ending with the data analysis. It has been shown that qPCR results are highly dependent on the reference genes chosen
, which explain the considerable effort applied into the validation of the gene(s) selected for the normalization stage, prior to extensive experimentation
. These housekeeping genes should not vary in their expression level considering the different tissues or cells under investigation, nor in response to any experimental treatment
Regardless of the experimental technique employed, appropriate normalization is essential for obtaining accurate and reliable quantifications of gene expression levels, especially when measuring small expression differences or when working with tissues of different histological origin
. The purpose of normalization is to correct variability associated with the various steps of the experimental procedure, such as differences in initial sample amount, RNA extraction recovery and integrity, efficiency on cDNA synthesis and differences in the overall transcriptional activity of the tissues or cells analyzed
. Among the numerous normalization approaches that have been proposed
[11, 12] the use of internal controls or reference genes has become the method of preference
[13, 14], because they potentially account for all of the sources of variability mentioned above. However, numerous studies have reported that the transcript quantity of the most commonly used reference genes can vary considerably under different developmental, physiological and experimental conditions
[11, 15–23]. Several reference genes are commonly used, such as elongation factor
[24, 25], actin
[26, 27], ubiquitin
[28, 29], and ribosomal units (18S or 28S rRNA)
[30–32]. However, several reports have demonstrated that transcript levels of these genes also vary considerably under different experimental conditions and consequently their suitability for gene expression studies must be evaluated case by case
[22, 33, 34]. This implies that a reference gene with stable expression in one organism may not be suitable for normalization of gene expression in another organism
[35, 36], or even in different experiments for the same species.
Many works have been carried out on animal models and in relation to human health
[37, 38], fields in which multiple reference genes for normalization of qPCR data have been described. However, similar reports are less abundant in plants
[10, 35, 39]. Czechowski et al.
 employed a new strategy for the identification of reference genes in Arabidopsis thaliana, based on the microarray data of Affymetrix (ATH1), and several new reference genes were revealed
. This list of Arabidopsis reference genes was successfully employed to search for reference genes by sequence homology in unrelated species such as Vitis vinifera. This approach resulted in a strategy that is based on the parallel use of a series of control genes and calculation of normalization factors using statistical algorithms
[8, 11, 41]. It is necessary to validate the expression stability of a candidate control gene in each experimental system prior to its use for normalization. In this regard, several free software applications such as geNorm
 or qBase
 are used in order to identify the best internal controls from a group of candidate normalization genes in a given set of biological samples.
To our knowledge, no investigations have been yet carried out for the identification of reference genes in table grape, one of the most important template fruit crops. In this work we used a data set obtained from a large RNA-Seq experiment of table grape segregants phenotypically and genetically diverse, belonging to a 'Ruby Seedless’ x 'Sultanina’ crossing, sampled at three phenotypic stages, anthesis, fruit-setting and berries of 6–8 mm diameter (the last one from plants treated or not with gibberellic acid). We focused the search of control genes evaluating the variability (or stability) in the expression of 19 genes selected from an initial set of 242 genes that showed a threshold stability level, comparing the four different developmental and physiological conditions. Two new reference genes, VvAIG1 (AvrRpt2-induced gene) and VvTCPB (T-complex 1 beta-like protein) were validated by qPCR and geNorm techniques and are presented as new housekeeping genes for table grape.