The general approach in normalization using multiple genes is the selection of reference genes among candidate genes, which have been commonly used for normalization. However a group of genes selected among the commonly used reference genes may not be a suitable reference gene set for each experimental condition. ACT1, which is a commonly used reference gene for Saccharomyces cerevisiae, was validated to be a suitable reference gene to normalize the expression of genes involved in central carbon metabolism in response to a short-term glucose pulse [6]. On the other hand, the use of ACT1 and other commonly used reference genes (PDA1, TDH3, RDN18) was invalidated as internal control for quantitative analysis of gene expression in the yeast S. cerevisiae during long- term growth on glucose [4]. This fact clearly indicates the necessity of the validation of the suitability of reference genes under specific experimental conditions and steers us towards the determination of a candidate reference gene set among novel genes instead of commonly used ones, which is suitable for use under the conditions of interest. A reasonably successful approach in the compilation of a candidate reference gene set is to select genes from a genome-wide background and to use a large number of microarray datasets for a specific condition and for specific tissues/organisms [1,2,7]. Since the determination of a suitable candidate gene set creates a big concern in dynamic expression profiling studies due to the multidimensional and dynamic nature of the experiments, microarray datasets could be used to identify the transcripts, which display stable expression with respect to time as well as to different genetic and environmental conditions. However, a vigilant human-mediated research is required for the selection of the microarray datasets, which will be used for the identification of the candidate reference gene set. Following the compilation of the candidate reference gene set, the second key point is the determination of the most stable genes among the candidates under selected conditions. Several software tools were developed for the verification of the suitability of the candidate reference genes such as geNorm [5], NormFinder [8] and Bestkeeper [9]. As designated by the geNorm algorithm, the expression ratio of two suitable reference genes should be constant across all samples. Therefore the inclusion of two correlated genes in the list of candidates may lead to false positive results due to the similarity in their expression profiles. On the other hand, the NormFinder algorithm depends on statistical linear mixed-effects modelling, which accounts for the overall variation in the expression of the candidate reference genes and also for the variation between the sample groups. NormFinder was found to be less sensitive in the case of correlated gene sets. Although the definition of ‘‘stability’’ differs somehow among these algorithms, there are many studies reporting similar results in terms of stability when either one of these software were used [3,10,11]. The aim of the present study was to determine a set of candidate reference genes to analyze the dynamic transcriptional response of selected genes to changing environmental conditions by real-time RT-qPCR in S. cerevisiae. For this purpose, a set of candidate reference genes, which can be used for the dynamic expression profiling studies in S. cerevisiae, was determined for the first time using 31 publicly available and independently generated time- dependent transcriptome data. This candidate reference gene set was then verified using two separate datasets to identify the most stable reference genes under these two selected environmental conditions and the stability of the reference sets were confirmed by expression analysis of the specific perturbation responsive genes. The stable reference gene sets comprised of both previously reported reference genes and genes that were identified as reference for the first time in this study. Yeast Reference Genes in Dynamic RT-qPCR Analysis Moreover these newly identified genes were determined to be more stable than the common reference candidates, used either individually or as a multiple gene set, presenting an improvement in the determination of gene expression profiles. This study revealed that the selection of reference genes among the candidate reference gene set, which was identified in this present study, outperformed the use of reference genes that were frequently reported in the literature. This novel approach allows the reduction of multiple parameters that would be frequently encountered in dynamic gene expression studies down to only one parameter, time, for the determination of a suitable set of reference genes. The high flexibility of this method enables the use of this candidate reference gene set in the determination of reference genes under any experimental condition as long as the supplemented data is of dynamic nature. Results Identification of a candidate reference gene set 31 different time series datasets from experiments conducted using S. cerevisiae strains were collected from the publicly available database of functional genomics experiments; ArrayExpress [12] (Table S1). The coefficient of variation (CV), which is the ratio of standard deviation to mean, was used to compare the extent of variation among the expression levels of genes and therefore to determine the transcripts, which displayed a stable expression profile throughout different time points. The ranking of the CV values was defined as the stability profile in this study. Two different approaches were used to determine the candidate reference gene set based on stability profiles. In the first approach, each of the 31 datasets was analyzed separately in order to obtain the individual stability profiles. The top 100 stable genes were identified for each dataset. 4156 transcripts did never rank within the top 100 genes in any of these datasets and interestingly there was no common unique transcript, which appeared among the top 100 stable genes in all cases. TPI1 and ACT1 were among the 100 most stable transcripts in 81% of the cases (Figure S1). The 10 transcripts, TPI1, ACT1, TDH3, FBA1, CCW12, CDC19, ADH1, PGK1, GCN4 and PDC1, which ranked within the top 100 in more than 55% of the datasets, were selected as candidate reference genes. Among these genes ACT1, which encodes the single essential gene for actin, and TDH3, which encodes glyceraldehyde-3-phosphate dehydrogenase (GAPDH), were reported to be commonly used reference genes in yeast [13]. CCW12, the cell wall mannoprotein with a role in the maintenance of newly synthesized areas of cell wall; GCN4, the transcriptional activator of amino acid biosynthetic genes in response to amino acid starvation, and other genes functioning in the super pathway of glucose fermentation were identified in this candidate reference gene set [14]. As a second approach, the stability profiles of the genes were investigated by combining different datasets in order to identify the genes, which display less fluctuating expression profiles across different time points and under different experimental conditions. The expression values of the genes that are common in all datasets were used to construct the combined dataset. At this point a question of whether there were a prerequisite number of experiments to be included into this analysis arose. In order to observe the effect of the number of datasets on stability profiles of the genes, resampling from the data was carried out via bootstrapping. Random combinations of 31 datasets were generated. The stability profiles for each of these combinations were calculated and the similarities between these stability profiles and the overall stability profile, which was obtained by combining PLoS ONE | www.plosone.org 2 June 2012 | Volume 7 | Issue 6 | e38351