all 31 datasets, were assessed using the Pearson correlation coefficient (PCC). Average of the PCC values were calculated for the combinations having the same number of datasets. The average and the minimum of the PCC values of the combinations that have the same number of datasets were examined as a function of the number of datasets (Figure S2). The analysis of these combinations showed that both the average and the minimum PCC increased with the inclusion of increasing numbers of datasets. This observation indicated that, each dataset included in the analysis has a contribution to the stability profile. Therefore the combination of all available data sets was used in the second approach. The stability profile of this combined dataset, which consists of the expression values of 5423 transcripts across 888 different time points, revealed the genes displaying fewer fluctuations in their expression profiles across all these time points. The top 100 genes displaying the lowest CV value were investigated for the identification of candidate reference genes. The CV values of these genes varied between 0.05 and 0.11. A binned frequency histogram of the available data indicated that a gene pool of 6 genes with the lowest CV values (corresponding to 1% of the total population) would be a suitable choice of candidate pool (Figure S3). Bringing the CV threshold above 0.07 resulted in an observable number of additional genes thus populating the candidate pool with genes having less stable expression profiles, which are likely to be discarded in a stability analysis and rendering the experiments costly. These genes were TDH3, RPS26A, TPI1, CDC19, ARF1 and ENO1. Due to the high homology between ENO1 and ENO2 (blastn e-value = 4.7 e-264), concerns regarding a possible unspecificity in the PCR product led to the elimination of this gene from the candidate set. Therefore the top five genes, TDH3, RPS26A, TPI1, CDC19 and ARF1 with the smallest coefficient of variation values were selected as the candidate reference genes. Among these genes, TDH3, CDC19 and TPI1 were also identified as candidate genes using the first approach. RPS26A encoding a protein component of the small ribosomal subunit (40S) has similarity to rat S26 ribosomal protein, which is commonly used as a reference gene in rat tissues [15]. The wheat ortholog of ARF1, which encodes an ADP- ribosylation factor, was shown to be one of the most stable reference genes in that particular plant [3]. The candidate reference genes, which were selected using two different approaches, were combined and a total of 12 transcripts were determined as the candidate reference gene set for normalization of dynamic real-time RT-qPCR data. Identification and verification of exclusive reference gene subsets under specific experimental conditions In order to test the applicability of this candidate reference gene set on different experimental conditions, this pool of genes were used to identify the most stable condition specific reference genes. For this purpose, perturbations involving nutrient availability in yeast were investigated. Yeast cells grown in glucose-limited (Case study I) or ammonium-limited (Case study II) continuous cultures at steady state were supplemented with the respective limiting nutrient in an impulse-like manner. Dynamic gene expression profiles of the candidate reference genes were investigated in response to recovery from limiting conditions. The expression levels of HAP4 in Case study I and MEP2 in Case study II were investigated using a reference gene set, which was determined from the available candidate genes. HAP4 and MEP2 were selected such that their expression profiles were responsive to variations in the amount of available glucose and ammonium, respectively. Primers of 12 candidates were designed and their Yeast Reference Genes in Dynamic RT-qPCR Analysis amplification efficiencies were determined. All candidates except for PGK1, whose amplification efficiency could not be improved to be higher than 85%, were procured for further analysis (Table S2). Regions from the 11 candidate reference genes as well as the query genes were PCR amplified and the raw Cq values were transformed into relative quantities using the delta-Ct method [3]. All analyses in these case studies were conducted in compliance with MIQE guidelines for qPCR (Table S3) [16]. The expression stability of these candidates throughout these time series experiments was tested using two different statistical software programs; geNorm and NormFinder. Case Study I: Selection and validation of the most stable reference genes during glucose perturbation The presence of any correlated gene pairs among the candidates was previously reported to engender misleading predictions regarding the stability order of the candidate reference genes [5]. In order to identify correlated gene pairs from the candidate list, Pearson correlation coefficients between expression values of each pair of the candidates were calculated (Table S4). The threshold for the correlation among gene pairs was decided as 60.85. A total of three correlated gene pairs were identified. ARF1 was correlated with two other genes; TPI1 and CDC19. The third correlated gene pair was FBA1 and CCW12. Five different pools of candidate genes were created in order to investigate the effect of the inclusion of correlated gene pairs in the reference gene set. P-0 was the pool including all candidate genes. P-1 and P-3 were created by excluding ARF1 from the candidate list together with either CCW12 or FBA1, respectively. As an alternative, ARF1 was kept in the pool and the other two transcripts (TPI1 and CDC19), which showed high correlation with ARF1, were excluded from the candidate list in addition to either CCW12 or FBA1 (P-2 and P-4). One other alternative pool was constructed by excluding all of these correlated genes from the candidate list (P-5). In order to identify a suitable set of reference genes, two algorithms; NormFinder and geNorm were used. Both algorithms were used to rank the eleven candidates based on the stability of their expression profiles. Additionally, geNorm algorithm was also used to determine the stability ranking of genes in the pools, in which only the genes with a correlation of ,D0:85D among their expression profiles were left (P1–P5). The genes were scored using the average value of the stability rankings obtained by NormFin- der and geNorm for each pool separately. The total score for each candidate gene was calculated by summing up its individual scores for each case; P-0 to P-5. The genes were then ranked based on their total scores from the minimum score to the maximum score indicating an order from the most stable to the least stable gene. The minimum number of reference genes to be used was selected as 4 in order to make sure that at least one stable gene could be selected even after the removal of correlated gene pairs in P-4 and P-5 (Figure S4). This ranking indicated that the lowest total scores were obtained for TPI1, FBA1, CDC19 and ACT1. The scores for P-1 and P-2 were the lowest for these four reference genes indicating that these pools provided the optimal configuration. Since two of the most stable genes; TPI1 and CDC19, were removed in P-2 in order to discard the correlations among gene pairs, the scoring for these genes was based solely on the stability ranking obtained from NormFinder. The reference gene selection was carried out based on the stability scoring that was provided for P-1 since both geNorm and NormFinder stability rankings were available for all of these four genes. (Figure 1, Figure S4, Figure S5). TPI1, FBA1, CDC19 and ACT1 were also determined as the four most stable genes among all candidates PLoS ONE | www.plosone.org 3 June 2012 | Volume 7 | Issue 6 | e38351