Yeast Reference Genes in Dynamic RT-qPCR Analysis Figure 1. Scoring of the candidate genes in Case Study I. The cells representing the scores of the genes in different pools were colour coded. A coding scale from red to green indicated decreasing stability. The score of the genes in each pool was indicated by the number assigned to that cell in a quantitative manner; the smallest score indicating the most stable gene and the largest score indicating the least stable one. The stability of the gene associated with that score was also indicated by the colour assigned to that cell in a qualitative manner. The scoring of the six different pools of candidate genes was determined as the average ranking for each gene as indicated by the NormFinder and the geNorm analyses and the results were provided in the first six columns. The analysis was carried out using all genes (P-0), and using the 5 pools, each excluding a different set of correlated genes (P-1 to P-5). The total score for each gene was provided in the last column by summing up all of its scores through P-0 to P-5. doi:10.1371/journal.pone.0038351.g001 when the NormFinder algorithm was used to perform the stability analysis. Exclusion of the correlated gene pairs resulted in differences in the stability orders of the candidate genes when geNorm was used. The comparison of the stability rankings of P-0 and P-1 by geNorm with the rankings obtained by NormFinder indicated that elimination of correlations by the removal of ARF1 and CCW12 resulted in obtaining similar results using both algorithms. TPI1 and ACT1 could not be identified as stable unless correlations that were present among the expression profiles of the genes in the pool were removed. These results indicated that, correlated gene pairs should be excluded from the candidate list when using geNorm in order to avoid misleading results during the determination of the stable reference genes. Exclusion of the correlated gene pairs facilitates the attainment of comparable results by both algorithms. These two algorithms thus could be used to cross-check conjoint results while selecting the most suitable reference genes. geNorm suggested using a varying number of reference genes between 3 and 6 for different pools (with a pairwise variation threshold of 0.15 as suggested by the software developers) for determining the expression profile of HAP4 (Figure S6). A set of at most six reference genes; TPI1, FBA1, CDC19, ACT1, ARF1 and GCN4 were determined to constitute a suitable pool for the analysis of gene expression in response to an impulse-like glucose perturbation. The change in the expression level of HAP4 with a known transcriptional response to the amount of glucose in the medium was investigated in order to test the performance of this reference gene pool. HAP4, whose gene product is a transcrip- tional activator and global regulator of respiratory gene expres- sion, was known to be repressed by glucose in order to prevent the activation of respiration [17]. Therefore it could be expected that HAP4 would show a decreasing expression profile in response to a sudden increase in the amount of available glucose in the medium. In order to evaluate the importance of determining a suitable reference gene set, which would be used in the normalization of the expression profile of the gene of interest, three different sets of reference genes were used to calculate the relative expression profile of HAP4. Initially TDH3; one of the commonly used reference genes was used as the sole gene for normalization. Then, the geometric average of the Ct values for ARF1-CDC19; the gene pair, which wasidentified as the most stable pair in the analysis conducted by geNorm using P-0 was used as the reference gene set for the normalization of HAP4 expression. In the third case, TPI1, FBA1, CDC19 and ACT1; the reference genes identified as the most stable genes both by NormFinder and by geNorm using P-1 were employed in the analysis. Similarly the geometric average of the Ct values of these four genes was used for determining the expression profile of HAP4. HAP4 displayed a fluctuating expression profile in response to glucose perturbation when the data was normalized using TDH3; a commonly used reference gene. Although normalization using ARF1 and CDC19 captured the general trend for HAP4 expression, the sudden increase within the first minute upon addition of glucose was an unacceptable outcome of this selection of reference genes. In contrast, the relative expression of HAP4 decreased immediately after the impulse and remained repressed during the time course of the experiment as expected when the four most stable reference genes (TPI1, FBA1, CDC19and ACT1) were used for normalization (Figure 2-a). These results clearly demonstrated that using a commonly preferred reference gene without verification would yield to misleading expression profiles. Moreover the selection of a correlated gene pair would not improve the outcome even though they were identified to be stable by commonly used reference selection software. The TPI1-FBA1-CDC19-ACT1 reference gene set was further analyzed in order to investigate the effect of having additional genes in the set or the effect of excluding one or more of the members of the set. Since the pairwise variations among the candidate reference genes that were obtained from the geNorm algorithm indicated that a set of six reference genes would be suitable, the contribution of the next fifth and the sixth most stable reference genes in determining the expression profile of HAP4 were investigated. The results indicated that the inclusion of ARF1 or ARF1 and GCN4 in the reference gene set did not yield any improvements in the determination of the expression profile of HAP4. Moreover, the expression trend of HAP4 was observed to change slightly unfavourably upon addition of ARF1 in the reference gene set. Further inclusion of GCN4 in the reference set yielded a similar undesirable outcome (Figure 2-b). Therefore a PLoS ONE | www.plosone.org 4 June 2012 | Volume 7 | Issue 6 | e38351