Primer3 software was used in the design of the primers except for that for 18S rRNA, which was designed in this study [39]. The complete set of primer pair sequences and RTPrimerDB identification numbers are listed in Table S2. The designed primers were manufactured by Alpha DNA (Montreal, Quebec) and desalted. The performance of all primers was experimentally confirmed by conventional PCR to ensure the amplification of a single region with the correct amplicon length. qPCR protocol and qPCR validation. Qiagen Quanti- TectH SYBRH Green one step RT-PCR kit was used for real-time RT-qPCR applications as described by the manufacturer (Qiagen, USA, Cat no: 204245). All kit contents are optimized and validated by the manufacturer. The PCR reactions were performed in a final reaction volume of 12.5 ml containing the final concentration of 2.5 mM of MgCl2 and 0.5 mM of forward and reverse primers. Plates and film sealers were manufactured from Bio-Rad Laboratories (Cat no: MSB1001, MLP9601). The reaction mixtures were prepared manually and the reactions were allowed to proceed in iCycler 5 instrument (Bio-Rad Laborato- ries). Assays were conducted in triplicates. The annealing temperature optimization and thermocycling parameters are provided in Table S8. RT-PCR sensitivity and reproducibility assays were conducted as described by the manufacturer (Qiagen, USA, Cat no: 204245). The melt curves indicating the specificity of the reaction were provided in Text S3. The range of the Cq values was determined as 10.9–31.70 for case study I and as 12.00–28.30 for case study II. The presence of no-template control wells were ensured in all reaction plates. Serial dilutions of RNA (5-point 4-fold dilution series, starting with 100 ng/reaction) were used for the measurement of the overall assay efficiency for one step RT-PCR. The log template amount was plotted against the corresponding Cq value and the slope (S) was determined. The efficiency (E) was calculated according to the following formula: E~10({1=S){1 The efficiency values, standard curves and R2 of standard curves for each of the candidate reference genes and the target genes were provided in Table S2. The linear dynamic range of the Cq values was between 14.7 and 35.7. The 95% confidence interval at the lowest limit was determined to be less than 1 for the Cq values that were determined using either forward/reverse primer pair except for ACT1 and PDC1 (1.5 and 1.8, respectively). The average of the 95% confidence intervals of the Cq values throughout the range was 0.6. Data analysis. iCyclerTM iQ Optical System Software version 3.0a (Bio-Rad Laboratories) was used with PCR base line subtracted curve fit method for the measurement of quantification cycle (Cq). The geometric average of the Cq values with a standard deviation of less than 0.5 cycles was used in further calculations. Raw Cq values were used to determine the relative gene expression values (Q) using delta-Ct method [40]. Cq values for no-template control wells were excluded from further analysis since the values were greater than 35 or not detected. The raw Cq values and intra assay variations are provided as Table S9. The data analysis was carried out using Microsoft Office 2007 Excel implemented with the statistical analysis tool. Assessment of expression stability by geNorm and NormFinder. The expression stability of candidate reference genes was evaluated using geNorm [5] and NormFinder [7] software programs. The expression values were provided as input for both algorithms. Delta-Ct method was used for the calculation Yeast Reference Genes in Dynamic RT-qPCR Analysis of expression values with the gene having the lowest Cq value being used as the reference gene [5]. Supporting Information Figure S1 Frequency distribution of transcripts in terms of their occurrence among top 100 most stable genes. The stability ranking of all transcripts was determined in terms of CV values for each independent set of experiments. For every transcript, the number of datasets, in which that transcript occurred among the top 100 most stable genes, was determined. These numbers were then represented as a percentage of the total number of independent data sets. The percentage of data sets, in which a number of transcripts were represented, was plotted against that number of transcripts as a frequency distribution. (TIF) Figure S2 Minimum and average PCC values as a function of the number of experiments. PCC values were calculated in order to determine the correlation between the overall stability profile for 31 datasets and each stability profile, which was calculated from the combination of the changing number of datasets ranging between 1 and 30. For the case, in which a specific number of datasets were used, both the average PCC of the available dataset combinations and the minimum PCC were determined. The figure represents the variation in the average and the minimum PCC values as a function the number of the experiments, which were used in the calculation of the individual stability profiles that would be compared with the overall stability profile. (TIF) Figure S3 A non-cumulative histogram plot of the number of genes assigned to a specific CV value. The CV value of each transcript, which was calculated from the combination of all datasets, was used to obtain the overall stability profile. The CV values of the most stable 100 genes, which were determined based on this stability profile, were binned in a range such that the maximal and the minimal CV values were included. A non-cumulative histogram plot of this frequency distribution was presented in the figure. (TIF) Figure S4 Stability analysis of the candidate genes in Case Study I. The cells representing the stability ranking of the genes in different pools were indicated in the corresponding areas in a quantitative manner; 1 indicating the most stable gene and 11 indicating the least stable one. A dash was used to indicate the genes that were omitted from analysis due to having a correlated expression profile with another gene in the candidate set. geNorm was used for the stability analysis of the six different pools of candidate genes and the results were provided in the last 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 stability analysis conducted using NormFinder with all candidate genes was provided in the first column. (TIF) Figure S5 geNorm stability output charts for Case Study I. The stability analysis based on the expression levels of the candidate genes obtained in Case Study I was conducted for each pool; P0 to P5 using geNorm software. The M values indicating the average expression stability for the candidate genes were provided in the output format that the software provided. The plots for P0 to P5 were represented in the figure as designated by the letters A to F, respectively. (TIF) PLoS ONE | www.plosone.org 12 June 2012 | Volume 7 | Issue 6 | e38351