Yeast Reference Genes in Dynamic RT-qPCR Analysis Figure 5. Flowchart representing the approach. This figure represents the general strategy followed for the identification of a pool of reference gene candidates and the determination of a reference gene set to be used for a specific condition in real-time RT-qPCR experiments. doi:10.1371/journal.pone.0038351.g005 reported for these datasets were closest to their experimental setup. The potential reference genes were selected among the transcripts displaying stable expression profiles in these microarray datasets. Additionally traditionally used reference genes were also included in the study [4]. Another study focused on systematically collecting microarray data for selecting reference genes under a specific set of conditions [1]. In yet another study, the candidate reference genes were determined via stability of their expression levels across different tissues using their EST profiles [3]. Existence of a pool of commonly used reference genes has proven very useful in time invariant RT-qPCR analysis. However, these reference genes were often found to display far from stable expression profiles in dynamic studies mandating researchers to seek alternative ways to identify novel reference gene candidates [4]. Unfortunately currently utilized approaches usually fail in the identification of a universal set of candidate reference genes to be used in dynamic studies regardless of experimental conditions. Our approach may help serve this purpose by gathering dynamic microarray datasets having as diverse experimental conditions as possible for the identification of a universal pool of candidate reference genes, from which users may select subsets that would be suitable for their particular needs. The present approach allows its users a vast space to manoeuvre using the proposed candidate reference genes. Although the specific cases presented in this study focused on transient changes in the amount of available glucose or ammonium in the fermentation medium in yeast, suitable reference genes would be selected among the candidate pool for completely different experimental conditions regardless of whether such an experi- mental setup was previously analyzed or not. The candidate pool itself was created from a collection of dynamic datasets with a diverse set of experimental conditions. Among these, environmen- tal variations including osmotic stress, heat shock, cell cycle, DNA damage, nutrient availability, chemical treatments, desiccation stress, nitrosative stress and genetic mutations including deletion and overexpression of genes were included (Table S1). The genes in the candidate pool were identified such that regardless of the diversity of the experimental conditions under which the microarray data were generated, the expression profile of each candidate was stable. This strength of the method of selection increases the possibility of identifying reference gene among the pool of candidates, which would have stable expression profiles across time in a specific experimental condition that was not previously analyzed. It can be concluded that the pool of candidate reference genes determined in this study; TPI1, ACT1, TDH3, FBA1, CCW12, CDC19, ADH1, PGK1, GCN4, PDC1, RPS26A and ARF1, may be used to identify the set of suitable reference genes in the analysis of dynamic transcriptional data by real-time RT-qPCR in S. cerevisiae under different experimental conditions. However, it is also undeniable that, as the number of the publicly available time course microarray datasets increases, this candidate gene set may be improved. The flexibility of the methods used in this approach enable the inclusion of additional datasets thus constant update of the candidate pool. Additional methods could also be implement- ed in a modular manner to enhance the results obtained from this study. This study showed the significance of researchers’ intervention at various stages of reference gene selection both PLoS ONE | www.plosone.org 10 June 2012 | Volume 7 | Issue 6 | e38351