Transcriptome Analysis of Spermophilus lateralis and Spermophilus tridecemlineatus Liver Does Not Suggest the Presence of Spermophilus-Liver-Specific Reference Genes
The expressions of reference genes used in gene expression studies are assumed to be stable under most circumstances. However, studies had demonstrated that genes assumed to be stably expressed in a species are not necessarily stably expressed in other organisms. This study aims to evaluate the likelihood of genus-specific reference genes for liver using comparable microarray datasets from Spermophilus lateralis and Spermophilus tridecemlineatus. The coefficient of variance (CV) of each probe was calculated and there were 178 probes common between the lowest 10% CV of both datasets ( ). All 3 lists were analysed by NormFinder. Our results suggest that the most invariant probe for S. tridecemlineatus was 02n12, while that for S. lateralis was 24j21. However, our results showed that Probes 02n12 and 24j21 are ranked 8644 and 926 in terms of invariancy for S. lateralis and S. tridecemlineatus respectively. This suggests the lack of common liver-specific reference probes for both S. lateralis and S. tridecemlineatus. Given that S. lateralis and S. tridecemlineatus are closely related species and the datasets are comparable, our results do not support the presence of genus-specific reference genes. 1. Introduction Gene expression analysis is examining the variations in gene expression by measuring DNA expression levels over time. These variations may be a result of many factors, such as environmental, developmental, and metabolic changes, or treatments. Quantitative real-time polymerase chain reaction (qRT-PCR) is one such used technique to quantify and analyse gene expressions [1, 2]. However, qRT-PCR requires a stably expressed gene under a wide variety of conditions [3, 4], known as a reference gene, as a standard to produce accurate and reliable results on transcriptional differences of various genes of interest. Candidate reference genes, which are commonly assumed to be invariant, can be identified using statistically based algorithms, such as geNorm [5], NormFinder [6], and BestKeeper [7], or descriptive statistics, such as regression [8]. Microarrays, which usually contain thousands of probes, present a good source of data for identifying reference genes [9]. Reference genes had been successfully identified from microarrays in a number of studies [10, 11]. However, several studies had refuted the possibility of universal reference genes [10–14] that can be used in every organ in every organism. This corroborates several studies demonstrating that genes commonly considered to be expressionally invariable may vary under different experimental
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