Dataset Information

Reference gene selection for quantitative real-time RT-PCR normalization in Iris. lactea var. chinensis roots under cadmium, lead, and salt stress conditions.

ABSTRACT: Quantitative real time PCR (RT-qPCR) has emerged as an accurate and sensitive method to measure the gene expression. However, obtaining reliable result depends on the selection of reference genes which normalize differences among samples. In this study, we assessed the expression stability of seven reference genes, namely, ubiquitin-protein ligase UBC9 (UBC), tubulin alpha-5 (TUBLIN), eukaryotic translation initiation factor (EIF-5A), translation elongation factor EF1A (EF1 ? ), translation elongation factor EF1B (EF1b), actin11 (ACTIN), and histone H3 (HIS), in Iris. lactea var. chinensis (I. lactea var. chinensis) root when the plants were subjected to cadmium (Cd), lead (Pb), and salt stress conditions. All seven reference genes showed a relatively wide range of threshold cycles (C t ) values in different samples. GeNorm and NormFinder algorithms were used to assess the suitable reference genes. The results from the two software units showed that EIF-5A and UBC were the most stable reference genes across all of the tested samples, while TUBLIN was unsuitable as internal controls. I. lactea var. chinensis is tolerant to Cd, Pb, and salt. Our results will benefit future research on gene expression in response to the three abiotic stresses.

SUBMITTER: Gu CS

PROVIDER: S-EPMC4058273 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature

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Publications

Reference gene selection for quantitative real-time RT-PCR normalization in Iris. lactea var. chinensis roots under cadmium, lead, and salt stress conditions.

Gu Chun-Sun CS Liu Liang-qin LQ Xu Chen C Zhao Yan-hai YH Zhu Xu-dong XD Huang Su-Zhen SZ

TheScientificWorldJournal 20140526

Quantitative real time PCR (RT-qPCR) has emerged as an accurate and sensitive method to measure the gene expression. However, obtaining reliable result depends on the selection of reference genes which normalize differences among samples. In this study, we assessed the expression stability of seven reference genes, namely, ubiquitin-protein ligase UBC9 (UBC), tubulin alpha-5 (TUBLIN), eukaryotic translation initiation factor (EIF-5A), translation elongation factor EF1A (EF1 α ), translation elon ...[more]

PMID: 24977206

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Project description:BackgroundNormalization in real-time qRT-PCR is necessary to compensate for experimental variation. A popular normalization strategy employs reference gene(s), which may introduce additional variability into normalized expression levels due to innate variation (between tissues, individuals, etc). To minimize this innate variability, multiple reference genes are used. Current methods of selecting reference genes make an assumption of independence in their innate variation. This assumption is not always justified, which may lead to selecting a suboptimal set of reference genes.ResultsWe propose a robust approach for selecting optimal subset(s) of reference genes with the smallest variance of the corresponding normalizing factors. The normalizing factor variance estimates are based on the estimated unstructured covariance matrix of all available candidate reference genes, adjusting for all possible correlations. Robustness is achieved through bootstrapping all candidate reference gene data and obtaining the bootstrap upper confidence limits for the variances of the log-transformed normalizing factors. The selection of the reference gene subset is optimized with respect to one of the following criteria: (A) to minimize the variability of the normalizing factor; (B) to minimize the number of reference genes with acceptable upper limit on variability of the normalizing factor, (C) to minimize the average rank of the variance of the normalizing factor. The proposed approach evaluates all gene subsets of various sizes rather than ranking individual reference genes by their stability, as in the previous work. In two publicly available data sets and one new data set, our approach identified subset(s) of reference genes with smaller empirical variance of the normalizing factor than in subsets identified using previously published methods. A small simulation study indicated an advantage of the proposed approach in terms of sensitivity to identify the true optimal reference subset in the presence of even modest, especially negative correlation among the candidate reference genes.ConclusionsThe proposed approach performs comprehensive and robust evaluation of the variability of normalizing factors based on all possible subsets of candidate reference genes. The results of this evaluation provide flexibility to choose from important criteria for selecting the optimal subset(s) of reference genes, unless one subset meets all the criteria. This approach identifies gene subset(s) with smaller variability of normalizing factors than current standard approaches, particularly if there is some nontrivial innate correlation among the candidate genes.

Dataset Information

Reference gene selection for quantitative real-time RT-PCR normalization in Iris. lactea var. chinensis roots under cadmium, lead, and salt stress conditions.

Publications

Reference gene selection for quantitative real-time RT-PCR normalization in Iris. lactea var. chinensis roots under cadmium, lead, and salt stress conditions.

Similar Datasets

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