What are the citrus fruit normalizing genes for rt-pcr?
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The most stable reference genes for normalizing gene expression in citrus fruit using RT-PCR are 18SrRNA, ACTB, and rpII . These genes were found to be stable among different citrus genotypes and tissues. It is recommended to use the mean result of these three genes as reference genes for accurate normalization of mRNA levels in qPCR analysis of diverse cultivars and tissues of citrus .
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| The provided paper does not mention the citrus fruit normalizing genes for RT-PCR. | |
116 Citations | The paper does not specifically mention the normalizing genes for citrus fruit in RT-PCR. The paper focuses on identifying suitable reference genes for gene expression analysis in citrus using qPCR. |
Open access 01 Jan 2012 | The paper suggests using 18SrRNA, ACTB, and rpII as reference genes for normalizing gene expression in citrus fruit using RT-PCR. |
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What relative normalised expression is significant in qpcr?7 answersDetermining significant relative normalized expression in qPCR involves several critical considerations, including the selection of appropriate reference genes, the normalization method employed, and the analysis of data quality. The choice of reference genes is paramount, as their expression should ideally remain constant across different experimental conditions and sample types. However, studies have shown that commonly used reference genes can exhibit variability under certain conditions, such as in stroke patients, where whole blood expression levels of reference genes were significantly altered. This variability underscores the importance of selecting stable reference genes, with software tools like geNorm, BestKeeper, and NormFinder assisting in identifying the most stable ones across various conditions.
Normalization methods also play a crucial role in determining significant expression levels. Traditional ΔCt methods, which rely on subtracting Ct values of reference genes from target genes, may introduce bias due to differences in PCR amplification efficiency among genes. Alternative approaches, such as data-driven normalization algorithms like NORMA-Gene, have been proposed to produce more robust gene expression data. Moreover, the regression of Ct values of target genes onto those of reference genes has been suggested to effectively remove biases introduced by traditional normalization methods.
The quality of RNA and the integrity of data are additional factors that influence the significance of relative normalized expression. RNA integrity, for example, has been shown to significantly impact relative expression results, with recommendations to normalize gene expression by an internal reference gene and perform efficiency correction. Furthermore, the number of reference genes used for normalization can affect the robustness of data normalization, with too few or too many reference genes potentially detrimenting data quality.
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