Dataset Information

Simple regression for correcting ?Ct bias in RT-qPCR low-density array data normalization.

ABSTRACT: Reverse transcription quantitative PCR (RT-qPCR) is considered the gold standard for quantifying relative gene expression. Normalization of RT-qPCR data is commonly achieved by subtracting the Ct values of the internal reference genes from the Ct values of the target genes to obtain ?Ct. ?Ct values are then used to derive ??Ct when compared to a control group or to conduct further statistical analysis.We examined two rheumatoid arthritis RT-qPCR low density array datasets and found that this normalization method introduces substantial bias due to differences in PCR amplification efficiency among genes. This bias results in undesirable correlations between target genes and reference genes, which affect the estimation of fold changes and the tests for differentially expressed genes. Similar biases were also found in multiple public mRNA and miRNA RT-qPCR array datasets we analysed. We propose to regress the Ct values of the target genes onto those of the reference genes to obtain regression coefficients, which are then used to adjust the reference gene Ct values before calculating ?Ct.The per-gene regression method effectively removes the ?Ct bias. This method can be applied to both low density RT-qPCR arrays and individual RT-qPCR assays.

SUBMITTER: Cui X

PROVIDER: S-EPMC4335788 | biostudies-literature | 2015 Feb

REPOSITORIES: biostudies-literature

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Simple regression for correcting ΔCt bias in RT-qPCR low-density array data normalization.

Cui Xiangqin X Yu Shaohua S Tamhane Ashutosh A Causey Zenoria L ZL Steg Adam A Danila Maria I MI Reynolds Richard J RJ Wang Jinyi J Wanzeck Keith C KC Tang Qi Q Ledbetter Stephanie S SS Redden David T DT Johnson Martin R MR Bridges S Louis SL

BMC genomics 20150214

<h4>Background</h4>Reverse transcription quantitative PCR (RT-qPCR) is considered the gold standard for quantifying relative gene expression. Normalization of RT-qPCR data is commonly achieved by subtracting the Ct values of the internal reference genes from the Ct values of the target genes to obtain ΔCt. ΔCt values are then used to derive ΔΔCt when compared to a control group or to conduct further statistical analysis.<h4>Results</h4>We examined two rheumatoid arthritis RT-qPCR low density arr ...[more]

PMID: 25888492

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Project description:Chondrus crispus is a common red macroalga living on the rocky shores of the North Atlantic Ocean. It has a long research history, being a major source of carrageenan, a thickener widely used in the food industry, but also for physiological and ecological studies. To establish it as a model for red algae, its genome has been sequenced, allowing the development of molecular tools such as quantification of gene expression, including RNAseq and RT-qPCR. To determine appropriate genes for RT-qPCR normalization, the expression of 14 genes was monitored in 18 conditions using two sets of algal samples: samples from the sequenced strain, cultured and stressed in laboratory conditions and C. crispus collected on the shore and stressed in situ. The expression stability of the genes between the samples was evaluated by comparing the Ct range and using the programs geNorm and NormFinder. The candidate genes encoded translation related proteins (initiation factors IF4A-1 and IF4A-2, elongation factor EF1α and eRF3, an eukaryotic polypeptide chain release factor), cytoskeleton proteins (two β-tubulins, α-tubulin and actin), enzymes involved in the pentose phosphate pathway (glucose 6-phosphate deshydrogenase), protein recycling process (ubiquitin and ubiquitin-conjugating enzyme) and glycolysis (isocitrate dehydrogenase). The two sets of samples showed different expression patterns. Most of the genes were stable in the algae cultivated in the laboratory, whereas environmental samples showed a more important variation in gene expression. When analyzing the two sets separately, the ranking of the most stables genes were different from one method to another. When considering all samples, the two statistical methods were concordant, revealing translation initiation factor 4A-2 and eukaryotic polypeptide chain release factor 3 as pertinent normalization genes. This study highlights thus the importance of testing reference genes according to the experiments as well as the genetic and physiological background of the organism.

Project description:BackgroundA critical step in the RT-qPCR workflow for studying gene expression is data normalization, one of the strategies being the use of reference genes. This study aimed to identify and validate a selection of reference genes for relative quantification in Talaromyces versatilis, a relevant industrial filamentous fungus. Beyond T. versatilis, this study also aimed to propose reference genes that are applicable more widely for RT-qPCR data normalization in filamentous fungi.ResultsA selection of stable, potential reference genes was carried out in silico from RNA-seq based transcriptomic data obtained from T. versatilis. A dozen functionally unrelated candidate genes were analysed by RT-qPCR assays over more than 30 relevant culture conditions. By using geNorm, we showed that most of these candidate genes had stable transcript levels in most of the conditions, from growth environments to conidial germination. The overall robustness of these genes was explored further by showing that any combination of 3 of them led to minimal normalization bias. To extend the relevance of the study beyond T. versatilis, we challenged their stability together with sixteen other classically used genes such as β-tubulin or actin, in a representative sample of about 100 RNA-seq datasets. These datasets were obtained from 18 phylogenetically distant filamentous fungi exposed to prevalent experimental conditions. Although this wide analysis demonstrated that each of the chosen genes exhibited sporadic up- or down-regulation, their hierarchical clustering allowed the identification of a promising group of 6 genes, which presented weak expression changes and no tendency to up- or down-regulation over the whole set of conditions. This group included ubcB, sac7, fis1 and sarA genes, as well as TFC1 and UBC6 that were previously validated for their use in S. cerevisiae.ConclusionsWe propose a set of 6 genes that can be used as reference genes in RT-qPCR data normalization in any field of fungal biology. However, we recommend that the uniform transcription of these genes is tested by systematic experimental validation and to use the geometric averaging of at least 3 of the best ones. This will minimize the bias in normalization and will support trustworthy biological conclusions.

Dataset Information

Simple regression for correcting ?Ct bias in RT-qPCR low-density array data normalization.

Publications

Simple regression for correcting ΔCt bias in RT-qPCR low-density array data normalization.

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