Dataset Information

Correction of RT-qPCR data for genomic DNA-derived signals with ValidPrime.

ABSTRACT: Genomic DNA (gDNA) contamination is an inherent problem during RNA purification that can lead to non-specific amplification and aberrant results in reverse transcription quantitative PCR (RT-qPCR). Currently, there is no alternative to RT(-) controls to evaluate the impact of the gDNA background on RT-PCR data. We propose a novel method (ValidPrime) that is more accurate than traditional RT(-) controls to test qPCR assays with respect to their sensitivity toward gDNA. ValidPrime measures the gDNA contribution using an optimized gDNA-specific ValidPrime assay (VPA) and gDNA reference sample(s). The VPA, targeting a non-transcribed locus, is used to measure the gDNA contents in RT(+) samples and the gDNA reference is used to normalize for GOI-specific differences in gDNA sensitivity. We demonstrate that the RNA-derived component of the signal can be accurately estimated and deduced from the total signal. ValidPrime corrects with high precision for both exogenous (spiked) and endogenous gDNA, contributing ∼60% of the total signal, whereas substantially reducing the number of required qPCR control reactions. In conclusion, ValidPrime offers a cost-efficient alternative to RT(-) controls and accurately corrects for signals derived from gDNA in RT-qPCR.

SUBMITTER: Laurell H

PROVIDER: S-EPMC3326333 | biostudies-literature | 2012 Apr

REPOSITORIES: biostudies-literature

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Correction of RT-qPCR data for genomic DNA-derived signals with ValidPrime.

Laurell Henrik H Iacovoni Jason S JS Abot Anne A Svec David D Maoret Jean-José JJ Arnal Jean-François JF Kubista Mikael M

Nucleic acids research 20120106 7

Genomic DNA (gDNA) contamination is an inherent problem during RNA purification that can lead to non-specific amplification and aberrant results in reverse transcription quantitative PCR (RT-qPCR). Currently, there is no alternative to RT(-) controls to evaluate the impact of the gDNA background on RT-PCR data. We propose a novel method (ValidPrime) that is more accurate than traditional RT(-) controls to test qPCR assays with respect to their sensitivity toward gDNA. ValidPrime measures the gDN ...[more]

PMID: 22228834

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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

Correction of RT-qPCR data for genomic DNA-derived signals with ValidPrime.

Publications

Correction of RT-qPCR data for genomic DNA-derived signals with ValidPrime.

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