Real-Time Quantitative RT-PCR: Design, Calculations, and Statistics

Methods, 2010

Experiments using quantitative real-time PCR to test hypotheses are limited by technical and biological variability; we seek to minimise sources of confounding variability through optimum use of biological and technical replicates. The quality of an experiment design is commonly assessed by calculating its prospective power. Such calculations rely on knowledge of the expected variances of the measurements of each group of samples and the magnitude of the treatment effect; the estimation of which is often uninformed and unreliable. Here we introduce a method that exploits a small pilot study to estimate the biological and technical variances in order to improve the design of a subsequent large experiment. We measure the variance contributions at several 'levels' of the experiment design and provide a means of using this information to predict both the total variance and the prospective power of the assay. A validation of the method is provided through a variance analysis of representative genes in several bovine tissuetypes. We also discuss the effect of normalisation to a reference gene in terms of the measured variance components of the gene of interest. Finally, we describe a software implementation of these methods, powerNest, that gives the user the opportunity to input data from a pilot study and interactively modify the design of the assay. The software automatically calculates expected variances, statistical power, and optimal design of the larger experiment. powerNest enables the researcher to minimise the total confounding variance and maximise prospective power for a specified maximum cost for the large study.

Plant Cell, 2008

American Journal of Respiratory Cell and Molecular Biology, 1998

Progress toward complete sequencing of all human genes through the Human Genome Project has already resulted in a need for methods that allow quantitative expression measurement of multiple genes simultaneously. It is increasingly recognized that relative measurement of multiple genes will provide more mechanistic information regarding cell pathophysiology than measurement of individual genes one by one or by methods that do not allow direct intergene comparison. In this study, previously described quantitative reverse transcription-polymerase chain reaction methods were modified in an effort to provide a rapid, simple method for this purpose. Internal standard competitive templates (CTs) were prepared for each gene and were combined in a single solution containing CTs for more than 40 genes at defined concentrations relative to one another. Any subsequent dilution of the CT mixture did not alter the relationship of one CT to another. Because the same CT standard solution or a dilution of it was used in all experiments, data obtained from different experiments were easily compared. The use of multiple CT mixtures with different housekeeping gene to target gene ratios provided a linear dynamic range spanning the range of expression of all genes thus far evaluated. CT stock solutions were used to simultaneously quantify the expression of 25 genes relative to ␤-actin and glyceraldehyde-3-phosphate dehydrogenase in normal and malignant bronchial epithelial cells. Because the CT concentrations were known, data in the form of both absolute messenger RNA (mRNA) copy number and mRNA relative to housekeeping gene mRNA were obtained. The methods and reagents described will allow rapid, quantitative measurement of multiple genes simultaneously, using inexpensive and widely available equipment. Furthermore, the CT standard solution may be distributed to other investigators for interlaboratory standardization of experimental conditions. Willey, J.

Clinical Chemistry, 2009

Background: Quantitative PCR (qPCR) is a valuable technique for accurately and reliably profiling and quantifying gene expression. Typically, samples obtained from the organism of study have to be processed via several preparative steps before qPCR.Method: We estimated the errors of sample withdrawal and extraction, reverse transcription (RT), and qPCR that are introduced into measurements of mRNA concentrations. We performed hierarchically arranged experiments with 3 animals, 3 samples, 3 RT reactions, and 3 qPCRs and quantified the expression of several genes in solid tissue, blood, cell culture, and single cells.Results: A nested ANOVA design was used to model the experiments, and relative and absolute errors were calculated with this model for each processing level in the hierarchical design. We found that intersubject differences became easily confounded by sample heterogeneity for single cells and solid tissue. In cell cultures and blood, the noise from the RT and qPCR steps c...

Biotechnology Advances, 2009

Major improvements have been made in mRNA quantification and internal standard selection over the last decade. Our aim in this paper is to present the main developments that are of interest for practical laboratory work, contrasting the situation as it is now with the one of ten years ago, and presenting some excellent examples of what can be done today. Specifically, we will mainly discuss Real-Time RT-PCR major improvements that have been performed in the following areas: the most commonly used quantification techniques, the mathematical and software tools created to help researchers in their work on internal standard selection, the availability of detection chemistries and technical information and of commercial tools and services. In addition to mRNA quantification, we will also discuss some aspects of non-coding RNA and protein quantification. In addition to technical improvements, the development of international cooperation and the creation of technical databases are likely to represent a major tool for the future in the standardization of gene expression quantification.

BMC Bioinformatics, 2010

Background: Normalization 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. Results: We 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. Conclusions: The 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.

Polymerase Chain Reaction for Biomedical Applications, 2016

This chapter was developed to provide some important guidelines for studies with quantitative PCR (qPCR) using either dyes or probes, citing several essential components necessary for a good PCR assay. The efficiency and specificity of quantitative PCR (qPCR) depend on several parameters related to mRNA quantification that must be controlled to avoid mistakes in data interpretation. Avoiding contamination with proteins, carbohydrate and phenolic compounds during RNA extraction and purification processes will improve RNA quality and provide reliable results. Specific primers and sensible probes are also crucial to intensify efficiency, specificity and fluorescence. Other parameters such as the optimization of primer concentrations and efficiency primer curves must be done. During gene-expression profile quantification, qPCR assays using reference genes are required to normalize the target gene expression data. These reference genes are checked for stability to identify the most stable genes among a group of candidate genes that will be used to normalize the qPCR data, using programs such as geNorm, BestKeeper and NormFinder. Additionally, the choice of appropriate reference genes for a specific experimental condition is fundamental. The main aim of this chapter is to provide guidelines and highlight precautions to obtain a successful qPCR assays.

This manual explains how to use casper to help design RNA-seq experiments, for the main manual please load the package and type vignette('casper') at the command prompt. casper provides tools to design RNA-seq isoform expression experiments, both single and multiple sample studies. The former may e.g. estimate relative isoform expression within a gene or perform de novo isoform discovery, whereas the latter usually search for isoforms that are differentially expressed across sample groups (e.g. healthy vs. sick). When designing such an experiment researchers face numerous decisions, including the sequencing setup (e.g. number of reads, read length), sample preparation (e.g. insert sizes) and the sample size (in multiple sample studies). Choosing the optimal strategy is challenging, as it depends on the structure of the isoforms, their (unknown) absolute and relative expressions, the extent to which they differ across groups and on non-trivial interactions with the sequencing technology and sample preparation. Because it is impossible to anticipate all these issues, casper adopts a sequential strategy. Based on preliminary data, it simulates RNA-seq data under several experimental setups and evaluates their relative merits using Bayesian decision theory. The approach is sequential in that, whenever new experimental data is observed it can be added to the preliminary data to refine the predictions. More formally, predictions are based on posterior predictive draws that incorporate the uncertainty on all unknown quantities and condition on all data observed so far. The performance (utility) of each considered experimental setup is evaluated with default criteria related to estimation error (single sample studies) or operating characteristics (multiple sample studies). An advantage of the proposed simulation-based approach is that the experimenter may easily change or modify these default criteria. The best design can then be chosen either informally or by maximizing posterior expected utility (Savage, 1954). Simulated data are returned as ExpressionSet objects or .bam files, so that alternative analysis strategies within Bioconductor (Gentleman et al., 2004)

Quantitative Real-time Polymerase Chain Reaction (qPCR) is an important tool for molecular biology and biotechnology research, widely used to determine the expression levels of mRNA. Two main methods to performing qPCR are largely used: The absolute quantification, in which the mRNA levels are determined by using a standard curve and the relative method, which is based on the use of reference genes. Reference genes are widely expressed in cells of animal and plant tissues and their expression pattern are theoretically unchanged within several situations, which makes them an excellent choice to normalize mRNA quantification data in relative qPCR studies. However, several reports are increasingly showing that the use of only one reference gene in relative qPCR studies should be avoided, because in the real world their expression levels can significantly change from tissue to tissue. Several softwares, such as geNorm, BestKeeper and NormFinder, have been developed to perform data normalisation, and these programs may assist in choosing the most stable reference genes. The aim of this review was to describe the current normalisation strategies used in qPCR assay, as well as to establish essential rules to perform reliable mRNA quantification. Finally, this review show some innovations in the advances on qPCR.