Dataset Information

No control genes required: Bayesian analysis of qRT-PCR data.

ABSTRACT:

Background

Model-based analysis of data from quantitative reverse-transcription PCR (qRT-PCR) is potentially more powerful and versatile than traditional methods. Yet existing model-based approaches cannot properly deal with the higher sampling variances associated with low-abundant targets, nor do they provide a natural way to incorporate assumptions about the stability of control genes directly into the model-fitting process.

Results

In our method, raw qPCR data are represented as molecule counts, and described using generalized linear mixed models under Poisson-lognormal error. A Markov Chain Monte Carlo (MCMC) algorithm is used to sample from the joint posterior distribution over all model parameters, thereby estimating the effects of all experimental factors on the expression of every gene. The Poisson-based model allows for the correct specification of the mean-variance relationship of the PCR amplification process, and can also glean information from instances of no amplification (zero counts). Our method is very flexible with respect to control genes: any prior knowledge about the expected degree of their stability can be directly incorporated into the model. Yet the method provides sensible answers without such assumptions, or even in the complete absence of control genes. We also present a natural Bayesian analogue of the "classic" analysis, which uses standard data pre-processing steps (logarithmic transformation and multi-gene normalization) but estimates all gene expression changes jointly within a single model. The new methods are considerably more flexible and powerful than the standard delta-delta Ct analysis based on pairwise t-tests.

Conclusions

Our methodology expands the applicability of the relative-quantification analysis protocol all the way to the lowest-abundance targets, and provides a novel opportunity to analyze qRT-PCR data without making any assumptions concerning target stability. These procedures have been implemented as the MCMC.qpcr package in R.

SUBMITTER: Matz MV

PROVIDER: S-EPMC3747227 | biostudies-literature | 2013

REPOSITORIES: biostudies-literature

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Publications

No control genes required: Bayesian analysis of qRT-PCR data.

Matz Mikhail V MV Wright Rachel M RM Scott James G JG

PloS one 20130819 8

<h4>Background</h4>Model-based analysis of data from quantitative reverse-transcription PCR (qRT-PCR) is potentially more powerful and versatile than traditional methods. Yet existing model-based approaches cannot properly deal with the higher sampling variances associated with low-abundant targets, nor do they provide a natural way to incorporate assumptions about the stability of control genes directly into the model-fitting process.<h4>Results</h4>In our method, raw qPCR data are represented ...[more]

PMID: 23977043

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Project description:BackgroundDendrocalamus latiflorus Munro distributes widely in subtropical areas and plays vital roles as valuable natural resources. The transcriptome sequencing for D. latiflorus Munro has been performed and numerous genes especially those predicted to be unique to D. latiflorus Munro were revealed. qRT-PCR has become a feasible approach to uncover gene expression profiling, and the accuracy and reliability of the results obtained depends upon the proper selection of stable reference genes for accurate normalization. Therefore, a set of suitable internal controls should be validated for D. latiflorus Munro.ResultsIn this report, twelve candidate reference genes were selected and the assessment of gene expression stability was performed in ten tissue samples and four leaf samples from seedlings and anther-regenerated plants of different ploidy. The PCR amplification efficiency was estimated, and the candidate genes were ranked according to their expression stability using three software packages: geNorm, NormFinder and Bestkeeper. GAPDH and EF1α were characterized to be the most stable genes among different tissues or in all the sample pools, while CYP showed low expression stability. RPL3 had the optimal performance among four leaf samples. The application of verified reference genes was illustrated by analyzing ferritin and laccase expression profiles among different experimental sets. The analysis revealed the biological variation in ferritin and laccase transcript expression among the tissues studied and the individual plants.ConclusionsgeNorm, NormFinder, and BestKeeper analyses recommended different suitable reference gene(s) for normalization according to the experimental sets. GAPDH and EF1α had the highest expression stability across different tissues and RPL3 for the other sample set. This study emphasizes the importance of validating superior reference genes for qRT-PCR analysis to accurately normalize gene expression of D. latiflorus Munro.

Project description:BackgroundBivalves comprise around 30,000 extant species and have received much attention for their importance in ecosystems, aquaculture and evolutionary studies. Despite the increasing application of real-time quantitative reverse transcription PCR (qRT-PCR) in gene expression studies on bivalve species, little research has been conducted on reference gene selection which is critical for reliable and accurate qRT-PCR analysis. For scallops, systematic evaluation of reference genes that can be used among tissues or embryo/larva stages is lacking, and β-actin (ACT) is most frequently used as qRT-PCR reference gene without validation.ResultsIn this study, 12 commonly used candidate reference genes were selected from the transcriptome data of Yesso scallop (Patinopectenyessoensis) for suitable qRT-PCR reference genes identification. The expression of these genes in 36 tissue samples and 15 embryo/larva samples under normal physiological conditions was examined by qRT-PCR, and their expression stabilities were evaluated using three statistic algorithms, geNorm, NormFinder, and comparative ∆Ct method. Similar results were obtained by the three approaches for the most and the least stably expressed genes. Final comprehensive ranking for the 12 genes combing the results from the three programs showed that, for different tissues, DEAD-box RNA helicase (HELI), ubiquitin (UBQ), and 60S ribosomal protein L16 (RPL16) were the optimal reference genes combination, while for different embryo/larva stages, gene set containing Cytochrome B (CB), Cytochrome C (CC), Histone H3.3 (His3.3), and Glyceraldehyde-3-phosphate dehydrogenase (GAPDH) were recommended for qRT-PCR normalization. ACT was among the least stable genes for both adult tissues and embryos/larvae.ConclusionsThis work constitutes the first systematic analysis on reference genes selection for qRT-PCR normalization in scallop under normal conditions. The suitable reference genes we recommended will be useful for the identification of genes related to biological processes in Yesso scallop, and also in the reference gene selection for other scallop or bivalve species.

Dataset Information

No control genes required: Bayesian analysis of qRT-PCR data.

Background

Results

Conclusions

Publications

No control genes required: Bayesian analysis of qRT-PCR data.

Similar Datasets

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