Statistical algorithm for assuring similar efficiency in standards and samples for absolute quantification by real-time reverse transcription polymerase chain reaction

Chervoneva, Inna; Hyslop, Terry; Iglewicz, Boris; Johns, Laura; Wolfe, Henry R.; Schulz, Stephanie; Leong, Elinor

doi:10.1016/j.ab.2005.10.042

Cited by 18 publications

(17 citation statements)

References 29 publications

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“…The first KOD methods (46,49,59,60) assumed that the exponential phase, with its constant efficiency continues at least up to the threshold, where Cq is determined. Thus, a single parameter, the efficiency at the early cycles, could be used to validate similarity of the kinetics of compared reactions.…”

Section: Kodmentioning

confidence: 99%

“…(59) pointed out that the second KOD method, which is intended to retain 95% of samples with efficiencies comparable to those in the reference set, the acceptance interval included the desired 95% coverage only if the mean and standard deviation used in the computations are true parameters describing the efficiency distribution in the underlying reference population. However, the employed estimates of the mean and standard deviation are calculated from the reference set without any adjustment for the error of estimation and, consequently, the second KOD method is not a valid statistical procedure.…”

Section: Kodmentioning

confidence: 99%

“…However, the employed estimates of the mean and standard deviation are calculated from the reference set without any adjustment for the error of estimation and, consequently, the second KOD method is not a valid statistical procedure. In particular, if the reference set is small, the second KOD method may erroneously reject reactions with efficiencies that would be consistent with those of the infinite reference setpopulation (59). …”

Section: Kodmentioning

confidence: 99%

“…suggested an improved, third, KOD method (59). Here, reaction-specific efficiency is estimated by fitting an exponential growth model to the fluorescence data in the early detectable cycles of the reaction.…”

Section: Kodmentioning

confidence: 99%

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Validation of kinetics similarity in qPCR

Bar

Kubista

Tichopád

2011

Nucleic Acids Research

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Quantitative real-time PCR (qPCR) is the method of choice for specific and sensitive quantification of nucleic acids. However, data validation is still a major issue, partially due to the complex effect of PCR inhibition on the results. If undetected PCR inhibition may severely impair the accuracy and sensitivity of results. PCR inhibition is addressed by prevention, detection and correction of PCR results. Recently, a new family of computational methods for the detection of PCR inhibition called kinetics outlier detection (KOD) emerged. KOD methods are based on comparison of one or a few kinetic parameters describing a test reaction to those describing a set of reference reactions. Modern KOD can detect PCR inhibition reflected by shift of the amplification curve by merely half a cycle with specificity and sensitivity >90%. Based solely on data analysis, these tools complement measures to improve and control pre-analytics. KOD methods do not require labor and materials, do not affect the reaction accuracy and sensitivity and they can be automated for fast and reliable quantification. This review describes the background of KOD methods, their principles, assumptions, strengths and limitations. Finally, the review provides recommendations how to use KOD and how to evaluate its performance.

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Section: Kodmentioning

confidence: 99%

Section: Kodmentioning

confidence: 99%

Section: Kodmentioning

confidence: 99%

Section: Kodmentioning

confidence: 99%

See 2 more Smart Citations

Validation of kinetics similarity in qPCR

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Kubista

Tichopád

2011

Nucleic Acids Research

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“…Of the more than 70 glycoside hydrolase families defined on the basis of amino acid sequence identity in their catalytic domains (18), cellulases are found in 15 families (families 5,6,7,8,9,10,11,12,26,44,45,48,51,60, and 61) (24,40). The cellulase type does not correlate closely with phylogeny: most cellulolytic microorganisms produce cellulases that belong to different families, and cellulases of the same family occur in widely different microorganisms (4).…”

Section: Resultsmentioning

confidence: 99%

Detection and Quantification of Functional Genes of Cellulose- Degrading, Fermentative, and Sulfate-Reducing Bacteria and Methanogenic Archaea

Pereyra¹,

Hiibel²,

Riquelme

et al. 2010

Appl Environ Microbiol

134

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Cellulose degradation, fermentation, sulfate reduction, and methanogenesis are microbial processes that coexist in a variety of natural and engineered anaerobic environments. Compared to the study of 16S rRNA genes, the study of the genes encoding the enzymes responsible for these phylogenetically diverse functions is advantageous because it provides direct functional information. However, no methods are available for the broad quantification of these genes from uncultured microbes characteristic of complex environments. In this study, consensus degenerate hybrid oligonucleotide primers were designed and validated to amplify both sequenced and unsequenced glycoside hydrolase genes of cellulose-degrading bacteria, hydA genes of fermentative bacteria, dsrA genes of sulfate-reducing bacteria, and mcrA genes of methanogenic archaea. Specificity was verified in silico and by cloning and sequencing of PCR products obtained from an environmental sample characterized by the target functions. The primer pairs were further adapted to quantitative PCR (Q-PCR), and the method was demonstrated on samples obtained from two sulfate-reducing bioreactors treating mine drainage, one lignocellulose based and the other ethanol fed. As expected, the Q-PCR analysis revealed that the lignocellulose-based bioreactor contained higher numbers of cellulose degraders, fermenters, and methanogens, while the ethanol-fed bioreactor was enriched in sulfate reducers. The suite of primers developed represents a significant advance over prior work, which, for the most part, has targeted only pure cultures or has suffered from low specificity. Furthermore, ensuring the suitability of the primers for Q-PCR provided broad quantitative access to genes that drive critical anaerobic catalytic processes.

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Relative quantification based on logistic models for individual polymerase chain reactions

Chervoneva

Iglewicz

et al. 2007

Statistics in Medicine

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The quantitative real-time reverse transcription polymerase chain reaction (RT-PCR) technology measures molecular variations in specific biomarkers. Relative quantification determines the target expression relative to an external standard or reference sample and should be adjusted for the PCR efficiencies actually achieved. More accurate methods of estimating PCR efficiency require a number of serial dilutions of the target sample, which is not generally feasible for clinical specimens. Alternatively, the efficiency of a single reaction may be estimated by considering kinetic data from this reaction. The current methods of estimating individual reaction efficiency require finding its exponential phase, which may affect the accuracy and precision of efficiency estimates. Thus, a model adequately representing all available kinetic RT-PCR data is preferable, but no such model is currently in use for relative quantification. In this work, we use a logistic model for all kinetic data from each RT-PCR and propose a new method of efficiency-adjusted relative quantification based on the estimates from the fitted logistic models. This method allows incorporating multiple replicates and possibly multiple reference ('housekeeping') genes for estimating relative expression and corresponding confidence interval. Real kinetic RT-PCR data are used to compare the proposed and standard methods. The methods are applied to the clinical data from the ongoing study of guanylyl cyclase C as a biomarker for colorectal cancer.

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Statistical algorithm for assuring similar efficiency in standards and samples for absolute quantification by real-time reverse transcription polymerase chain reaction

Cited by 18 publications

References 29 publications

Validation of kinetics similarity in qPCR

Validation of kinetics similarity in qPCR

Detection and Quantification of Functional Genes of Cellulose- Degrading, Fermentative, and Sulfate-Reducing Bacteria and Methanogenic Archaea

Relative quantification based on logistic models for individual polymerase chain reactions

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