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Optimization of multiplex quantitative polymerase chain reaction based on response surface methodology and an artificial neural network-genetic algorithm approach.

ABSTRACT: Multiplex quantitative polymerase chain reaction (qPCR) has found an increasing range of applications. The construction of a reliable and dynamic mathematical model for multiplex qPCR that analyzes the effects of interactions between variables is therefore especially important. This work aimed to analyze the effects of interactions between variables through response surface method (RSM) for uni- and multiplex qPCR, and further optimize the parameters by constructing two mathematical models via RSM and back-propagation neural network-genetic algorithm (BPNN-GA) respectively. The statistical analysis showed that Mg2+ was the most important factor for both uni- and multiplex qPCR. Dynamic models of uni- and multiplex qPCR could be constructed using both RSM and BPNN-GA methods. But RSM was better than BPNN-GA on prediction performance in terms of the mean absolute error (MAE), the mean square error (MSE) and the Coefficient of Determination (R2). Ultimately, optimal parameters of uni- and multiplex qPCR were determined by RSM.

SUBMITTER: Pan P

PROVIDER: S-EPMC6059488 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

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Optimization of multiplex quantitative polymerase chain reaction based on response surface methodology and an artificial neural network-genetic algorithm approach.

Pan Ping P Jin Weifeng W Li Xiaohong X Chen Yi Y Jiang Jiahui J Wan Haitong H Yu Daojun D

PloS one 20180725 7

Multiplex quantitative polymerase chain reaction (qPCR) has found an increasing range of applications. The construction of a reliable and dynamic mathematical model for multiplex qPCR that analyzes the effects of interactions between variables is therefore especially important. This work aimed to analyze the effects of interactions between variables through response surface method (RSM) for uni- and multiplex qPCR, and further optimize the parameters by constructing two mathematical models via R ...[more]

PMID: 30044832

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Project description:BACKGROUND AND OBJECTIVES:The variable response of hepatitis C virus (HCV) genotypes towards anti-viral treatment requires prior information on the genotype status before planning a therapeutic strategy. Although assays for typing or subtyping of HCV are available, however, a fast and reliable assay system is still needed. The present study was planned to develop a single-step multiplex quantitative real time polymerase chain reaction (qPCR) assay to determine HCV genotypes in patients' sera. METHODS:The conserved sequences from 5' UTR, core and NS5b regions of HCV genome were used to design primers and hydrolysis probes labeled with fluorophores. Starting with the standardization of singleplex (qPCR) for each individual HCV-genotype, the experimental conditions were finally optimized for the development of multiplex assay. The sensitivity and specificity were assessed both for singleplex and multiplex assays. Using the template concentration of 102 copies per microliter, the value of quantification cycle (Cq) and the limit of detection (LOD) were also compared for both singleplex and multiplex assays. Similarly, the merit of multiplex assay was also compared with sequence analysis and restriction fragment length polymorphism (RFLP) techniques used for HCV genotyping. In order to find the application of multiplex qPCR assay, it was used for genotyping in a panel of 98 sera positive for HCV RNA after screening a total number of 239 patients with various liver diseases. RESULTS:The results demonstrated the presence of genotype 1 in 26 of 98 (26.53%) sera, genotype 3 in 65 (66.32%) and genotype 4 in 2 (2.04%) sera samples, respectively. One sample showed mixed infection of genotype 1 and 3. Five samples could not show the presence of any genotype. Genotypes 2, 5 and 6 could not be detected in these sera samples. The analysis of sera by singleplex and RFLP indicated the results of multiplex to be comparable with singleplex and with clear merit of multiplex over RFLP. In addition, the results of multiplex assay were also found to be comparable with those from sequence analysis. The sensitivity, specificity, Cq values and LOD values were compared and found to be closely associated both for singleplex and multiplex assays. CONCLUSION:The multiplex qPCR assay was found to be a fast, specific and sensitive method that can be used as a technique of choice for HCV genotyping in all routine laboratories.

Dataset Information

Optimization of multiplex quantitative polymerase chain reaction based on response surface methodology and an artificial neural network-genetic algorithm approach.

Publications

Optimization of multiplex quantitative polymerase chain reaction based on response surface methodology and an artificial neural network-genetic algorithm approach.

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