Quantitative PCR (qPCR) Data Analysis of SOD and its Activity and Expression in Elettaria cardamomum under Biotic Stress

Normalization to reference genes is the most common method to avoid bias in real-time quantitative PCR (qPCR), which has been widely used for quantification of gene expression. Despite several studies on gene expression,Lilium, and particularlyL. regale, has not been fully investigated regarding the evaluation of reference genes suitable for normalization. In this study, nine putative reference genes, namely18S rRNA,ACT,BHLH,CLA,CYP,EF1,GAPDH,SANDandTIP41, were analyzed for accurate quantitative PCR normalization at different developmental stages and under different stress conditions, including biotic (Botrytis elliptica), drought, salinity, cold and heat stress. All these genes showed a wide variation in their Cq (quantification Cycle) values, and their stabilities were calculated by geNorm, NormFinder and BestKeeper. In a combination of the results from the three algorithms,BHLHwas superior to the other candidates when all the experimental treatments were analyzed together;CLAandEF1were also recommended by two of the three algorithms. As for specific conditions,EF1under various developmental stages,SANDunder biotic stress,CYP/GAPDHunder drought stress, andTIP41under salinity stress were generally considered suitable. All the algorithms agreed on the stability ofSANDandGAPDHunder cold stress, while onlyCYPwas selected under heat stress by all of them. Additionally, the selection of optimal reference genes under biotic stress was further verified by analyzing the expression level ofLrLOXin leaves inoculated withB. elliptica. Our study would be beneficial for future studies on gene expression and molecular breeding ofLilium.

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Pairwise Efficiency: A new mathematical approach to qPCR data analysis increases the precision of the classical calibration curve assay

10.1101/399337 ◽

2018 ◽

Author(s):

Yulia Panina ◽

Arno Germond ◽

Brit G. David ◽

Tomonobu M. Watanabe ◽

Keyword(s):

Data Analysis ◽

Real Time ◽

Calibration Curve ◽

Quantitative Polymerase Chain Reaction ◽

Expression Ratio ◽

Qpcr Data ◽

Novel Approach ◽

Data Points ◽

Polymerase Chain ◽

New Formula

ABSTRACTThe real-time quantitative polymerase chain reaction (qPCR) is routinely used for quantification of nucleic acids and is considered the gold standard in the field of relative nucleic acid measurements. The efficiency of the qPCR reaction is one of the most important parameters that needs to be determined, reported, and incorporated into data analysis in any qPCR experiment. The Minimum Information for Publication of Quantitative Real-Time PCR Experiments (MIQE) guidelines recognize the calibration curve as the method of choice for estimation of qPCR efficiency. The precision of this method has been reported to be between SD=0.007 (3 replicates) and SD=0.022 (no replicates). In this manuscript we present a novel approach to analysing qPCR data obtained by running a dilution series. Unlike previously developed methods, our method relies on a new formula that describes pairwise relationships between data points on separate amplification curves and thus operates extensive statistics (hundreds of estimations). The comparison of our method with classical calibration curve by Monte Carlo simulation shows that our approach can almost double the precision of efficiency and gene expression ratio estimations on the same dataset.

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FaDA: A Shiny web application to accelerate common lab data analyses

10.1101/2020.04.23.055673 ◽

2020 ◽

Author(s):

Yodit Feseha ◽

Quentin Moiteaux ◽

Estelle Geffard ◽

Gérard Ramstein ◽

Sophie Brouard ◽

...

Keyword(s):

Principal Component Analysis ◽

Data Analysis ◽

Rna Sequencing ◽

Quantitative Pcr ◽

Principal Component ◽

Component Analysis ◽

Receiver Operating Curve ◽

Web Based ◽

Data Analyses ◽

Receiver Operating

AbstractBackgroundWeb-based data analysis and visualization tools are mostly designed for specific purposes, such as data from whole transcriptome RNA sequencing or single-cell RNA sequencing. However, limited efforts have been made to develop tools designed for data of common laboratory data for non-computational scientists. The importance of such web-based tool is stressed by the current increased samples capacity of conventional laboratory tools such as quantitative PCR, flow cytometry or ELISA.ResultsWe provide a web-based application FaDA, developed with the R Shiny package providing users to perform statistical group comparisons, including parametric and non-parametric tests, with multiple testing corrections suitable for most of the standard wet-lab analyses. FaDA provides data visualization such as heatmap, principal component analysis (PCA) and receiver operating curve (ROC). Calculations are performed through the R language.ConclusionsFaDA application provides a free and intuitive interface allowing biologists without bioinformatic skills to easily and quickly perform common lab data analyses. The application is freely accessible at https://shiny-bird.univ-nantes.fr/app/FadaAbbreviationsAUC: Area Under the Curve; FaDA: Fast Data Analysis; GEO: Gene Expression Omnibus; ELISA: enzyme-linked immunosorbent assay; PCA: Principal Component Analysis; qPCR: quantitative PCR; ROC: Receiver Operating Curve.

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qPCR data analysis: Better results through iconoclasm

Biomolecular Detection and Quantification ◽

10.1016/j.bdq.2019.100084 ◽

2019 ◽

Vol 17 ◽

pp. 100084 ◽

Cited By ~ 1

Author(s):

Joel Tellinghuisen ◽

Andrej-Nikolai Spiess

Keyword(s):

Data Analysis ◽

Qpcr Data

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Comprehensive evaluation of candidate reference genes for real-time quantitative PCR (RT-qPCR) data normalization in nutri-cereal finger millet [Eleusine Coracana (L.)]

PLoS ONE ◽

10.1371/journal.pone.0205668 ◽

2018 ◽

Vol 13 (10) ◽

pp. e0205668 ◽

Cited By ~ 4

Author(s):

Palakolanu Sudhakar Reddy ◽

Mahamaya G. Dhaware ◽

Dumbala Srinivas Reddy ◽

Bommineni Pradeep Reddy ◽

Kummari Divya ◽

...

Keyword(s):

Real Time ◽

Quantitative Pcr ◽

Reference Genes ◽

Finger Millet ◽

Comprehensive Evaluation ◽

Eleusine Coracana ◽

Data Normalization ◽

Qpcr Data ◽

Real Time Quantitative Pcr

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Easy and rapid method of zygosity determination in transgenic mice by SYBR® Green real-time quantitative PCR with a simple data analysis

Transgenic Research ◽

10.1007/s11248-006-9024-4 ◽

2006 ◽

Vol 16 (1) ◽

pp. 127-131 ◽

Cited By ~ 13

Author(s):

Karine Haurogné ◽

Jean-Marie Bach ◽

Blandine Lieubeau

Keyword(s):

Data Analysis ◽

Transgenic Mice ◽

Real Time ◽

Quantitative Pcr ◽

Rapid Method ◽

Real Time Quantitative Pcr

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Pairwise efficiency: a new mathematical approach to qPCR data analysis increases the precision of the calibration curve assay

BMC Bioinformatics ◽

10.1186/s12859-019-2911-5 ◽

2019 ◽

Vol 20 (1) ◽

Cited By ~ 2

Author(s):

Yulia Panina ◽

Arno Germond ◽

Brit G. David ◽

Tomonobu M. Watanabe

Keyword(s):

Data Analysis ◽

Calibration Curve ◽

Mathematical Approach ◽

Qpcr Data

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Use and Misuse of Cq in qPCR Data Analysis and Reporting

Life ◽

10.3390/life11060496 ◽

2021 ◽

Vol 11 (6) ◽

pp. 496

Author(s):

Adrián Ruiz-Villalba ◽

Jan M. Ruijter ◽

Maurice J. B. van den Hoff

Keyword(s):

Gene Expression ◽

Data Analysis ◽

Concentration Ratio ◽

Amplification Curve ◽

Qpcr Data ◽

Pcr Efficiency ◽

Qpcr Analysis ◽

Sampling Variation ◽

The Difference ◽

Threshold Setting

In the analysis of quantitative PCR (qPCR) data, the quantification cycle (Cq) indicates the position of the amplification curve with respect to the cycle axis. Because Cq is directly related to the starting concentration of the target, and the difference in Cq values is related to the starting concentration ratio, the only results of qPCR analysis reported are often Cq, ΔCq or ΔΔCq values. However, reporting of Cq values ignores the fact that Cq values may differ between runs and machines, and, therefore, cannot be compared between laboratories. Moreover, Cq values are highly dependent on the PCR efficiency, which differs between assays and may differ between samples. Interpreting reported Cq values, assuming a 100% efficient PCR, may lead to assumed gene expression ratios that are 100-fold off. This review describes how differences in quantification threshold setting, PCR efficiency, starting material, PCR artefacts, pipetting errors and sampling variation are at the origin of differences and variability in Cq values and discusses the limits to the interpretation of observed Cq values. These issues can be avoided by calculating efficiency-corrected starting concentrations per reaction. The reporting of gene expression ratios and fold difference between treatments can then easily be based on these starting concentrations.

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