Introducing the f0% method: a reliable and accurate approach for qPCR analysis

Mahmoud Gamal; Marwa A Ibrahim

doi:10.1186/s12859-024-05630-y

Introducing the f₀% method: a reliable and accurate approach for qPCR analysis

BMC Bioinformatics. 2024 Jan 11;25(1):17. doi: 10.1186/s12859-024-05630-y.

Authors

Mahmoud Gamal¹, Marwa A Ibrahim²

Affiliations

¹ Department of Biochemistry and Molecular Biology, Faculty of Veterinary Medicine, Cairo University, Giza, 12211, Egypt. mahmoud.gamal@cu.edu.eg.
² Department of Biochemistry and Molecular Biology, Faculty of Veterinary Medicine, Cairo University, Giza, 12211, Egypt.

Abstract

Background: qPCR is a widely used technique in scientific research as a basic tool in gene expression analysis. Classically, the quantitative endpoint of qPCR is the threshold cycle (C_T) that ignores differences in amplification efficiency among many other drawbacks. While other methods have been developed to analyze qPCR results, none has statistically proven to perform better than the C_T method. Therefore, we aimed to develop a new qPCR analysis method that overcomes the limitations of the C_T method. Our f₀% [eff naught percent] method depends on a modified flexible sigmoid function to fit the amplification curve with a linear part to subtract the background noise. Then, the initial fluorescence is estimated and reported as a percentage of the predicted maximum fluorescence (f₀%).

Results: The performance of the new f₀% method was compared against the C_T method along with another two outstanding methods-LinRegPCR and Cy₀. The comparison regarded absolute and relative quantifications and used 20 dilution curves obtained from 7 different datasets that utilize different DNA-binding dyes. In the case of absolute quantification, f₀% reduced CV%, variance, and absolute relative error by 1.66, 2.78, and 1.8 folds relative to C_T; and by 1.65, 2.61, and 1.71 folds relative to LinRegPCR, respectively. While, regarding relative quantification, f₀% reduced CV% by 1.76, 1.55, and 1.25 folds and variance by 3.13, 2.31, and 1.57 folds regarding C_T, LinRegPCR, and Cy₀, respectively. Finally, f₀% reduced the absolute relative error caused by LinRegPCR by 1.83 folds.

Conclusions: We recommend using the f₀% method to analyze and report qPCR results based on its reported advantages. Finally, to simplify the usage of the f₀% method, it was implemented in a macro-enabled Excel file with a user manual located on https://github.com/Mahmoud0Gamal/F0-perc/releases .

Keywords: Calibration curve; Curve analysis; Delta CT; Inflection cycle; PCR efficiency; Performance indicators; Real-time PCR; Variation between replicates.