Assessment of mathematical approaches for the estimation and comparison of efficiency in qPCR assays for a prokaryotic model
Guardado en:
| Autores: | , , , , , |
|---|---|
| Formato: | artículo original |
| Fecha de Publicación: | 2024 |
| Descripción: | Quantitative PCR is a molecular technique for DNA quantification that depends on reaction efficiency and the Ct value (“cycle threshold”). However, the results are dependent on laboratory conditions and mathematical approaches. Thus, the data of 16 genes from Pseudomonas aeruginosa strain AG1 were generated using qPCR to assess the effect of DNA concentration and three mathematical methods (a standard curve and two individual-curve-based approaches called exponential and sigmoidal models) on efficiency and DNA quantification. Differences in efficiency were revealed depending on the mathematical method used; the values were 100% in three out of the four standard curves, but estimations of the expected fold change in DNA serial dilutions were not achieved, indicating the possible overestimation of efficiency. Moreover, when efficiency was compared to DNA concentration, a decreasing trend in efficiency as DNA concentration increased in the reaction was observed in most cases, which is probably related to PCR inhibitors. For all 16 genes at a single DNA concentration, the efficiencies for the exponential model were found in the range of 1.5–2.79 (50–79%), and for the sigmoidal approach, the range was 1.52–1.75 (52–75%), with similar impact on normalized expression values, as indicated by the genes for standard curves. Jointly, DNA concentration and mathematical model choice were demonstrated to impact the estimation of reaction efficiency and, subsequently, DNA quantification when using qPCR. |
| País: | Kérwá |
| Institución: | Universidad de Costa Rica |
| Repositorio: | Kérwá |
| Lenguaje: | Inglés |
| OAI Identifier: | oai:kerwa.ucr.ac.cr:10669/91722 |
| Acceso en línea: | https://hdl.handle.net/10669/91722 https://doi.org/10.3390/dna4030012 |
| Palabra clave: | GENES MATHEMATICAL MODEL QUANTIFICATION EFFICIENCY ESTIMATION COMPARISON |