Department of Horticultural Science, North Carolina State University, Raleigh, NC, USA.
Mountain Crop Improvement Lab, Department of Horticultural Science, Mountain Horticultural Crops Research and Extension Center, North Carolina State University, Mills River, NC, USA.
Methods Mol Biol. 2023;2653:317-332. doi: 10.1007/978-1-0716-3131-7_20.
Quantitative real-time reverse transcription PCR (qRT-PCR) analysis has been used routinely to quantify gene expression levels. Primer design and the optimization of qRT-PCR parameters are critical for the accuracy and reproducibility of qRT-PCR analysis. Computational tool-assisted primer design often overlooks the presence of homologous sequences of the gene of interest and the sequence similarities between homologous genes in a plant genome. This sometimes results in skipping the optimization of qRT-PCR parameters due to the false confidence in the quality of the designed primers. Here we present a stepwise optimization protocol for single nucleotide polymorphisms (SNPs)-based sequence-specific primer design and sequential optimization of primer sequences, annealing temperatures, primer concentrations, and cDNA concentration range for each reference and target gene. The goal of this optimization protocol is to achieve a standard cDNA concentration curve with an R ≥ 0.9999 and efficiency (E) = 100 ± 5% for the best primer pair of each gene, which serves as the prerequisite for using the 2 method for data analysis.
定量实时逆转录聚合酶链反应 (qRT-PCR) 分析已被常规用于定量基因表达水平。引物设计和 qRT-PCR 参数的优化对于 qRT-PCR 分析的准确性和可重复性至关重要。基于计算工具的引物设计往往忽略了目的基因同源序列的存在以及植物基因组中同源基因之间的序列相似性。这有时会导致由于对设计引物质量的虚假信心而跳过 qRT-PCR 参数的优化。在这里，我们提出了一种基于单核苷酸多态性 (SNP) 的序列特异性引物设计和引物序列、退火温度、引物浓度和每个参考和靶基因的 cDNA 浓度范围的顺序优化的分步优化方案。该优化方案的目标是为每个基因的最佳引物对获得标准 cDNA 浓度曲线，其 R ≥ 0.9999 和效率 (E) = 100 ± 5%，这是使用 2 方法进行数据分析的前提。
