However, this can only be achieved under optimized qPCR conditions. To the best of our knowledge, the SYBR Taq DNA polymerase can differentiate the SNPs in the last one or two nucleotides at the 3’-end of each primer between any two homologous sequences. Single-nucleotide polymorphisms (SNPs) are the only nucleotides that can discern the differences among these homologous gene sequences and can be used to design robust and sequence-specific qPCR primers for each gene. Highly similar (or identical) homologous genes often exist in a genome of interest due to genome and gene duplication. Also, qPrimerDB and MRPrimerW2 are the most comprehensive predesigned primer databases that contain genome sequences for 9 and 516 organisms, respectively 13, 14.Ī significant drawback of using these tools for primer design is that they largely ignore homologous genes and their sequence similarities for a gene of interest in a plant genome. On the contrary, PrimerBank, primer-BLAST, and MRPrimerW were developed to take advantage of the whole-genome sequence data to test off-target binding. However, these tools are unable to test off-target primer binding. Primer3Plus, Primique, BatchPrimer3, and QuantPrime were developed based on the Primer3 core algorithm and provide a user-friendly interface with rich filtering constraints, allowing primer ranking. Multiple computational tools have been developed for online primer design for qPCR analysis examples are Primer3Plus 5, Primique 6, BatchPrimer3 7, QuantPrime 8, PrimerBank 9, primer-BLAST 10, MRPrimerW 11, Oli2go 12, qPrimerDB 13, and MRPrimerW2 14. To date, primer design considerations primarily focus on target specificity, primer GC content, self-dimerization, and secondary structure formation 3, 4. The accuracy of qPCR analysis highly depends on (i) the specificity of sequence-specific primers, (ii) the optimization of qPCR amplification conditions, and (iii) the accuracy of transcript normalization using stably expressed reference genes. Relative quantification of gene expression permits the measurement of relative changes in gene expression without knowing the absolute quantity of the reference (or internal control) genes 2. Real-time reverse transcription PCR (i.e., real-time RT-PCR or qPCR) has become a powerful and widely used method for quantifying gene expression levels 1. Thus, these case studies demonstrated the effectiveness of our optimized protocol for qPCR analysis. We also applied this approach to test the expression stability of six reference genes in soybean under biotic stress treatment with Xanthomonas axonopodis pv. We applied our newly developed approach to identify the best reference genes in different tissues and at various inflorescence developmental stages of Tripidium ravennae, an ornamental and biomass grass, and validated their utility under varying abiotic stress conditions. As a result, an R 2 ≥ 0.9999 and the efficiency ( E) = 100 ± 5% should be achieved for the best primer pair of each gene, which serve as the prerequisite for using the 2 −ΔΔCt method for data analysis. By combining the efficiency calibrated and standard curve methods with the 2 −ΔΔCt method, the standard cDNA concentration curve with a logarithmic scale was obtained for each primer pair for each gene. Our approach started with a sequence-specific primer design that should be based on the single-nucleotide polymorphisms (SNPs) present in all the homologous sequences for each of the reference (and target) genes under study. Here, we proposed an optimized approach to sequentially optimizing primer sequences, annealing temperatures, primer concentrations, and cDNA concentration range for each reference (and target) gene. However, the optimization of qPCR parameters plays an essential role in the efficiency, specificity, and sensitivity of each gene’s primers. It can lead to false confidence in the quality of the designed primers, which sometimes results in skipping the optimization steps for qPCR. Computational tool-assisted primer design for real-time reverse transcription (RT) PCR (qPCR) analysis largely ignores the sequence similarities between sequences of homologous genes in a plant genome.
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