MOTIVATION: Polymerase chain reaction (PCR) has been a revolutionary biomedical advancement. However, for PCR to be appropriately used, one must spend a significant amount of effort on PCR primer design. Carefully designed PCR primers not only increase sensitivity and specificity, but also decrease effort spent on experimental optimization. Computer software removes the human element by performing and automating the complex and rigorous calculations required in PCR primer design. Classification and review of the available software options and their capabilities should be a valuable resource for any PCR application. RESULTS: This article focuses on currently available free PCR primer design software and their major functions (https://pcrprimerdesign.github.io/). The software are classified according to their PCR applications, such as Sanger sequencing, reverse transcription quantitative PCR, single nucleotide polymorphism detection, splicing variant detection, methylation detection, microsatellite detection, multiplex PCR and targeted next generation sequencing, and conserved/degenerate primers to clone orthologous genes from related species, new gene family members in the same species, or to detect a group of related pathogens. Each software is summarized to provide a technical review of their capabilities and utilities.

Materials and methods For screening, a single biological sample is used for each control and treatment group. If screening shows interesting findings, confirmation follows with more biological replicates and a definitive statistical analysis using pooled data from the screening and confirmation steps. This experimental design for qPCR reduces reagent cost and labor without sacrificing sensitivity. To quantitate gene expression, we selected the comparative Ct method due to its simplicity, intuitiveness, and popularity. For statistical analysis, however, this two-step approach raised an interesting question; which parameter should be used for statistical analysis of the data from the screening step. With a single biological sample, the statistical analysis relies on the technical replicates of qPCR. Even though it is natural to assume a normal distribution of the relative gene expression levels of biological replicates based on the central limit theorem, it is unknown for technical replicates because the Ct values of the technical replicates have been non-linearly transformed to obtain relative gene expression levels. Hence, we studied the distribution of delta-Ct and relative gene expression levels from our qPCR data and found that the distribution of delta-Ct is symmetrical and approximately normal while the distribution of relative gene expression levels is skewed. Therefore, we chose deltaCt as our parameter for statistical analysis of the technical replicates from the screening step. To automate the data analysis of the screening step, we developed REALPLOT. A text file is used to enter Ct values and gene/ group information into REALPLOT that calculates relative gene expression, performs statistical analysis, and graphically displays the screening step data. To automate the data analysis after the confirmation step, we developed REALPOOL that pools the data from both the screening and confirmation steps, performs the final statistical analysis, and generates a publishable graphical representation. REALPLOT and REALPOOL were developed using the open-source statistical R environment. They are compatible with all major computer operating systems.