PCR-RFLP Primer Design Using Particle Swarm Optimization Combined with Chaotic Logistic Map

Abstract

Polymerase chain reaction restriction fragment length polymorphism (PCR-RFLP) is an important technique in basic research of complex genetic diseases associated with single nucleotide polymorphisms (SNPs). Performing PCR-RFLP for SNP genotyping requires feasible primer pairs subject to numerous constraints and, in turn, requires a restriction enzyme to discriminate the target SNP. However, designing optimal primers from large quantities of template DNA is time consuming and often fails to provide a specific size of PCR product. In this study, a chaotic logistic map (CLM) is proposed to determine the value of the inertia weight of PSO (CLMPSO) to design feasible primers. The in silico simulation for SNPs of the SLC6A4 gene demonstrates that this CLM method reliably produces designs for PCR-RFLP primers which best fit the common primer constraints and also identifies available restriction enzymes. A comparison of the results obtained from PSO and CLMPSO primer design showed that CLM provided better primer sets than PSO primer design.