TLBONPD-elite

SNP (single nucleotide polymorphism) genotyping is the determination of genetic variations of SNPs between members of a species. In many laboratories, PCR-RFLP (polymerase chain reaction-restriction fragment length polymorphism) is a usually used biotechnology for SNP genotyping, especially in small-scale basic research studies of complex genetic diseases. PCR-RFLP requires an available restriction enzyme at least for identify a target SNP and an effective primer pair conforms numerous constraints. However, the lots of restriction enzymes, tedious sequence and complicated constraints make the mining of available restriction enzymes and the design of effective primer pairs become a major challenge. In the study, we propose a novel and available CI (Computation Intelligence)-based method called TLBO (teaching-learning-based optimization) and introduce the elite strategy to design effective primer pairs. Three common melting temperature computations are available in the method. REHUNT (Restriction Enzymes HUNTing) is first combined with the method to mine available restriction enzymes. Robust in silico simulations for the GA (genetic algorithm), the PSO (particle swarm optimization), and the method for natural PCR-RFLP primer design in the SLC6A4 gene with two hundred and eighty-eight SNPs had been performed and compared. These methods had been implemented in JAVA and they are freely available at https://sites.google.com/site/yhcheng1981/tlbonpd-elite for users of academic and non-commercial interests.

Index Terms—SNP genotyping, polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP), primer design, teaching-learning-based optimization (TLBO), elite strategy.