A Novel Teaching-Learning-Based Optimization for Improved Mutagenic Primer Design in Mismatch PCR-RFLP SNP Genotyping

Many single nucleotide polymorphisms (SNPs) for complex genetic diseases are genotyped by polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP) in small-scale basic research studies. It is an essential work to design feasible PCR-RFLP primer pair and find out available restriction enzymes to recognize the target SNP for PCR experiments. However, many SNPs are incapable of performing PCR-RFLP makes SNP genotyping become unpractical. A genetic algorithm (GA) had been proposed for designing mutagenic primer and get available restriction enzymes, but it gives an unrefined solution in mutagenic primers. In order to improve the mutagenic primer design, we propose TLBOMPD (TLBO-based Mutagenic Primer Design) a novel computational intelligence-based method that uses the notion of "teaching and learning" to search for more feasible mutagenic primers and provide the latest available restriction enzymes. The original Wallace's formula for the calculation of melting temperature is maintained, and more accurate calculation formulas of GC-based melting temperature and thermodynamic melting temperature are introduced into the proposed method. Mutagenic matrix is also reserved to increase the efficiency of judging a hypothetical mutagenic primer if involve available restriction enzymes for recognizing the target SNP. Furthermore, the core of SNP-RFLPing version 2 is used to enhance the mining work for restriction enzymes based on the latest REBASE. Twenty-five SNPs with mismatch PCR-RFLP screened from 288 SNPs in human SLC6A4 gene are used to appraise the TLBOMPD. Also, the computational results are compared with those of the GAMPD. In the future, the usage of the mutagenic primers in the wet lab needs to been validated carefully to increase the reliability of the method. The TLBOMPD is implemented in JAVA and it is freely available at http://tlbompd.googlecode.com/.