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DNA barcoding is widely used in fields, such as taxonomy and species identification. Conventional DNA barcoding sequences employ uninformative or repeat nucleotides in known groups of taxa within a monophylum. Herein, we propose a decision theory-based DNA barcode that tests for the ribulose bisphosphate carboxylase gene (rbcL). The proposed method can generate shorter DNA barcodes called single nucleotide polymorphism (SNP) tags, which shorten rbcL sequences from their full length (400–654 bp) to 25-bp DNA tags. These DNA tags are then represented by quick response (QR) codes containing the species names, accession numbers, and DNA tag sequences. Our proposed method can efficiently reduce data storage and provide DNA barcoding for various plant species.

SNP-SNP interaction studies focus on the investigation of associated variations in disease susceptibility. In recent years, increased attention has been focused on the analysis of huge combinations of SNPs in association studies. The large number of calculations required, however, often limits the application of statistical methods. In this study, we applied an evolutionary algorithm to facilitate statistical methods in the analysis of associated variations for disease susceptibility. The Gauss particle swarm optimization (Gauss PSO) algorithm was used to detect and identify the best protective association (i.e., combinations of SNPs with genotypes) with breast cancer. In the simulated data set, we systematically evaluated the combination effects of 26 SNPs from eight epigenetic modifier-related genes for breast cancer. The 2- to 6-order SNP combinations were identified for protective association with the risk of breast cancer (odds ratio

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.

Many association studies analyze the genotype frequencies of case and control data to predict susceptibility to diseases and cancers. An increasing number of studies has shown that the risk of getting diseases and cancers is associated with the co-occurrence of some contain single nucleotide polymorphisms (SNPs). Determining the disease-causing SNPs has become an important objective. In order to study the SNPSNP interaction in breast cancer, we used a particle swarm optimization (PSO) algorithm to compute the difference between the control and case data and performed a feature selection from different SNP combinations with their corresponding genotypes. The best combination of SNP-SNP interactions is the maximal difference of co-occurrences between the control and case groups. In this study, we explored the SNP interaction of 19 SNPs in 372 controls and 398 cases of breast cancer association using simulated SNP data of breast cancers. The odds ratio (OR) were used to evaluate the breast cancer risk in terms of the best combination of SNP-SNP interactions. Compared to their corresponding non-SNP combinations, the estimated OR of the best predicted SNP combination with genotypes for breast cancer is significantly greater than 1 (about 1.771 and 2.417, confidence interval (CI): 1.223-4.371, p

The polymerase chain reaction restriction fragment length polymorphism (PCR-RFLP) technique is often used in laboratories and many basic research studies of complex genetic diseases associated with single nucleotide polymorphisms (SNP). When performing PCR-RFLP for SNP genotyping, feasible primer pairs are subject to numerous constraints and require a restriction enzyme for discriminating the target SNP. In this study, we develop a method for natural PCR-RFLP primer design for SNP genotyping using a particle swarm optimization algorithm. The in silico simulation with SNPs of the SLC6A4 gene demonstrates that this method reliably produces designs for natural PCR-RFLP primers which best fit the common primer constraints and also identifies available restriction enzymes.

Designing a feasible primer pair is an important work before performing polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP) for single nucleotide polymorphism (SNP) genotyping. However, in many cases, no restriction enzymes are available to discriminate the target SNP, thus rendering the primer design useless. We propose a method that uses a genetic algorithm (GA) to search for optimal mutagenic PCR-RFLP primers and employ the core of SNP-RFLPing to reliably mine available restriction enzymes. The in silico simulation of the proposed method in the SNPs of the SLC6A4 gene showed that it is capable of designing stable mismatch PCR-RFLP primers which fit the common primer constraints and that it can provide available restriction enzymes.

A fuzzy system with dynamically adapt the inertia weight of the particle swarm optimization (FAPSO) had been implemented to select a specific feasible primer pair for PCR experiments. Overall, fifty accession nucleotide sequences between 1900 bps and 2100 bps were sampled for primer design with specific PCR product lengths of 150~300 bps and 500~800 bps. Total five hundred runs of the proposed primer design approaches were performed for each accession nucleotide sequence to calculate the optimum accuracy. The proposed approach is compared to standard PSO primer design method. The results generated in a dry dock experiment showed that the FAPSO primer design yielded approximately optimal primer sets and had a relatively short CPU-time than standard PSO. Related materials are available online at http://bio.kuas.edu.tw/fapso-pd/.

Many single nucleotide polymorphisms (SNPs) genotyping techniques have been developed but most of them are expensive. Polymerase chain reaction with confronting two-pair primers (PCR-CTPP) is a restriction enzyme-free and economic genotyping but its primer design is still computationally challenged. Here, we introduced a genetic algorithm (GA)-based PCR-CTPP primer design method. Thirty SNPs of the Janus kinase 2 gene with their SNP flanking length for 500 bps were tested. These GA-based designing CTPP primers were characterized with close values for melting temperature (Tm) and specificity, and their corresponding PCR products were provided with the optimal length. In conclusion, this novel PCR-CTPP primer designing method provides the computation for the primer information of cost- and time-effective enzyme-free SNP genotyping.