Multiobjective optimization-driven primer design mechanism: towards user-specified parameters of PCR primer.

Primers are critical for polymerase chain reaction (PCR) and influence PCR experimental outcomes. Designing numerous combinations of forward and reverse primers involves various primer constraints, posing a computational challenge. Most PCR primer design methods limit parameters because the available algorithms use general fitness functions. This study designed new fitness functions based on user-specified parameters and used the functions in a primer design approach based on the multiobjective particle swarm optimization (MOPSO) algorithm to address the challenge of primer design with user-specified parameters. Multicriteria evaluation was conducted simultaneously based on primer constraints. The fitness functions were evaluated using 7425 DNA sequences and compared with a predominant primer design approach based on optimization algorithms. Each DNA sequence was run 100 times to calculate the difference between the user-specified parameters and primer constraint values. The algorithms based on fitness functions with user-specified parameters outperformed the algorithms based on general fitness functions for 11 primer constraints. Moreover, MOPSO exhibited superior implementation in all experiments. Practical gel electrophoresis was conducted to verify the PCR experiments and established that MOPSO effectively designs primers based on user-specified parameters.

REHUNT: a reliable and open source package for restriction enzyme hunting.

BACKGROUND: Restriction enzymes are used frequently in biotechnology. However, manual mining of restriction enzymes is challenging. Furthermore, integrating available restriction enzymes into different bioinformatics systems is necessary for many biotechnological applications, such as polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP). Thus, in the present study, we developed the package REHUNT (Restriction Enzymes HUNTing), which mines restriction enzymes from the public database REBASE using a series of search operations. RESULTS: REHUNT is a reliable and open source package implemented in JAVA. It provides useful methods and manipulations for biological sequence analysis centered around restriction enzymes contained in REBASE. All available restriction enzymes for the imported biological sequences can be identified by REHUNT. Different genotypes can be identified using PCR-RFLP based on REHUNT for single nucleotide polymorphism (SNP), mutations, and the other variations. REHUNT robustly recognizes multiple inputs with different formats, e.g. regular DNA sequences, variation-in-sequence indicated by IUPAC code, as well as variation-in-sequence indicated by dNTPs format. Variations including di-, tri-, and tetra-allelic types and indel formats are also acceptable. Furthermore, REHUNT provides classified restriction enzymes output, including IUPAC and general sequence types, as well as commercial and non-commercial availabilities. REHUNT also enables analysis for high throughput screening (HTS) technologies. CONCLUSIONS: REHUNT is open source software with GPL v3 license and can be run on all platforms. Its features include: 1) Quick restriction enzymes search throughout a sequence based on the Boyer-Moore algorithm; 2) all available restriction enzymes provided and regularly updated from REBASE; 3) an open source API available of integrating all types of bioinformatics systems and applications; 4) SNP genotyping available for plant and animal marker-assisted breeding, and for human genetics; and 5) high throughput analysis available for Next Generation Sequencing (NGS). REHUNT not only to effectively looks for restriction enzymes in a sequence, but also available for SNP genotyping. Furthermore, it can be integrated into other biological and medical applications. REHUNT offers a convenient and flexible package for powerful restriction enzymes analyses in association studies, and supports high throughput analysis. The source codes and complete API documents are available at SourceForge: https://sourceforge.net/projects/rehunt/ , GitHub: https://github.com/yuhuei/rehunt , and at: https://sites.google.com/site/yhcheng1981/rehunt .

Effective Natural PCR-RFLP Primer Design for SNP Genotyping Using Teaching-Learning-Based Optimization With Elite Strategy.

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.

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/.

PCR-CTPP design for enzyme-free SNP genotyping using memetic algorithm.

In recent years, many single nucleotide polymorphisms (SNPs) have been successfully genotyped by polymerase chain reaction with confronting two-pair primers (PCR-CTPP). However, computation experiments of feasible CTPP primers are still challenging. The melting temperatures between four primers must be within a very narrow range, and many primer constraints need to be conformed to. PCR-CTPP is a simple, time- and cost-effective SNP genotyping method applied in molecular biology and biomedical fields. In this study, an MA (memetic algorithm)-based method is proposed to enable the design of feasible CTPP primer sets. Overall, 288 SNPs which exclude the deletion/insertion polymorphisms (DIPs) and multi-nucleotide polymorphisms (MNPs) in the SLC6A4 gene were tested in silico. The results were compared with a GA (genetic algorithm)-based method and indicate that the proposed method provides more feasible CTPP primers than the GA-based method. The MA-based CTPP primer design method provides critical melting temperatures and all kinds of evaluation of the common primer constraints. It could conceivably assist biologists and other researchers in obtaining feasible CTTP primer sets. The MA-CTPP algorithm is implemented in JAVA and a user-friendly input interface is freely available at http://bio.kuas.edu.tw/ma-ctpp/.

Estimation of teaching-learning-based optimization primer design using regression analysis for different melting temperature calculations.

Primers plays important role in polymerase chain reaction (PCR) experiments, thus it is necessary to select characteristic primers. Unfortunately, manual primer design manners are time-consuming and easy to get human negligence because many PCR constraints must be considered simultaneously. Automatic programs for primer design were developed urgently. In this study, the teaching-learning-based optimization (TLBO), a robust and free of algorithm-specific parameters method, is applied to screen primers conformed primer constraints. The optimal primer frequency (OPF) based on three known melting temperature formulas is estimated by 500 runs for primer design in each different number of generations. We selected optimal primers from fifty random nucleotide sequences of Homo sapiens at NCBI. The results indicate that the SantaLucia's formula is better coupled with the method to get higher optimal primer frequency and shorter CPU-time than the Wallace's formula and the Bolton and McCarthy's formula. Through the regression analysis, we also find the generations are significantly associated with the optimal primer frequency. The results are helpful for developing the novel TLBO-based computational method to design feasible primers.

Computational intelligence-based polymerase chain reaction primer selection based on a novel teaching-learning-based optimisation.

Specific primers play an important role in polymerase chain reaction (PCR) experiments, and therefore it is essential to find specific primers of outstanding quality. Unfortunately, many PCR constraints must be simultaneously inspected which makes specific primer selection difficult and time-consuming. This paper introduces a novel computational intelligence-based method, Teaching-Learning-Based Optimisation, to select the specific and feasible primers. The specified PCR product lengths of 150-300 bp and 500-800 bp with three melting temperature formulae of Wallace's formula, Bolton and McCarthy's formula and SantaLucia's formula were performed. The authors calculate optimal frequency to estimate the quality of primer selection based on a total of 500 runs for 50 random nucleotide sequences of 'Homo species' retrieved from the National Center for Biotechnology Information. The method was then fairly compared with the genetic algorithm (GA) and memetic algorithm (MA) for primer selection in the literature. The results show that the method easily found suitable primers corresponding with the setting primer constraints and had preferable performance than the GA and the MA. Furthermore, the method was also compared with the common method Primer3 according to their method type, primers presentation, parameters setting, speed and memory usage. In conclusion, it is an interesting primer selection method and a valuable tool for automatic high-throughput analysis. In the future, the usage of the primers in the wet lab needs to be validated carefully to increase the reliability of the method.

Improved candidate drug mining for Alzheimer's disease.

Alzheimer's disease (AD) is the main cause of dementia for older people. Although several antidementia drugs such as donepezil, rivastigmine, galantamine, and memantine have been developed, the effectiveness of AD drug therapy is still far from satisfactory. Recently, the single nucleotide polymorphisms (SNPs) have been chosen as one of the personalized medicine markers. Many pharmacogenomics databases have been developed to provide comprehensive information by associating SNPs with drug responses, disease incidence, and genes that are critical in choosing personalized therapy. However, we found that some information from different sets of pharmacogenomics databases is not sufficient and this may limit the potential functions for pharmacogenomics. To address this problem, we used approximate string matching method and data mining approach to improve the searching of pharmacogenomics database. After computation, we can successfully identify more genes linked to AD and AD-related drugs than previous online searching. These improvements may help to improve the pharmacogenomics of AD for personalized medicine.

Associate PCR-RFLP assay design with SNPs based on genetic algorithm in appropriate parameters estimation.

Polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP) is a commonly used laboratory technique and useful in small-scale basic research studies of complex genetic diseases that are associated with single nucleotide polymorphisms (SNPs). Before PCR-RFLP assay for SNP genotyping can be performed, a feasible primer pair observes numerous constraints and an available restriction enzyme for discriminating a target SNP, are required. The computation of feasible PCR-RFLP primers and find available restriction enzymes simultaneously aim at a target SNP is a challenging problem. Here, we propose an available method which combines the updated core of SNP-RFLPing with a genetic algorithm to reliably mine available restriction enzymes and search for feasible PCR-RFLP primers. We have in silico simulated the method in the SLC6A4 gene under different parameter settings and provided an appropriate parameter setting. The wet laboratory validation showed that it indeed usable in providing the available restriction enzymes and designing feasible primers that fit the common primer constraints. We have provided an easy and kindly interface to assist the researchers designing their PCR-RFLP assay for SNP genotyping. The program is implemented in JAVA and is freely available at http://bio.kuas.edu.tw/ganpd/.

Specific primer design for the polymerase chain reaction.

The design of primers has a major impact on the success of PCR in relation to the specificity and yield of the amplified product. Here, we introduce the applications of PCR as well as the definition and characteristics for PCR primer design. Recent primer design tools based on Primer3, along with several computational intelligence-based primer design methods which have been applied in primer design, are also reviewed. In addition, characteristics of population-based methods used in primer design are discussed in detail.

Drug-SNPing: an integrated drug-based, protein interaction-based tagSNP-based pharmacogenomics platform for SNP genotyping.

Many drug or single nucleotide polymorphism (SNP)-related resources and tools have been developed, but connecting and integrating them is still a challenge. Here, we describe a user-friendly web-based software package, named Drug-SNPing, which provides a platform for the integration of drug information (DrugBank and PharmGKB), protein-protein interactions (STRING), tagSNP selection (HapMap) and genotyping information (dbSNP, REBASE and SNP500Cancer). DrugBank-based inputs include the following: (i) common name of the drug, (ii) synonym or drug brand name, (iii) gene name (HUGO) and (iv) keywords. PharmGKB-based inputs include the following: (i) gene name (HUGO), (ii) drug name and (iii) disease-related keywords. The output provides drug-related information, metabolizing enzymes and drug targets, as well as protein-protein interaction data. Importantly, tagSNPs of the selected genes are retrieved for genotyping analyses. All drug-based and protein-protein interaction-based SNP genotyping information are provided with PCR-RFLP (PCR-restriction enzyme length polymorphism) and TaqMan probes. Thus, users can enter any drug keywords/brand names to obtain immediate information that is highly relevant to genotyping for pharmacogenomics research.

URPD: a specific product primer design tool.

BACKGROUND: Polymerase chain reaction (PCR) plays an important role in molecular biology. Primer design fundamentally determines its results. Here, we present a currently available software that is not located in analyzing large sequence but used for a rather straight-forward way of visualizing the primer design process for infrequent users. FINDINGS: URPD (yoUR Primer Design), a web-based specific product primer design tool, combines the NCBI Reference Sequences (RefSeq), UCSC In-Silico PCR, memetic algorithm (MA) and genetic algorithm (GA) primer design methods to obtain specific primer sets. A friendly user interface is accomplished by built-in parameter settings. The incorporated smooth pipeline operations effectively guide both occasional and advanced users. URPD contains an automated process, which produces feasible primer pairs that satisfy the specific needs of the experimental design with practical PCR amplifications. Visual virtual gel electrophoresis and in silico PCR provide a simulated PCR environment. The comparison of Practical gel electrophoresis comparison to virtual gel electrophoresis facilitates and verifies the PCR experiment. Wet-laboratory validation proved that the system provides feasible primers. CONCLUSIONS: URPD is a user-friendly tool that provides specific primer design results. The pipeline design path makes it easy to operate for beginners. URPD also provides a high throughput primer design function. Moreover, the advanced parameter settings assist sophisticated researchers in performing experiential PCR. Several novel functions, such as a nucleotide accession number template sequence input, local and global specificity estimation, primer pair redesign, user-interactive sequence scale selection, and virtual and practical PCR gel electrophoresis discrepancies have been developed and integrated into URPD. The URPD program is implemented in JAVA and freely available at http://bio.kuas.edu.tw/urpd/.

Single nucleotide polymorphism barcoding to evaluate oral cancer risk using odds ratio-based genetic algorithms.

Cancers often involve the synergistic effects of gene-gene interactions, but identifying these interactions remains challenging. Here, we present an odds ratio-based genetic algorithm (OR-GA) that is able to solve the problems associated with the simultaneous analysis of multiple independent single nucleotide polymorphisms (SNPs) that are associated with oral cancer. The SNP interactions between four SNPs-namely rs1799782, rs2040639, rs861539, rs2075685, and belonging to four genes (XRCC1, XRCC2, XRCC3, and XRCC4)-were tested in this study, respectively. The GA decomposes the SNPs sets into different SNP combinations with their corresponding genotypes (called SNP barcodes). The GA can effectively identify a specific SNP barcode that has an optimized fitness value and uses this to calculate the difference between the case and control groups. The SNP barcodes with a low fitness value are naturally removed from the population. Using two to four SNPs, the best SNP barcodes with maximum differences in occurrence between the case and control groups were generated by GA algorithm. Subsequently, the OR provides a quantitative measure of the multiple SNP synergies between the oral cancer and control groups by calculating the risk related to the best SNP barcodes and others. When these were compared to their corresponding non-SNP barcodes, the estimated ORs for oral cancer were found to be great than 1 [approx. 1.72-2.23; confidence intervals (CIs): 0.94-5.30, p < 0.03-0.07] for various specific SNP barcodes with two to four SNPs. In conclusion, the proposed OR-GA method successfully generates SNP barcodes, which allow oral cancer risk to be evaluated and in the process the OR-GA method identifies possible SNP-SNP interactions.

Mutagenic primer design for mismatch PCR-RFLP SNP genotyping using a genetic algorithm.

Polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP) is useful in small-scale basic research studies of complex genetic diseases that are associated with single nucleotide polymorphism (SNP). Designing a feasible primer pair is an important work before performing PCR-RFLP for SNP genotyping. However, in many cases, restriction enzymes to discriminate the target SNP resulting in the primer design is not applicable. A mutagenic primer is introduced to solve this problem. GA-based Mismatch PCR-RFLP Primers Design (GAMPD) provides a method that uses a genetic algorithm to search for optimal mutagenic primers and available restriction enzymes from REBASE. In order to improve the efficiency of the proposed method, a mutagenic matrix is employed to judge whether a hypothetical mutagenic primer can discriminate the target SNP by digestion with available restriction enzymes. The available restriction enzymes for the target SNP are mined by the updated core of SNP-RFLPing. GAMPD has been used to simulate the SNPs in the human SLC6A4 gene under different parameter settings and compared with SNP Cutter for mismatch PCR-RFLP primer design. The in silico simulation of the proposed GAMPD program showed that it designs mismatch PCR-RFLP primers. The GAMPD program is implemented in JAVA and is freely available at http://bio.kuas.edu.tw/gampd/.

A combined bisulfite restriction analysis bioinformatics tool: methyl-typing.

In this chapter, we introduce our developed freeware tool Methyl-Typing. It provides methylation-related bioinformatics with a special focus on combined bisulfite restriction analysis (COBRA). We give an overview of the implementation and program modules for Methyl-Typing. Various databases and methylation-related functions are integrated into Methyl-Typing and a helpful example is illustrated in detail. Several input protocols and their outputs for COBRA-related information are demonstrated, such as the inputs of multiple gene names in official gene symbols, multiple accession numbers for nucleotide sequence retrieval, multiple template sequences in a free format, primer sequences, and file uploads. The program goal of Methyl-Typing is to provide computation and visualization of the essential information for COBRA assay so that methylation can easily be analyzed by COBRA. It is a fast and efficient tool for providing all possible methylation sites of restriction enzymes.

Confronting two-pair primer design for enzyme-free SNP genotyping based on a genetic algorithm.

BACKGROUND: Polymerase chain reaction with confronting two-pair primers (PCR-CTPP) method produces allele-specific DNA bands of different lengths by adding four designed primers and it achieves the single nucleotide polymorphism (SNP) genotyping by electrophoresis without further steps. It is a time- and cost-effective SNP genotyping method that has the advantage of simplicity. However, computation of feasible CTPP primers is still challenging. RESULTS: In this study, we propose a GA (genetic algorithm)-based method to design a feasible CTPP primer set to perform a reliable PCR experiment. The SLC6A4 gene was tested with 288 SNPs for dry dock experiments which indicated that the proposed algorithm provides CTPP primers satisfied most primer constraints. One SNP rs12449783 in the SLC6A4 gene was taken as an example for the genotyping experiments using electrophoresis which validated the GA-based design method as providing reliable CTPP primer sets for SNP genotyping. CONCLUSIONS: The GA-based CTPP primer design method provides all forms of estimation for the common primer constraints of PCR-CTPP. The GA-CTPP program is implemented in JAVA and a user-friendly input interface is freely available at http://bio.kuas.edu.tw/ga-ctpp/.

SNP-RFLPing 2: an updated and integrated PCR-RFLP tool for SNP genotyping.

BACKGROUND: PCR-restriction fragment length polymorphism (RFLP) assay is a cost-effective method for SNP genotyping and mutation detection, but the manual mining for restriction enzyme sites is challenging and cumbersome. Three years after we constructed SNP-RFLPing, a freely accessible database and analysis tool for restriction enzyme mining of SNPs, significant improvements over the 2006 version have been made and incorporated into the latest version, SNP-RFLPing 2. RESULTS: The primary aim of SNP-RFLPing 2 is to provide comprehensive PCR-RFLP information with multiple functionality about SNPs, such as SNP retrieval to multiple species, different polymorphism types (bi-allelic, tri-allelic, tetra-allelic or indels), gene-centric searching, HapMap tagSNPs, gene ontology-based searching, miRNAs, and SNP500Cancer. The RFLP restriction enzymes and the corresponding PCR primers for the natural and mutagenic types of each SNP are simultaneously analyzed. All the RFLP restriction enzyme prices are also provided to aid selection. Furthermore, the previously encountered updating problems for most SNP related databases are resolved by an on-line retrieval system. CONCLUSIONS: The user interfaces for functional SNP analyses have been substantially improved and integrated. SNP-RFLPing 2 offers a new and user-friendly interface for RFLP genotyping that can be used in association studies and is freely available at http://bio.kuas.edu.tw/snp-rflping2.

An introduction to mitochondrial informatics.

In this chapter, we review the public resources available for human mitochondrial DNA and protein related bioinformatics, with a special focus on mitochondrial single nucleotide polymorphisms (mtSNPs). We also review our own freeware tool V-MitoSNP, giving an overview of its implementation and program workflow. Apart from these, we review several protocols for the graphic input of genes, keywords, gene searching by sequence, mtSNP searching by sequence, restriction enzyme mining, primer design, and virtual electrophoresis for PCR-RFLP genotyping. Some databases with similar function are integrated and compared.

Methyl-Typing: an improved and visualized COBRA software for epigenomic studies.

Combined bisulfite restriction analysis (COBRA) is one of the most commonly used methylation quantification methods. However, it focuses on relatively few restriction enzymes. Here, we present Methyl-Typing, a web-based software that provides restriction enzyme mining data for methyl-cytosine-containing sequences following bisulfite-conversion. Gene names, accession numbers, sequences, PCR primers, and file upload are accessible for input. Promoter sequences and restriction enzymes for CpG- and GpC-containing recognition sites are retrieved. Four representative enzymes were tested successfully by COBRA on the experimental work. Therefore, the Methyl-Typing tool provides a comprehensive COBRA-restriction enzyme mining. It is freely available at http://bio.kuas.edu.tw/methyl-typing.

Novel generating protective single nucleotide polymorphism barcode for breast cancer using particle swarm optimization.

BACKGROUND: High-throughput single nucleotide polymorphism (SNP) genotyping generates a huge amount of SNP data in genome-wide association studies. Simultaneous analyses for multiple SNP interactions associated with many diseases and cancers are essential; however, these analyses are still computationally challenging. METHODS: In this study, we propose an odds ratio-based binary particle swarm optimization (OR-BPSO) method to evaluate the risk of breast cancer. RESULTS: BPSO provides the combinational SNPs with their corresponding genotype, called SNP barcodes, with the maximal difference of occurrence between the control and breast cancer groups. A specific SNP barcode with an optimized fitness value was identified among seven SNP combinations within the space of one minute. The identified SNP barcodes with the best performance between control and breast cancer groups were found to be control-dominant, suggesting that these SNP barcodes may prove protective against breast cancer. After statistical analysis, these control-dominant SNP barcodes were processed for odds ratio analysis for quantitative measurement with regard to the risk of breast cancer. CONCLUSION: This study proposes an effective high-speed method to analyze the SNP-SNP interactions for breast cancer association study.