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The polymerase chain reaction–restriction fragment length polymorphism (PCR-RFLP) experiment has the characteristics of low-cost, rapidity, simplicity, convenience, high sensitivity and high specificity; thus, many small and medium laboratories use it to perform all kinds of single nucleotide polymorphisms (SNPs) genotyping works, and as a molecular biotechnology for disease-related analysis. However, many single nucleotide polymorphisms lack available restriction enzymes to distinguish the specific genotypes on a target SNP, and that causes the PCR-RFLP assay which is unavailable to be called mismatch PCR-RFLP. In order to completely solve the problem of mismatch PCR-RFLP, we have created a teaching–learning-based optimization (TLBO) multi-point mutagenic primer design algorithm which, combined with REHUNT, provides a complete and specific restriction enzyme mining solution. The proposed method not only introduces several search strategies suitable for multi-point mutagenesis primers, but also enhances the reliability of mutagenic primer design. In addition, this study is also designed for more complex SNP structures (with multiple dNTPs and insertion and deletion) to provide specific solutions for SNP diversity. We tested against fifteen mismatch PCR-RFLP SNPs in the human SLC6A4 gene on the NCBI dbSNP database as experimental templates. The experimental results indicate that the proposed method is helpful for providing the multi-point mutagenic primers that meet the constrain conditions of PCR experiments.

The high-throughput antibiotic resistance gene (ARG) qPCR array, initially published in 2012, is increasingly used to quantify resistance and mobile determinants in environmental matrices. Continued utility of the array; however, necessitates improvements such as removing or redesigning questionable primer sets, updating targeted genes and coverage of available sequences. Towards this goal, a new primer design tool (EcoFunPrimer) was used to aid in identification of conserved regions of diverse genes. The total number of assays used for diverse genes was reduced from 91 old primer sets to 52 new primer sets, with only a 10% loss in sequence coverage. While the old and new array both contain 384 primer sets, a reduction in old primer sets permitted 147 additional ARGs and mobile genetic elements to be targeted. Results of validating the updated array with a mock community of strains resulted in over 98% of tested instances incurring true positive/negative calls. Common queries related to sensitivity, quantification and conventional data analysis (e.g. Ct cutoff value, and estimated genomic copies without standard curves) were also explored. A combined list of new and previously used primer sets is provided with a recommended set based on redesign of primer sets and results of validation.

Background MicroRNAs are small but biologically important RNA molecules. Although different methods can be used for quantification of microRNAs, quantitative PCR is regarded as the reference that is used to validate other methods. Several commercial qPCR assays are available but they often come at a high price and the sequences of the primers are not disclosed. An alternative to commercial assays is to manually design primers but this work is tedious and, hence, not practical for the design of primers for a larger number of targets. Results I have developed the software miRprimer for automatic design of primers for the method miR-specific RT-qPCR, which is one of the best performing microRNA qPCR methods available. The algorithm is based on an implementation of the previously published rules for manual design of miR-specific primers with the additional feature of evaluating the propensity of formation of secondary structures and primer dimers. Testing of the primers showed that 76 out of 79 primers (96%) worked for quantification of microRNAs by miR-specific RT-qPCR of mammalian RNA samples. This success rate corresponds to the success rate of manual primer design. Furthermore, primers designed by this method have been distributed to several labs and used successfully in published studies. Conclusions The software miRprimer is an automatic and easy method for design of functional primers for miR-specific RT-qPCR. The application is available as stand-alone software that will work on the MS Windows platform and in a developer version written in the Ruby programming language.

Background Targeted amplicon sequencing of the 16S ribosomal RNA gene is one of the key tools for studying microbial diversity. The accuracy of this approach strongly depends on the choice of primer pairs and, in particular, on the balance between efficiency, specificity and sensitivity in the amplification of the different bacterial 16S sequences contained in a sample. There is thus the need for computational methods to design optimal bacterial 16S primers able to take into account the knowledge provided by the new sequencing technologies. Results We propose here a computational method for optimizing the choice of primer sets, based on multi-objective optimization, which simultaneously: 1) maximizes efficiency and specificity of target amplification; 2) maximizes the number of different bacterial 16S sequences matched by at least one primer; 3) minimizes the differences in the number of primers matching each bacterial 16S sequence. Our algorithm can be applied to any desired amplicon length without affecting computational performance. The source code of the developed algorithm is released as the mopo16S software tool (Multi-Objective Primer Optimization for 16S experiments) under the GNU General Public License and is available at http://sysbiobig.dei.unipd.it/?q=Software#mopo16S . Conclusions Results show that our strategy is able to find better primer pairs than the ones available in the literature according to all three optimization criteria. We also experimentally validated three of the primer pairs identified by our method on multiple bacterial species, belonging to different genera and phyla. Results confirm the predicted efficiency and the ability to maximize the number of different bacterial 16S sequences matched by primers.

We present multiPrime, a novel tool that automatically designs minimal primer sets for targeted next‐generation sequencing, tailored to specific microbiomes or genes. MultiPrime enhances primer coverage by designing primers with mismatch tolerance and ensures both high compatibility and specificity. We evaluated the performance of multiPrime using a data set of 43,016 sequences from eight viruses. Our results demonstrated that multiPrime outperformed conventional tools, and the primer set designed by multiPrime successfully amplified the target amplicons. Furthermore, we expanded the application of multiPrime to 30 types of viruses and validated the work efficacy of multiPrime‐designed primers in 80 clinical specimens. The subsequent sequencing outcomes from these primers indicated a sensitivity of 94% and a specificity of 89%. This study introduces multiPrime, a novel tool tailored for the wide‐ranging detection of target sequences through targeted next‐generation sequencing (tNGS). By integrating degenerate primer design principles with effective mismatch handling, multiPrime demonstrates enhanced precision and specificity in the identification of diverse sequence types. This breakthrough presents a prospective pathway for optimizing the development of tNGS. In performance comparison, multiPrime excelled over conventional tools in terms of execution time, primer coverage, and the number of candidate primers. It offers a streamlined and versatile solution for the rapid and cost‐effective detection of diverse microbiomes. Highlights MultiPrime is a user‐friendly and one‐step tool for designing targeted next‐generation sequencing primer sets. It integrates degenerate primer design theory with mismatch handling, resulting in improved accuracy and specificity in detecting broad‐spectrum sequences. It outperformed conventional programs in terms of run time, primer number, and primer coverage. The versatility and potential of multiPrime are highlighted by its potential application in detecting single or multiple genes, exons, antisense strands, RNA, or other specific DNA segments.

While high efficiency and cost‐effectiveness are two merits of environmental DNA (eDNA) techniques for detecting aquatic organisms, the difficulty of designing species‐specific primers can result in significant expenditure of time and money. During the in silico stage of primer development, primer specificity is predicted with alignment techniques such as BLAST that is based on the number and position of the primer/nontarget template mismatches. However, we speculate that nonspecific amplification is influenced by additional parameters, which lead to inaccuracies of in silico prediction. We performed in vitro specificity tests for 38 species‐specific primers selected for seven fishes and six turtles, using single‐plex conventional PCR (cPCR). A subset of 12 primer pairs were further tested with SYBR Green‐based or TaqMan‐based single‐plex quantitative PCR (qPCR). We disentangle the relative importance of mismatch properties (types and positions), primer properties (length, GC content, and 3′ end stability), PCR conditions (template concentrations and annealing temperatures), and PCR technique (cPCR, TaqMan‐based, or SYBR Green‐based qPCR) in determining the occurrence of amplifications. We then compared the PCR outcomes with the specificity check under two stringency scenarios based on alignment (i.e., BLAST search). We conducted a total of 679 cPCR and 226 qPCR analyses, with 90% of the reactions tested with nontarget templates. Primer pairs predicted by Primer‐BLAST to be specific rarely showed such specificity during the in vitro testing. BLAST searches correctly predicted the outcomes of around 67% of cPCR and qPCR, but had low sensitivity in detection of nontarget amplification (29–57%). Primer specificity increased significantly with total number of mismatches and annealing temperature, but decreased with higher GC content in the primer sequence. Mismatches that consisted of A‐A, G‐A, and C‐C pairings exerted 56% stronger reduction in nonspecific amplification effects than other mismatches. To conclude, we show that the prediction of primer specificity based only on the number and position of mismatches can be misleading. Our findings can be applied to increase the efficiency of the in silico primer selection process to maintain the relatively high efficiency and cost‐effectiveness of eDNA techniques.

Background Multiplex PCR amplifies numerous targets in a single tube reaction and is essential in molecular biology and clinical diagnostics. One of its most important applications is in the targeted sequencing of pathogens. Despite this importance, few tools are available for designing multiplex primers. Results We developed primerJinn, a tool that designs a set of multiplex primers and allows for the in silico PCR evaluation of primer sets against numerous input genomes. We used primerJinn to create a multiplex PCR for the sequencing of drug resistance-conferring gene regions from Mycobacterium tuberculosis , which were then successfully sequenced. Conclusions primerJinn provides a user-friendly, efficient, and accurate method for designing multiplex PCR primers for targeted sequencing and performing in silico PCR. It can be used for various applications in molecular biology and bioinformatics research, including the design of assays for amplifying and sequencing drug-resistance-conferring regions in important pathogens.

In December 2019, a new coronavirus disease (COVID-19) outbreak occurred in Wuhan, China. Severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2), which is the seventh coronavirus known to infect humans, is highly contagious and has rapidly expanded worldwide since its discovery. Quantitative nucleic acid testing has become the gold standard for diagnosis and guiding clinical decisions regarding the use of antiviral therapy. However, the RT-qPCR assays targeting SARS-CoV-2 have a number of challenges, especially in terms of primer design. Primers are the pivotal components of a RT-qPCR assay. Once virus mutation and recombination occur, it is difficult to effectively diagnose viral infection by existing RT-qPCR primers. Some primers and probes have also been made available on the WHO website for reference. However, no previous review has systematically compared the previously reported primers and probes and described how to design new primers in the event of a new coronavirus infection. This review focuses on how primers and probes can be designed methodically and rationally, and how the sensitivity and specificity of the detection process can be improved. This brief review will be useful for the accurate diagnosis and timely treatment of the new coronavirus pneumonia.

The high prevalence of antibiotic resistant bacteria (ARB) in several environments is a great concern threatening human health. Particularly, wastewater treatment plants (WWTP) become important contributors to the dissemination of ARB to receiving water bodies, due to the inefficient management or treatment of highly antibiotic-concentrated wastewaters. Hence, it is vital to develop molecular tools that allow proper monitoring of the genes encoding resistances to these important therapeutic compounds (antibiotic resistant genes, ARGs). For an accurate quantification of ARGs, there is a need for sensitive and robust qPCR assays supported by a good design of primers and validated protocols. In this study, eleven relevant ARGs were selected as targets, including aadA and aadB (conferring resistance to aminoglycosides); ampC , bla TEM , bla SHV , and mecA (resistance to beta-lactams); dfrA 1 (resistance to trimethoprim); ermB (resistance to macrolides); fosA (resistance to fosfomycin); qnrS (resistance to quinolones); and tetA (A) (resistance to tetracyclines). The in silico design of the new primer sets was performed based on the alignment of all the sequences of the target ARGs (orthology grade > 70%) deposited in the Kyoto Encyclopedia of Genes and Genomes (KEGG) database, allowing higher coverages of the ARGs’ biodiversity than those of several primers described to date. The adequate design and performance of the new molecular tools were validated in six samples, retrieved from both natural and engineered environments related to wastewater treatment. The hallmarks of the optimized qPCR assays were high amplification efficiency (> 90%), good linearity of the standard curve ( R 2  > 0.980), repeatability and reproducibility across experiments, and a wide linear dynamic range. The new primer sets and methodology described here are valuable tools to upgrade the monitorization of the abundance and emergence of the targeted ARGs by qPCR in WWTPs and related environments.

Background Targeted amplicon sequencing (TAS) is a high-throughput genotyping method in which markers can be designed at desired positions. However, genotyping by TAS requires a genome-wide primer design and complex post-sequencing analyses, which are difficult for researchers who are not familiar with bioinformatics. There was a demand for an environment where researchers could easily perform data analysis for genotyping by TAS. Results In this study, we developed the primer design tool MKDESIGNER and the post-sequencing analysis tool TASEQ. Using these tools, users can complete the process of primer design for TAS with just three commands, and they can also obtain the files necessary for genetic analysis with just four commands. The strategy of MKDESIGNER is that it designs as many markers as possible and then thins them out to the necessary number. This allows users to design markers that are more evenly distributed. It is also possible to reduce the density of markers around the centromere. We performed genotyping by TAS using these tools and achieved a success rate close to that reported in previous studies (approximately 80%). Conclusion MKDESIGNER and TASEQ contribute to easy implementation of genotyping by TAS in environments where next-generation sequencers are available. They are implemented in Python and are freely available in Bioconda. The source codes are available on GitHub. ( https://github.com/KChigira/mkdesigner , https://github.com/KChigira/taseq ).