Embedded Files
Primers are critical for polymerase chain reaction (PCR) and influence the success of PCR experiments. Designing large combinations of forward and reverse primers involves various primer constraints, which poses a computational challenge. Most PCR primer design methods limit setting optimal primer parameters because the available algorithms use a single-objective strategy based on primer constraints. This study proposes a new primer design strategy based on a multiobjective approach, called multiobjective primer design, to address the challenge of primer design according to the optimal parameter settings of users. The proposed multiobjective function simultaneously conducts multicriteria evaluation based on primer constraints. Multiobjective particle swarm optimization (MOPSO) was used to determine optimal primers. Single-objective and multiobjective primer design algorithms were evaluated using 2036 DNA sequences. Optimal parameters were set, and each DNA sequence was run 100 times to calculate the difference between the optimal parameters and primer constraint values. The multiobjective primer design had better performance than the single-objective primer design for 11 primer constraints. Practical gel electrophoresis was conducted to verify the PCR experiments, and the wet-laboratory validation established that MOPSO provides feasible primer design. The novel computational approach can help to further develop primer design algorithms for specific experimental situations.
Primers are critical for polymerase chain reaction (PCR) and influence the success of PCR experiments. Designing large combinations of forward and reverse primers involves various primer constraints, which poses a computational challenge. Most PCR primer design methods limit setting optimal primer parameters because the available algorithms use a single-objective strategy based on primer constraints. This study proposes a new primer design strategy based on a multiobjective approach, called multiobjective primer design, to address the challenge of primer design according to the optimal parameter settings of users. The proposed multiobjective function simultaneously conducts multicriteria evaluation based on primer constraints. Multiobjective particle swarm optimization (MOPSO) was used to determine optimal primers. Single-objective and multiobjective primer design algorithms were evaluated using 2036 DNA sequences. Optimal parameters were set, and each DNA sequence was run 100 times to calculate the difference between the optimal parameters and primer constraint values. The multiobjective primer design had better performance than the single-objective primer design for 11 primer constraints. Practical gel electrophoresis was conducted to verify the PCR experiments, and the wet-laboratory validation established that MOPSO provides feasible primer design. The novel computational approach can help to further develop primer design algorithms for specific experimental situations.
The MOPSO has been published in Briefings in Bioinformatics.
Journal: Briefings in Bioinformatics
Publisher: OXFORD
Indexed in Journal Citation Report (JCR)
Indexed in WoS (Q1)
Impact Factor: 11.622
Rank: 2/58 [97.41%], MATHEMATICAL & COMPUTATIONAL BIOLOGY
Cite
Cheng-Hong Yang, Yu-Huei Cheng, Emirlyn Cheng Yang, Li-Yeh Chuang, Yu-Da Lin. Multiobjective optimization-driven primer design mechanism: towards user-specified parameters of PCR primer. Briefings in Bioinformatics, bbac121, 2022.
Why use multiobjective primer design?
Why use multiobjective primer design?
A computational intelligence–based method for multiobjective primer design that comprehensively evaluates primer constraints through a multiobjective function is presented;
Because multiobjective primer design is based on using heuristic methods to determine optimal primers, the proposed method can be applied with numerous DNA templates;
Multiobjective primer design involves an automated process that can produce viable primer pairs to meet specific experimental design requirements;
The feasibility of multiobjective primer design is verified through practical gel electrophoresis in PCR experiments, and wet-laboratory validation established that multiobjective primer design can provide feasible primers;
Multiobjective primer design allows users to select their optimal primer settings, including optimal temperature, optimal temperature difference between forward and reverse primers, optimal length difference of forward and reverse primers, optimal GC proportion (GC%), optimal number of allowed dimers, and optimal number of allowed hairpins.
Multiobjective primer design system
Multiobjective primer design system
Multiobjective primer design was developed using JAVA programming.
The parameters of MOPSO algorithm include
maximum iteration size,
particle size,
maximum pbest Pareto set size (i.e., max_pbest),
maximum gbest Pareto set size (i.e., max_gbest).
Information regarding the primer constraints includes
primer length range,
primer length difference,
melting temperature range (°C),
Na+ (molar sodium concentration),
GC proportion range (%),
PCR product length (bps),
dimer (cross-dimer and self-dimer) annealing number (bps),
hairpin annealing number (bps),
specificity for mismatches allowed (bps).
The optimal user settings include
primer length difference,
melting temperature (°C),
melting temperature difference (°C),
GC proportion (%),
number of hairpins (bps),
number of dimers (cross-dimers and self-dimers) (bps).
The web interface for MOPD is available at http://203.64.101.172/mopd/
Page updated
Google Sites
Report abuse