A Novel Chaotic Inertia Weight Particle Swarm Optimization for PCR Primer Design

Many primer design methods have been proposed to provide feasible primer sets for performing polymerase chain reaction (PCR) experiments. However, the majority of these methods require a long time to obtain a feasible solution since large quantities of DNA template need to be analyzed and a specific PCR product size is not provided. Recently, particle swarm optimization (PSO) has been applied to solve many problems and yield good results. However, it is easily traped into a local optimal solution when the problem is complicated. In this paper, a novel logistic map is proposed to determine the value of inertia weight of PSO to design feasible primers (NCIWPSO). Accuracies for the primer design of the Homo sapiens RNA binding motif protein 11 (RBM11), mRNA (NM_144770), and the Homo sapiens G protein-coupled receptor 78 (GPR78), mRNA (NM_080819) were calculated. Five hundred runs of PSO and the NCIWPSO primer design methods performed different PCR product lengths with different melting temperature calculations. A comparison of the accuracies for PSO and the NCIWPSO primer design showed that the NCIWPSO is superior to PSO for primer design. The proposed method is effective for finding more feasible primer sets than PSO.

[1]  Jain-Shing Wu,et al.  Primer design using genetic algorithm , 2004, Bioinform..

[2]  J. Tennyson.  In the wake of chaos. Unpredictable order in dynamical systems , 1995 .

[3]  Shang-Jeng Tsai,et al.  Efficient Population Utilization Strategy for Particle Swarm Optimizer , 2009, IEEE Trans. Syst. Man Cybern. Part B.

[4]  R. Eberhart,et al.  Fuzzy adaptive particle swarm optimization , 2001, Proceedings of the 2001 Congress on Evolutionary Computation (IEEE Cat. No.01TH8546).

[5]  Li-Yeh Chuang,et al.  Specific PCR product primer design using memetic algorithm , 2009, Biotechnology progress.

[6]  Li-Yeh Chuang,et al.  Primer Design with Specific PCR Product using Particle Swarm Optimization , 2009 .

[7]  Wei-Che Hsu,et al.  An efficient algorithm for minimal primer set selection , 2003, Bioinform..

[8]  C. A. Thomas,et al.  Molecular cloning. , 1977, Advances in pathobiology.

[9]  Jiangye Yuan,et al.  A modified particle swarm optimizer with dynamic adaptation , 2007, Appl. Math. Comput..

[10]  Andries Petrus Engelbrecht,et al.  A Cooperative approach to particle swarm optimization , 2004, IEEE Transactions on Evolutionary Computation.

[11]  Riccardo Poli,et al.  Particle swarm optimization , 1995, Swarm Intelligence.

[12]  J. Shaffer,et al.  Hybridization of synthetic oligodeoxyribonucleotides to ΦX 174 DNA: the effect of single base pair mismatch , 1979 .

[13]  Jing J. Liang,et al.  Comprehensive learning particle swarm optimizer for global optimization of multimodal functions , 2006, IEEE Transactions on Evolutionary Computation.

[14]  K. Itakura,et al.  Hybridization of synthetic oligodeoxyribonucleotides to phi chi 174 DNA: the effect of single base pair mismatch. , 1979, Nucleic acids research.

[15]  Stephen H. Kellert.  In the wake of chaos: Unpredictable order in dynamical systems , 1993 .

[16]  C. Y. Lin,et al.  Primer Design Assistant (PDA): a web-based primer design tool , 2003, Nucleic Acids Res..

[17]  Thomas Kämpke,et al.  Efficient primer design algorithms , 2001, Bioinform..

[18]  Wing-Kin Sung,et al.  G-PRIMER: greedy algorithm for selecting minimal primer set , 2004, Bioinform..

[19]  K. Mullis,et al.  Specific synthesis of DNA in vitro via a polymerase-catalyzed chain reaction. , 1987, Methods in enzymology.

[20]  Yoshiyuki Sakaki,et al.  A novel strategy to design highly specific PCR primers based on the stability and uniqueness of 3'-end subsequences , 2005, Bioinform..