PSO Algorithm for Primer Design
In recent years, polymerase chain reactions (PCR) have been widely applied in medical science. The PCR technique allows a small amount of DNA to be amplified exponentially, thus ensuring that the amount of DNA is sufficient for DNA sequence analysis or gene therapy. It is important to choose a feasible primer pair to work quickly and efficiently.
Recently, many kinds of primer design software were developed, but most of these do not allow the use of sequence accession numbers for primer design. Examples are Primer Design Assistant (PDA)  and GeneFisher . The system we introduce in this chapter is based on a particle swarm optimization (PSO) algorithm. It incorporates the RefSeq database, which enables users to enter sequence accession numbers directly, or to copy/paste entire sequences in order to design primer sets. The software interface allows users to easily design fitting primer sets according to their needs.
The user-friendly interface allows (1) accession number input, (2) sequence input, and (3) input of primer constraints. The proposed PSO algorithm helps in correctly and quickly identifying an optimal primer pair required for a specific PCR experiment. Finally, information about a feasible primer pair is graphically depicted on the user interface.
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- 1.Liu, W.-T. Primer set selection in multiple PCR experiments, 2004, pp. 9–24.Google Scholar
- 2.Wu, J.-S., Lee, C., Wu, C.-C. and Shiue, Y.-L. Primer design using genetic algorithm, Bioinformatics, 2004, pp. 1710–1717.Google Scholar
- 3.Vallone, P.M. and Butler, J.M. AutoDimer: A screening tool for primer-dimer and hairpin structures, BioTechniques vol. 37, 2004, pp. 226–231.Google Scholar
- 5.Shi, Y. and Eberhart, R.C. Empirical study of particle swarm optimization, in: Proceedings of the Evolutionary Computation 1999 Congress, vol. 3, 1999, pp. 1945–1950.Google Scholar
- 6.Chen, H.-C., Chang, C.-J. and Liu, C.-H. Research of particle swarm optimization question, in: First Conference on Taiwan Research Association and 2004 Technology and Management.Google Scholar
- 7.Chang, H.-W. and Lin, C.-H. Introduction of polymerase chain reaction, Nano-communication, vol. 12, no 1, 2005, pp. 6–11.Google Scholar
- 8.Meyer, F., Schleiermacher, C. and Giegerich, R. (1995) Genefisher software support for the detection of postulated genes [Online]. Available: http://bibiserv.techfak.ni-bielefild.de/docs.gf_paper.html.
- 9.Shi, Y. and Eberhart, R.C. A modified particle swarm optimizer, in: IEEE Proceedings of the Evolutionary Computation, vol. 3, 1999, pp. 1945–1950.Google Scholar
- 11.Kennedy, J. and Eberhart, R.C. A discrete binary version of particle swarm algorithm, System, Man, and Cybernetics. ‘Computational Cybernetics and Simulation,’ 1997 IEEE International Conference, vol. 5, Oct 12–15, 1997, pp. 4101–4108.Google Scholar
- 12.Kennedy, J., Eberhart, R. and Shi, Y. Swarm Intelligence. Morgan Kaufmann, San Francisco.Google Scholar
- 13.Oh, I.-S., Lee, J.-S. and Moon, B.-R. Hybrid genetic algorithms for feature selection, IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 26, no. 11, Nov. 2004.Google Scholar
- 15.Rahmat-Samii, Y. Genetic algorithm (GA) and particle swarm optimization (PSO) in engineering electromagnetics, in: Proceedings of the Seventeenth International Conference on Applied Electromagnetics and Communications, 2003, pp. 1–5.Google Scholar