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Verification of Pathotyping by Quasispecies Model

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Emerging Intelligent Computing Technology and Applications (ICIC 2009)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 5754))

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Abstract

Discrimination using genetic diversity provides a significant support in genetic research and applications. Mostly, DNA markers indicate a process of determining the genotype presented at specific locations along the DNA molecule. Some developed DNA marker methods are RFLP, RAPD, AP-PCR, DAF, and AFLP. For these systems, enzymes play an important role. In this paper, we propose a mechanism to verify the enzyme efficacy for pathotyping. A procedure is given to inspect the validation on cleavage pattern by restriction enzymes, adapting the concept of genetic algorithm to quasispecies model – a genetic evolutionary processes of self-replicating macromolecules. The proposed mechanism is applied to viral strains of HPV (Papillomaviridae), including mutated strains from quasispecies model of homozygous, heterozygous, and various genetic variations. In the analysis of full length DNA strain PCR-RFLP subtyping, results showed that if digested patterns of HPV can be discriminated by specific enzyme set from non-high-risk and other papillomavirus, then it is also can be discriminated by the same enzyme set, under the condition of mutated simulation with quasispecies model. In addition, a measure of genetic diversity also evaluates the utility for PCR-RFLP markers in pathotyping, depending on the degree of digestion variation. We provide a specific and valid mechanism of examination on PCR-based pathotyping. Our approach offers a practical and verifiable direction for genomic pathotyping.

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© 2009 Springer-Verlag Berlin Heidelberg

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Peng, SL., Tsay, YW. (2009). Verification of Pathotyping by Quasispecies Model. In: Huang, DS., Jo, KH., Lee, HH., Kang, HJ., Bevilacqua, V. (eds) Emerging Intelligent Computing Technology and Applications. ICIC 2009. Lecture Notes in Computer Science, vol 5754. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04070-2_16

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  • DOI: https://doi.org/10.1007/978-3-642-04070-2_16

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-04069-6

  • Online ISBN: 978-3-642-04070-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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