• DocumentCode
    584640
  • Title

    Estimation of the Influence for Different Population Sizes in MA-Based Natural SNP-RFLP Primer Design

  • Author

    Yu-Huei Cheng ; Li-Yeh Chuang ; Cheng-Hong Yang

  • Author_Institution
    Dept. of Digital Content Design & Manage., Toko Univ., Chiayi, Taiwan
  • fYear
    2012
  • fDate
    16-18 Nov. 2012
  • Firstpage
    68
  • Lastpage
    73
  • Abstract
    Single nucleotide polymorphisms (SNPs) are the most common genetic variations that can be genotyped effectively by polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP). Although PCR-RFLP has become the popular technique, search for available restriction enzymes and design of feasible primers for SNP genotyping is still a challenging task. An available restriction enzyme at least must be provided to discriminate a target SNP, and simultaneously a feasible primer pair observes numerous constraints must be given before performing SNP-based PCR-RFLP experiments. Here, we called it "natural SNP-RFLP primer design". In the past, a memetic algorithm (MA) was introduced to design natural SNP-RFLP primers, however, the influence of the used population size was not considered. Here, we use different population size to estimate the result of MA-based natural SNP-RFLP primer design. From the test result, we suggested the population size used between 200 and 300 is preferred to provide the natural SNP-RFLP primers.
  • Keywords
    biology computing; enzymes; genetics; genomics; polymorphism; MA-based natural SNP-RFLP primer design; SNP genotyping; SNP-based PCR-RFLP experiments; genetic variations; memetic algorithm; polymerase chain reaction-restriction fragment length polymorphism; population size influence estimation; restriction enzymes; single nucleotide polymorphisms; Biochemistry; Bismuth; Clamps; DNA; Sociology; Statistics; Single nucleotide polymorphisms; memetic algorithm; polymerase chain reaction; primer design; restriction fragment length polymorphism;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Technologies and Applications of Artificial Intelligence (TAAI), 2012 Conference on
  • Conference_Location
    Tainan
  • Print_ISBN
    978-1-4673-4976-5
  • Type

    conf

  • DOI
    10.1109/TAAI.2012.59
  • Filename
    6395008