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Development of EST-SSR markers by data mining in three species of shrimp: Litopenaeus vannamei, Litopenaeus stylirostris, and Trachypenaeus birdy
Pérez, F.; Ortiz, J.; Zhinaula, M.; Gonzabay, C.; Calderón, J.; Volckaert, F.A.M.J. (2005). Development of EST-SSR markers by data mining in three species of shrimp: Litopenaeus vannamei, Litopenaeus stylirostris, and Trachypenaeus birdy. Mar. Biotechnol. 7(5): 554-569. dx.doi.org/10.1007/s10126-004-5099-1
In: Marine Biotechnology. Springer-Verlag: New York. ISSN 1436-2228; e-ISSN 1436-2236
Peer reviewed article  

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Keywords
    Biology > Genetics > Population genetics
    Data mining
    Data mining
    Data processing
    Mapping
    Litopenaeus Pérez Farfante, 1969 [WoRMS]
    Marine/Coastal
Author keywords
    data mining; EST-SSR; linkage mapping; Litopenaeus; population genetics; type I markers

Authors  Top 
  • Pérez, F.
  • Ortiz, J.
  • Zhinaula, M.
  • Gonzabay, C.
  • Calderón, J.
  • Volckaert, F.A.M.J., more

Abstract
    We report on the data mining of publicly available Litopenaeus vannamei expressed sequence tags (ESTs) to generate simple sequence repeat (SSRs) markers and on their transferability between related Penaeid shrimp species. Repeat motifs were found in 3.8% of the evaluated ESTs at a frequency of one repeat every 7.8 kb of sequence data. A total of 206 primer pairs were designed, and 112 loci were amplified with the highest success in L. vannamei. A high percentage (69%) of EST-SSRs were transferable within the genus Litopenaeus. More than half of the amplified products were polymorphic in a small testing panel of L. vannamei. Evaluation of those primers in a larger testing panel showed that 72% of the markers fit Hardy-Weinberg equilibrium, which shows their utility for population genetic analysis. Additionally, a set of 26 of the EST-SSRs were evaluated for Mendelian segregation. A high percentage of monomorphic markers (46%) proved to be polymorphic by singles-stranded conformational polymorphism analysis. Because of the high number of ESTs available in public databases, a data mining approach similar to the one outlined here might yield high numbers of SSR markers in many animal taxa.

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