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2D random walk representation of Begonia × tuberhybrida multiallelic loci used for germplasm identification


In this study, we wanted to inspect whether the evolutionary driven differences in primary sequences could correlate, and thus predict the genetic diversity of related marker loci, which is an important criterion to assess the quality of any DNA marker. We adopted new approach of quantitative symbolic DNA sequence analysis called DNA random walk representation to study multiallelic marker loci from Begonia × tuberhybrida Voss. We described significant correlation of random walk-derived digital invariants to genetic diversity of the marker loci. Specifically, on the 3D-contour plot of multivariate principal component analysis (PCA), we revealed statistical correlation between the first two PCA factors and the number of alleles per marker locus. Based on that correlation, we suggest that DNA walk representation may predict allele-rich loci solely from their primary sequences, which improves current design of new DNA germplasm identificators.

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pricipal component analysis


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This study was supported by the program of Targeted Research DP3-PP2 of the National Research Program, project No. 1QS500510566 of the Academy of Sciences of the Czech Republic, and by the project AV CR No. VZ-AVOZ 50510513. We thank Jan Wiesner for technical assistance.

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Correspondence to I. Wiesner.

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Wiesner, I., Wiesnerová, D. 2D random walk representation of Begonia × tuberhybrida multiallelic loci used for germplasm identification. Biol Plant 54, 353–356 (2010).

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Additional key words

  • bioinformatics
  • information entropy
  • Markov chain
  • primary sequence analysis
  • principal component analysis