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StructuRly: A novel shiny app to produce comprehensive, detailed and interactive plots for population genetic analysis

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  • Nicola G Criscuolo
  • Claudia Angelini

Abstract

Population genetics focuses on the analysis of genetic differences within and between-group of individuals and the inference of the populations’ structure. These analyses are usually carried out using Bayesian clustering or maximum likelihood estimation algorithms that assign individuals to a given population depending on specific genetic patterns. Although several tools were developed to perform population genetics analysis, their standard graphical outputs may not be sufficiently informative for users lacking interactivity and complete information. StructuRly aims to resolve this problem by offering a complete environment for population analysis. In particular, StructuRly combines the statistical power of the R language with the friendly interfaces implemented using the shiny libraries to provide a novel tool for performing population clustering, evaluating several genetic indexes, and comparing results. Moreover, graphical representations are interactive and can be easily personalized. StructuRly is available either as R package on GitHub, with detailed information for its installation and use and as shinyapps.io servers for those users who are not familiar with R and the RStudio IDE. The application has been tested on Linux, macOS and Windows operative systems and can be launched as a shiny app in every web browser.

Suggested Citation

  • Nicola G Criscuolo & Claudia Angelini, 2020. "StructuRly: A novel shiny app to produce comprehensive, detailed and interactive plots for population genetic analysis," PLOS ONE, Public Library of Science, vol. 15(2), pages 1-12, February.
    • Handle: RePEc:plo:pone00:0229330
    DOI: 10.1371/journal.pone.0229330
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    References listed on IDEAS

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    1. Lawrence Hubert & Phipps Arabie, 1985. "Comparing partitions," Journal of Classification, Springer;The Classification Society, vol. 2(1), pages 193-218, December.
    2. Laura D Hughes & Scott A Lewis & Michael E Hughes, 2017. "ExpressionDB: An open source platform for distributing genome-scale datasets," PLOS ONE, Public Library of Science, vol. 12(11), pages 1-13, November.
    3. Bohdan B Khomtchouk & James R Hennessy & Claes Wahlestedt, 2017. "shinyheatmap: Ultra fast low memory heatmap web interface for big data genomics," PLOS ONE, Public Library of Science, vol. 12(5), pages 1-9, May.
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    1. Mohammed Mohi-Ud-Din & Md. Alamgir Hossain & Md. Motiar Rohman & Md. Nesar Uddin & Md. Sabibul Haque & Eldessoky S. Dessoky & Mohammed Alqurashi & Salman Aloufi, 2022. "Assessment of Genetic Diversity of Bread Wheat Genotypes for Drought Tolerance Using Canopy Reflectance-Based Phenotyping and SSR Marker-Based Genotyping," Sustainability, MDPI, vol. 14(16), pages 1-19, August.

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