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ShinyCore: An R/Shiny program for establishing core collection based on single nucleotide polymorphism data.

Steven Kim1, Dong Sub Kim2, Hana Moyle1,3

  • 1Department of Mathematics and Statistics, California State University, Monterey Bay, Seaside, USA.

Plant Methods
|October 11, 2023
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Summary
This summary is machine-generated.

ShinyCore is a new method for selecting core collections, enhancing genetic diversity and improving data management. This user-friendly tool offers faster processing for large datasets compared to existing algorithms.

Keywords:
Core collectionEvaluation metricsGermplasmR/ShinySingle nucleotide polymorphism

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Area of Science:

  • Plant breeding
  • Genetics
  • Bioinformatics

Background:

  • Managing extensive genetic resources is complex.
  • Core collections offer a representative subset for research and breeding.
  • Effective core collections require high genetic diversity and minimal redundancy.

Purpose of the Study:

  • Introduce ShinyCore, a novel two-phase method for core collection selection.
  • Improve upon existing algorithms for core collection management.
  • Enhance genetic diversity and data processing efficiency.

Main Methods:

  • ShinyCore employs a two-phase approach: coverage criterion followed by a rarity score.
  • The coverage criterion quickly establishes a defined level of genetic representation.
  • The rarity score prioritizes unique alleles to maximize diversity.

Main Results:

  • ShinyCore achieves user-defined coverage faster than GenoCore, especially for large datasets.
  • The rarity score effectively enhances genetic diversity and distance metrics.
  • ShinyCore demonstrates superior speed and diversity enhancement capabilities.

Conclusions:

  • ShinyCore provides an accessible and efficient alternative for core collection selection.
  • The method is easily implemented via RStudio and offers an interactive web applet.
  • ShinyCore benefits researchers and breeders, particularly those working with large genetic datasets.