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The Nucleosome Core Particle02:10

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Nucleosomes are the DNA-histone complex, where the DNA strand is wound around the histone core. The histone core is an octamer containing two copies of H2A, H2B, H3, and H4 histone proteins.
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Core Hunter 3: flexible core subset selection.

Herman De Beukelaer1, Guy F Davenport2, Veerle Fack3

  • 1Department of Applied Mathematics, Computer Science and Statistics, Ghent University, Krijgslaan 281 S9, Gent, 9000, Belgium. herman.debeukelaer@ugent.be.

BMC Bioinformatics
|June 2, 2018
PubMed
Summary
This summary is machine-generated.

Core Hunter 3 (CH3) enhances core subset selection for plant genetic resources, improving diversity and representativeness. This tool offers faster, more effective selection of diverse and representative core collections using advanced algorithms.

Keywords:
Core collectionsLocal search heuristicsMulti-objective

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Area of Science:

  • Plant genetics
  • Bioinformatics
  • Computational biology

Background:

  • Core collections are crucial for managing genetic resources, reducing collection sizes while preserving genetic diversity.
  • Existing methods for core collection generation often rely on distance metrics based on genetic or phenotypic data.
  • Core Hunter is a tool designed for selecting core subsets using various metrics and local search algorithms.

Purpose of the Study:

  • To introduce and evaluate version 3 of Core Hunter (CH3), focusing on new and improved methods for core subset selection.
  • To compare the performance of CH3 against previous versions and other established methods like GDOpt and SimEli.
  • To assess CH3's ability to optimize diversity, representativeness, and allelic richness, both individually and simultaneously.

Main Methods:

  • Incorporation of two new, improved distance summarization metrics in CH3 for quantifying diversity and representativeness.
  • Utilizing local search algorithms, including a simple stochastic hill-climber and a parallel tempering algorithm.
  • Evaluating CH3's performance in maximizing diversity, representativeness, and allelic richness, and comparing it with GDOpt and SimEli.

Main Results:

  • CH3 incorporates improved metrics that can be optimized with less complex algorithms compared to CH2.
  • CH3 is more effective than CH2 in maximizing diversity, ensuring high average and minimum distances, and is faster for large datasets.
  • CH3 demonstrates comparable or superior performance to GDOpt and SimEli in constructing diverse, representative, and genetically rich core collections, especially when optimizing multiple criteria simultaneously.

Conclusions:

  • Core Hunter 3 (CH3) offers significant improvements in core subset selection with new metrics and enhanced performance.
  • CH3 effectively optimizes diversity and representativeness, outperforming previous versions and other methods in simultaneous optimization scenarios.
  • The updated tool, CH3, is freely available and provides an efficient, versatile solution for genebank curators and plant breeders.