Thachuk Chris, Crossa José, Franco Jorge, Dreisigacker Susanne, Warburton Marilyn, Davenport Guy F
Department of Computer Science, University of British Columbia, 2366 Main Mall, Vancouver, BC V6T1Z4, Canada.
BMC Bioinformatics. 2009 Aug 6;10:243. doi: 10.1186/1471-2105-10-243.
Existing algorithms and methods for forming diverse core subsets currently address either allele representativeness (breeder's preference) or allele richness (taxonomist's preference). The main objective of this paper is to propose a powerful yet flexible algorithm capable of selecting core subsets that have high average genetic distance between accessions, or rich genetic diversity overall, or a combination of both.
We present Core Hunter, an advanced stochastic local search algorithm for selecting core subsets. Core Hunter is able to find core subsets having more genetic diversity and better average genetic distance than the current state-of-the-art algorithms for all genetic distance and diversity measures we evaluated. Furthermore, Core Hunter can attempt to optimize any number of genetic measures simultaneously, based on the preference of the user. Notably, Core Hunter is able to select significantly smaller core subsets, which retain all unique alleles from a reference collection, than state-of-the-art algorithms.
Core Hunter is a highly effective and flexible tool for sampling genetic resources and establishing core subsets. Our implementation, documentation, and source code for Core Hunter is available at http://corehunter.org.
目前用于构建多样核心子集的现有算法和方法，要么侧重于等位基因代表性（育种者的偏好），要么侧重于等位基因丰富度（分类学家的偏好）。本文的主要目标是提出一种强大且灵活的算法，该算法能够选择在种质间具有较高平均遗传距离、整体遗传多样性丰富或兼具两者的核心子集。
我们提出了Core Hunter，一种用于选择核心子集的先进随机局部搜索算法。对于我们评估的所有遗传距离和多样性度量，Core Hunter能够找到比当前最先进算法具有更多遗传多样性和更好平均遗传距离的核心子集。此外，基于用户的偏好，Core Hunter能够同时尝试优化任意数量的遗传度量。值得注意的是，与最先进算法相比，Core Hunter能够选择显著更小的核心子集，这些子集保留了参考集合中的所有独特等位基因。
Core Hunter是一种用于遗传资源采样和构建核心子集的高效且灵活的工具。Core Hunter的实现、文档和源代码可在http://corehunter.org获取。
