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首页> 外文期刊>Journal of computational biology: A journal of computational molecular cell biology >Algorithms for MDC-Based Multi-Locus Phylogeny Inference: Beyond Rooted Binary Gene Trees on Single Alleles
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Algorithms for MDC-Based Multi-Locus Phylogeny Inference: Beyond Rooted Binary Gene Trees on Single Alleles

机译:基于MDC的多基因位系统发育推断的算法:单等位基因上超越根二叉树

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摘要

One of the criteria for inferring a species tree from a collection of gene trees, when gene tree incongruence is assumed to be due to incomplete lineage sorting (ILS), is Minimize Deep Coalescence (MDC). Exact algorithms for inferring the species tree from rooted, binary trees under MDC were recently introduced. Nevertheless, in phylogenetic analyses of biological data sets, estimated gene trees may differ from true gene trees, be incompletely resolved, and not necessarily rooted. In this article, we propose new MDC formulations for the cases where the gene trees are unrooted/binary, rootedon-binary, and unrooted/ non-binary. Further, we prove structural theorems that allow us to extend the algorithms for the rooted/binary gene tree case to these cases in a straightforward manner. In addition, we devise MDC-based algorithms for cases when multiple alleles per species may be sampled. We study the performance of these methods in coalescent-based computer simulations.
机译:当假定基因树不一致是由于不完整的谱系排序(ILS)时,从一组基因树中推断物种树的标准之一是最小化深度合并(MDC)。最近介绍了在MDC下从有根的二叉树推断物种树的精确算法。但是,在生物学数据集的系统发育分析中,估计的基因树可能与真实的基因树不同,无法完全解析,并且不一定扎根。在本文中,我们为基因树为无根/二进制,有根/非二进制和无根/非二进制的情况提出了新的MDC公式。此外,我们证明了结构定理,这些结构定理使我们能够以直接的方式将针对根/二进制基因树的算法扩展到这些情况。此外,我们设计了基于MDC的算法,可用于每个物种多个等位基因的采样。我们在基于合并的计算机仿真中研究了这些方法的性能。

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