NANUQ:一种在聚结模型下从基因树推断物种网络的方法。

IF 1.5 4区 生物学 Q4 BIOCHEMICAL RESEARCH METHODS
Algorithms for Molecular Biology Pub Date : 2019-12-06 eCollection Date: 2019-01-01 DOI:10.1186/s13015-019-0159-2
Elizabeth S Allman, Hector Baños, John A Rhodes
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引用次数: 38

摘要

物种网络概括了物种树的概念,以允许杂交或其他横向基因转移。在网络多物种聚结模型下,由网络产生的单个基因树可以具有任何拓扑结构,但其产生频率取决于网络结构和数值参数。本文提出了一种基于基因树拓扑结构的1级物种网络统计推断算法,并为该算法提供了理论依据。该算法基于对基因树上显示的四重奏的分析,结合几种统计假设检验和组合思想,如适合网络的基于四重奏的类群间距离、用于圆形分裂系统的NeighborNet算法和用于构造分裂图的圆形网络算法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

NANUQ: a method for inferring species networks from gene trees under the coalescent model.

NANUQ: a method for inferring species networks from gene trees under the coalescent model.

NANUQ: a method for inferring species networks from gene trees under the coalescent model.

NANUQ: a method for inferring species networks from gene trees under the coalescent model.

Species networks generalize the notion of species trees to allow for hybridization or other lateral gene transfer. Under the network multispecies coalescent model, individual gene trees arising from a network can have any topology, but arise with frequencies dependent on the network structure and numerical parameters. We propose a new algorithm for statistical inference of a level-1 species network under this model, from data consisting of gene tree topologies, and provide the theoretical justification for it. The algorithm is based on an analysis of quartets displayed on gene trees, combining several statistical hypothesis tests with combinatorial ideas such as a quartet-based intertaxon distance appropriate to networks, the NeighborNet algorithm for circular split systems, and the Circular Network algorithm for constructing a splits graph.

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来源期刊
Algorithms for Molecular Biology
Algorithms for Molecular Biology 生物-生化研究方法
CiteScore
2.40
自引率
10.00%
发文量
16
审稿时长
>12 weeks
期刊介绍: Algorithms for Molecular Biology publishes articles on novel algorithms for biological sequence and structure analysis, phylogeny reconstruction, and combinatorial algorithms and machine learning. Areas of interest include but are not limited to: algorithms for RNA and protein structure analysis, gene prediction and genome analysis, comparative sequence analysis and alignment, phylogeny, gene expression, machine learning, and combinatorial algorithms. Where appropriate, manuscripts should describe applications to real-world data. However, pure algorithm papers are also welcome if future applications to biological data are to be expected, or if they address complexity or approximation issues of novel computational problems in molecular biology. Articles about novel software tools will be considered for publication if they contain some algorithmically interesting aspects.
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