TINNiK: inference of the tree of blobs of a species network under the coalescent model.

IF 1.5 4区 生物学 Q4 BIOCHEMICAL RESEARCH METHODS
Elizabeth S Allman, Hector Baños, Jonathan D Mitchell, John A Rhodes
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引用次数: 0

Abstract

The tree of blobs of a species network shows only the tree-like aspects of relationships of taxa on a network, omitting information on network substructures where hybridization or other types of lateral transfer of genetic information occur. By isolating such regions of a network, inference of the tree of blobs can serve as a starting point for a more detailed investigation, or indicate the limit of what may be inferrable without additional assumptions. Building on our theoretical work on the identifiability of the tree of blobs from gene quartet distributions under the Network Multispecies Coalescent model, we develop an algorithm, TINNiK, for statistically consistent tree of blobs inference. We provide examples of its application to both simulated and empirical datasets, utilizing an implementation in the MSCquartets 2.0 R package.

TINNiK:聚合模型下的物种网络 Blob 树推断。
物种网络的 "花叶树 "只显示了网络中类群关系的树状方面,而忽略了发生杂交或其他类型遗传信息横向转移的网络子结构的信息。通过分离网络中的这些区域,推断 "斑点树 "可以作为更详细研究的起点,或表明在没有额外假设的情况下可以推断的极限。基于我们在网络多物种凝聚模型下从基因四元组分布中得出的花叶树可识别性的理论研究,我们开发了一种算法 TINNiK,用于统计一致的花叶树推断。我们利用 MSCquartets 2.0 R 软件包中的实现,提供了该算法在模拟和经验数据集上的应用实例。
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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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