计算显示一组树的树-子网络中网状结构的数量边界

IF 1.4 4区 生物学 Q4 BIOCHEMICAL RESEARCH METHODS Journal of Computational Biology Pub Date : 2024-04-01 Epub Date: 2024-01-29 DOI:10.1089/cmb.2023.0309
Yufeng Wu, Louxin Zhang
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引用次数: 0

摘要

系统发育网络是一种进化模型,它使用有根有向非环图(而不是树)来模拟发生网状事件(如杂交物种或水平基因转移)的物种进化史。树-子网络是一种具有结构限制的系统发育网络。现有的树-子网络重建方法对于大数据来说速度较慢。在本研究中,我们提出了几种计算方法,用于从下往上限制树-子网络中的网状结构数量,该网络显示了一组给定的有根二叉系统发育树。此外,我们还提出了一些关于从上方约束网状结构数量的理论结果。通过模拟,我们证明了树-子网络网状结构数的新下限实际上可以在大型树数据中计算出来。这些界限可以为相对较大的数据提供网状结构的估计值。
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Computing the Bounds of the Number of Reticulations in a Tree-Child Network That Displays a Set of Trees.

Phylogenetic network is an evolutionary model that uses a rooted directed acyclic graph (instead of a tree) to model an evolutionary history of species in which reticulate events (e.g., hybrid speciation or horizontal gene transfer) occurred. Tree-child network is a kind of phylogenetic network with structural constraints. Existing approaches for tree-child network reconstruction can be slow for large data. In this study, we present several computational approaches for bounding from below the number of reticulations in a tree-child network that displays a given set of rooted binary phylogenetic trees. In addition, we also present some theoretical results on bounding from above the number of reticulations. Through simulation, we demonstrate that the new lower bounds on the reticulation number for tree-child networks can practically be computed for large tree data. The bounds can provide estimates of reticulation for relatively large data.

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来源期刊
Journal of Computational Biology
Journal of Computational Biology 生物-计算机:跨学科应用
CiteScore
3.60
自引率
5.90%
发文量
113
审稿时长
6-12 weeks
期刊介绍: Journal of Computational Biology is the leading peer-reviewed journal in computational biology and bioinformatics, publishing in-depth statistical, mathematical, and computational analysis of methods, as well as their practical impact. Available only online, this is an essential journal for scientists and students who want to keep abreast of developments in bioinformatics. Journal of Computational Biology coverage includes: -Genomics -Mathematical modeling and simulation -Distributed and parallel biological computing -Designing biological databases -Pattern matching and pattern detection -Linking disparate databases and data -New tools for computational biology -Relational and object-oriented database technology for bioinformatics -Biological expert system design and use -Reasoning by analogy, hypothesis formation, and testing by machine -Management of biological databases
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