The tree-child network inference problem for line trees and the shortest common supersequence problem for permutation strings

IF 1.1 3区 计算机科学 Q1 BUSINESS, FINANCE
Laurent Bulteau , Louxin Zhang
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引用次数: 0

Abstract

One strategy for inference of phylogenetic networks is to solve the phylogenetic network problem, which involves inferring phylogenetic trees first and subsequently computing the smallest phylogenetic network that displays all the trees. This approach capitalizes on exceptional tools available for inferring phylogenetic trees from biomolecular sequences. Since the vast space of phylogenetic networks poses difficulties in obtaining comprehensive sampling, the researchers switch their attention to inferring tree-child networks from multiple phylogenetic trees, where in a tree-child network each non-leaf node must have at least one child that is an indegree-one node. Three results are obtained in this work: (1) The shortest common supersequence problem remains NP-hard even for permutation strings. (2) Derived from the first result, the tree-child network inference problem is also established as NP-hard even for line trees (also known as caterpillar trees). (3) The parsimonious tree-child networks that display all the line trees are the same as those displaying all the binary trees and their hybridization number is Θ(n3) for n taxa.

线状树的树-子网络推理问题和排列字符串的最短公共超序列问题
推断系统发育网络的一种策略是解决系统发育网络问题,即首先推断系统发育树,然后计算显示所有系统发育树的最小系统发育网络。这种方法充分利用了从生物分子序列推断系统发生树的特殊工具。由于庞大的系统发育网络难以获得全面的采样,研究人员将注意力转移到了从多个系统发育树推断树-子网络上,在树-子网络中,每个非叶节点必须至少有一个子节点是独立节点。这项工作获得了三个结果:(1) 即使对于置换字符串,最短公共超序列问题仍然是 NP 难的。(2) 根据第一个结果,树-子网络推理问题也被确定为 NP-难,即使对于行树(也称为毛毛虫树)也是如此。(3) 显示所有线状树的拟树子网络与显示所有二叉树的拟树子网络相同,对于 n 个类群,其杂交数为 Θ(n3)。
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来源期刊
Journal of Computer and System Sciences
Journal of Computer and System Sciences 工程技术-计算机:理论方法
CiteScore
3.70
自引率
0.00%
发文量
58
审稿时长
68 days
期刊介绍: The Journal of Computer and System Sciences publishes original research papers in computer science and related subjects in system science, with attention to the relevant mathematical theory. Applications-oriented papers may also be accepted and they are expected to contain deep analytic evaluation of the proposed solutions. Research areas include traditional subjects such as: • Theory of algorithms and computability • Formal languages • Automata theory Contemporary subjects such as: • Complexity theory • Algorithmic Complexity • Parallel & distributed computing • Computer networks • Neural networks • Computational learning theory • Database theory & practice • Computer modeling of complex systems • Security and Privacy.
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