发现系统发育网络之间最大的共同收缩。

IF 1.7 4区 生物学 Q4 BIOCHEMICAL RESEARCH METHODS
Bertrand Marchand, Nadia Tahiri, Shohreh Golpaigani Fard, Olivier Tremblay-Savard, Manuel Lafond
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引用次数: 0

摘要

在本文中,我们为基于边缘收缩和扩展作为编辑操作的系统发育网络的比较奠定了基础,这是Robinson和Foulds最初提出的用于比较树的方法。我们证明,即使我们禁止产生循环的收缩,这些操作也将同一叶上所有系统发育网络的空间连接起来。这允许在这个空间上定义一个操作距离,作为将一个网络转换为另一个网络所需的最小数量的收缩和扩展。我们强调了这个距离和计算两个网络之间的最大公共收缩之间的区别。鉴于它能够勾勒出它们之间的共同结构,这可以提供有价值的生物学见解,我们研究后者的算法方面。我们首先证明了计算两个网络之间的最大公共收缩是np困难的,即使当最大程度,公共收缩的大小或叶的数量是有界的。我们还根据指数时间假设给出了问题的下界。尽管如此,我们确实提供了一个多项式时间算法弱磨损树,磨损树的推广。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Finding maximum common contractions between phylogenetic networks.

Finding maximum common contractions between phylogenetic networks.

Finding maximum common contractions between phylogenetic networks.

Finding maximum common contractions between phylogenetic networks.

In this paper, we lay the groundwork on the comparison of phylogenetic networks based on edge contractions and expansions as edit operations, as originally proposed by Robinson and Foulds to compare trees. We prove that these operations connect the space of all phylogenetic networks on the same set of leaves, even if we forbid contractions that create cycles. This allows to define an operational distance on this space, as the minimum number of contractions and expansions required to transform one network into another. We highlight the difference between this distance and the computation of the maximum common contraction between two networks. Given its ability to outline a common structure between them, which can provide valuable biological insights, we study the algorithmic aspects of the latter. We first prove that computing a maximum common contraction between two networks is NP-hard, even when the maximum degree, the size of the common contraction, or the number of leaves is bounded. We also provide lower bounds to the problem based on the Exponential-Time Hypothesis. Nonetheless, we do provide a polynomial-time algorithm for weakly galled trees, a generalization of galled trees.

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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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