A Fixed-Parameter Tractable Algorithm for Finding Agreement Cherry-Reduced Subnetworks in Level-1 Orchard Networks.

IF 1.4 4区 生物学 Q4 BIOCHEMICAL RESEARCH METHODS Journal of Computational Biology Pub Date : 2024-04-01 Epub Date: 2023-12-20 DOI:10.1089/cmb.2023.0317
Kaari Landry, Olivier Tremblay-Savard, Manuel Lafond
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Abstract

Phylogenetic networks are increasingly being considered better suited to represent the complexity of the evolutionary relationships between species. One class of phylogenetic networks that have received a lot of attention recently is the class of orchard networks, which is composed of networks that can be reduced to a single leaf using cherry reductions. Cherry reductions, also called cherry-picking operations, remove either a leaf of a simple cherry (sibling leaves sharing a parent) or a reticulate edge of a reticulate cherry (two leaves whose parents are connected by a reticulate edge). In this article, we present a fixed-parameter tractable algorithm to solve the problem of finding a maximum agreement cherry-reduced subnetwork (MACRS) between two rooted binary level-1 networks. This is the first exact algorithm proposed to solve the MACRS problem. As proven in an earlier work, there is a direct relationship between finding an MACRS and calculating a distance based on cherry operations. As a result, the proposed algorithm also provides a distance that can be used for the comparison of level-1 networks.

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在一级果园网络中寻找协议樱桃还原子网络的固定参数可实现算法
越来越多的人认为系统发生网络更适合代表物种间进化关系的复杂性。最近受到广泛关注的一类系统发育网络是果园网络,它是由可以通过樱桃还原法还原为单叶的网络组成的。樱桃还原也称为樱桃摘取操作,它可以移除简单樱桃(共享一个父本的同胞叶子)的一片叶子或网状樱桃(父本由网状边连接的两片叶子)的网状边。在这篇文章中,我们提出了一种固定参数的可操作性算法,用于解决在两个有根二元一级网络之间寻找最大一致樱桃缩小子网络(MACRS)的问题。这是第一个提出的解决 MACRS 问题的精确算法。正如早先的工作所证明的那样,找到 MACRS 和计算基于樱桃运算的距离之间存在直接关系。因此,所提出的算法也提供了一个可用于比较一级网络的距离。
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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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