Computing consensus networks for collections of 1-nested phylogenetic networks

K. Huber, V. Moulton, A. Spillner
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Abstract

. An important and well-studied problem in phylogenetics is to compute a consensus tree so as to summarize the common features within a collection of rooted phylogenetic trees, all whose leaf-sets are bijectively labeled by the same set X of species. More recently, however, it has become of interest to find a consensus for a collection of more general, rooted directed acyclic graphs all of whose sink-sets are bijec-tively labeled by X , so called rooted phylogenetic networks . These networks are used to analyze the evolution of species that cross with one another, such as plants and viruses. In this paper, we introduce an algorithm for computing a consensus for a collection of so-called 1-nested phylogenetic networks. Our approach builds on a previous result by Rosell´o et al. that describes an encoding for any 1-nested phylogenetic network in terms of a collection of ordered pairs of subsets of X . More specifically, we characterize those collections of ordered pairs that arise as the encoding of some 1-nested phylogenetic network, and then use this characterization to compute a consensus network for a collection of t ≥ 1 1-nested networks in O ( t | X | 2 + | X | 3 ) time. Applying our algorithm to a collection of phylogenetic trees yields the well-known majority rule consensus tree. Our approach leads to several new directions for future work, and we expect that it should provide a useful new tool to help understand complex evolutionary scenarios.
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计算1套系统发育网络集合的共识网络
. 系统发育学中一个重要且被广泛研究的问题是计算共识树,以总结一组有根系统发育树的共同特征,所有这些树的叶集都被相同的物种集X客观地标记。然而,最近,对于一组更一般的、有根的有向无环图的集合(所有这些图的汇集都被双客观地标记为X),也就是所谓的有根系统发育网络,找到一个共识已经变得很有趣。这些网络被用来分析相互交叉的物种的进化,比如植物和病毒。在本文中,我们引入了一种算法,用于计算所谓的1套系统发育网络集合的一致性。我们的方法建立在Rosell ' o等人先前的结果的基础上,该结果描述了根据X的子集的有序对的集合对任何1嵌套系统发育网络的编码。更具体地说,我们描述了那些作为某种1嵌套系统发育网络编码而出现的有序对集合,然后使用该表征在O (t | X | 2 + | X | 3)时间内计算t≥11嵌套网络集合的共识网络。将我们的算法应用于系统发育树的集合产生了众所周知的多数规则共识树。我们的方法为未来的工作指明了几个新的方向,我们期望它能提供一个有用的新工具来帮助理解复杂的进化场景。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Journal of Graph Algorithms and Applications
Journal of Graph Algorithms and Applications Mathematics-Geometry and Topology
CiteScore
1.20
自引率
0.00%
发文量
28
审稿时长
50 weeks
期刊介绍: The Journal of Graph Algorithms and Applications (JGAA) is a peer-reviewed scientific journal devoted to the publication of high-quality research papers on the analysis, design, implementation, and applications of graph algorithms. JGAA is supported by distinguished advisory and editorial boards, has high scientific standards and is distributed in electronic form. JGAA is a gold open access journal that charges no author fees. Topics of interest for JGAA include but are not limited to: Design and analysis of graph algorithms: exact and approximation graph algorithms; centralized and distributed graph algorithms; static and dynamic graph algorithms; internal- and external-memory graph algorithms; sequential and parallel graph algorithms; deterministic and randomized graph algorithms. Experiences with graph and network algorithms: animations; experimentations; implementations. Applications of graph and network algorithms: biomedical informatics; computational biology; computational geometry; computer graphics; computer-aided design; computer and interconnection networks; constraint systems; databases; economic networks; graph drawing; graph embedding and layout; knowledge representation; multimedia; social networks; software engineering; telecommunication networks; user interfaces and visualization; VLSI circuits.
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