Pub Date : 2024-04-01Epub Date: 2024-04-17DOI: 10.1089/cmb.2023.0338
Sanket Wagle, Alexey Markin, Paweł Górecki, Tavis K Anderson, Oliver Eulenstein
Phylogenetic inference and reconstruction methods generate hypotheses on evolutionary history. Competing inference methods are frequently used, and the evaluation of the generated hypotheses is achieved using tree comparison costs. The Robinson-Foulds (RF) distance is a widely used cost to compare the topology of two trees, but this cost is sensitive to tree error and can overestimate tree differences. To overcome this limitation, a refined version of the RF distance called the Cluster Affinity (CA) distance was introduced. However, CA distances are symmetric and cannot compare different types of trees. These asymmetric comparisons occur when gene trees are compared with species trees, when disparate datasets are integrated into a supertree, or when tree comparison measures are used to infer a phylogenetic network. In this study, we introduce a relaxation of the original Affinity distance to compare heterogeneous trees called the asymmetric CA cost. We also develop a biologically interpretable cost, the Cluster Support cost that normalizes by cluster size across gene trees. The characteristics of these costs are similar to the symmetric CA cost. We describe efficient algorithms, derive the exact diameters, and use these to standardize the cost to be applicable in practice. These costs provide objective, fine-scale, and biologically interpretable values that can assess differences and similarities between phylogenetic trees.
系统发育推断和重建方法会产生关于进化历史的假设。相互竞争的推断方法经常被使用,而对生成的假说的评估是通过树比较成本来实现的。罗宾逊-福尔斯(Robinson-Foulds,RF)距离是比较两棵树拓扑结构的一种广泛使用的成本,但这种成本对树的误差很敏感,可能会高估树的差异。为了克服这一局限性,人们引入了 RF 距离的改进版,称为簇亲和力(CA)距离。然而,CA 距离是对称的,不能比较不同类型的树。当将基因树与物种树进行比较时,当将不同的数据集整合到一棵超级树中时,或者当使用树比较度量来推断系统发育网络时,这些非对称比较就会发生。在本研究中,我们引入了一种原始亲和距离的松弛方法,用于比较异质树,称为非对称 CA 成本。我们还开发了一种可从生物学角度解释的成本--集群支持成本,该成本根据基因树的集群大小进行归一化处理。这些成本的特点与对称 CA 成本类似。我们描述了高效的算法,推导出精确的直径,并利用这些算法将成本标准化,使其在实践中适用。这些成本提供了客观、精细和生物可解释的值,可以评估系统发生树之间的差异和相似性。
{"title":"Asymmetric Cluster-Based Measures for Comparative Phylogenetics.","authors":"Sanket Wagle, Alexey Markin, Paweł Górecki, Tavis K Anderson, Oliver Eulenstein","doi":"10.1089/cmb.2023.0338","DOIUrl":"10.1089/cmb.2023.0338","url":null,"abstract":"<p><p><b>Phylogenetic inference and reconstruction methods generate hypotheses on evolutionary history. Competing inference methods are frequently used, and the evaluation of the generated hypotheses is achieved using tree comparison costs. The Robinson</b>-<b>Foulds (RF) distance is a widely used cost to compare the topology of two trees, but this cost is sensitive to tree error and can overestimate tree differences. To overcome this limitation, a refined version of the RF distance called the Cluster Affinity (CA) distance was introduced. However, CA distances are symmetric and cannot compare different types of trees. These asymmetric comparisons occur when gene trees are compared with species trees, when disparate datasets are integrated into a supertree, or when tree comparison measures are used to infer a phylogenetic network. In this study, we introduce a relaxation of the original Affinity distance to compare heterogeneous trees called the asymmetric CA cost. We also develop a biologically interpretable cost, the Cluster Support cost that normalizes by cluster size across gene trees. The characteristics of these costs are similar to the symmetric CA cost. We describe efficient algorithms, derive the exact diameters, and use these to standardize the cost to be applicable in practice. These costs provide objective, fine-scale, and biologically interpretable values that can assess differences and similarities between phylogenetic trees.</b></p>","PeriodicalId":15526,"journal":{"name":"Journal of Computational Biology","volume":null,"pages":null},"PeriodicalIF":1.7,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11057527/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140863219","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-04-01Epub Date: 2023-12-20DOI: 10.1089/cmb.2023.0317
Kaari Landry, Olivier Tremblay-Savard, Manuel Lafond
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.
{"title":"A Fixed-Parameter Tractable Algorithm for Finding Agreement Cherry-Reduced Subnetworks in Level-1 Orchard Networks.","authors":"Kaari Landry, Olivier Tremblay-Savard, Manuel Lafond","doi":"10.1089/cmb.2023.0317","DOIUrl":"10.1089/cmb.2023.0317","url":null,"abstract":"<p><p><b>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</b>.</p>","PeriodicalId":15526,"journal":{"name":"Journal of Computational Biology","volume":null,"pages":null},"PeriodicalIF":1.7,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138830002","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}