Barba-Montoya Jose, Craig Jack M, Kumar Sudhir
Richard Gilder Graduate School, American Museum of Natural History, New York, NY, United States.
Institute for Genomics and Evolutionary Medicine, Temple University, Philadelphia, PA, United States.
Front Bioinform. 2025 Apr 30;5:1571568. doi: 10.3389/fbinf.2025.1571568. eCollection 2025.
Reconstructing the global Tree of Life necessitates computational approaches to integrate numerous molecular phylogenies with limited species overlap into a comprehensive supertree. Our survey of published literature shows that individual phylogenies are frequently restricted to specific taxonomic groups due to investigators' expertise and molecular evolutionary considerations, resulting in any given species present in a minuscule fraction of phylogenies. We present a novel approach, called the chronological supertree algorithm (Chrono-STA), that can build a supertree of species from such data by using node ages in published molecular phylogenies scaled to time. Chrono-STA builds a supertree by integrating chronological data from molecular timetrees. It fundamentally differs from existing approaches that generate consensus phylogenies from gene trees with missing taxa, as Chrono-STA does not impute nodal distances, use a guide tree as a backbone, or reduce phylogenies to quartets. Analyses of simulated and empirical datasets show that Chrono-STA can combine taxonomically restricted timetrees with extremely limited species overlap. For such data, approaches that impute missing distances or assemble phylogenetic quartets did not perform well. We conclude that integrating phylogenies via temporal dimension enhances the accuracy of reconstructed supertrees that are also scaled to time.
重建全球生命树需要采用计算方法,将众多物种重叠有限的分子系统发育树整合为一个全面的超级树。我们对已发表文献的调查表明,由于研究人员的专业知识和分子进化方面的考虑,单个系统发育树通常局限于特定的分类群,导致任何给定物种在系统发育树中所占比例极小。我们提出了一种名为时间顺序超级树算法(Chrono-STA)的新方法,该方法可以通过使用已发表分子系统发育树中按时间缩放的节点年龄,从这些数据构建物种超级树。Chrono-STA通过整合分子时间树的时间顺序数据来构建超级树。它与现有的从缺少分类单元的基因树生成共识系统发育树的方法有根本区别,因为Chrono-STA不估算节点距离、不使用引导树作为主干,也不将系统发育树简化为四重奏。对模拟数据集和实证数据集的分析表明,Chrono-STA可以将分类受限的时间树与物种重叠极少的数据结合起来。对于此类数据,估算缺失距离或组装系统发育四重奏的方法表现不佳。我们得出结论,通过时间维度整合系统发育树可以提高同样按时间缩放的重建超级树的准确性。