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Phylogenetic trees represent the evolutionary relationships and ancestry of various species or groups of organisms. Comparing these trees by measuring the distance between them is essential for applications such as tree clustering and the Tree of Life project. Many distance metrics for phylogenetic trees focus on trees defined on the same set of taxa. However, some problems require calculating distances between trees with different but overlapping sets of taxa. This study reviews state‐of‐the‐art distance measures for such trees, covering six major approaches, including the constraint‐based Robinson–Foulds (RF) distance RF(−), the completion‐based RF(+), the generalized RF (GRF), the dissimilarity measure, the vectorial tree distance, and the geodesic distance in the extended Billera‐Holmes‐Vogtmann tree space. Among these, three RF‐based methods, RF(−), RF(+), and GRF, were examined in detail on generated clusters of phylogenetic trees defined on different but mutually overlapping sets of taxa. Additionally, we reviewed nine related techniques, including leaf imputation methods, the tree edit distance, and visual comparison. A comparison of the related distance measures, highlighting their principal advantages and shortcomings, is provided. This review offers valuable insights into their applicability and performance, guiding the appropriate use of these metrics based on tree type (rooted or unrooted) and information type (topological or branch lengths). [ABSTRACT FROM AUTHOR]

Let G be a k -uniform hypergraph with vertex set V (G) and edge set E (G). A connected and acyclic hypergraph is called a supertree. For 0 ≤ α < 1 , the α -spectral radius of G is the largest H -eigenvalue of α D (G) + (1 − α) A (G) , where D (G) and A (G) are the diagonal tensor of the degrees and the adjacency tensor of G , respectively. In this paper, we determine the unique supertrees with maximum α -spectral radius among all k -uniform supertrees with m edges and independence number β for ⌈ m (k − 1) + 1 k ⌉ ≤ β ≤ m , among all k -uniform supertrees with given degree sequences, and among all k -uniform supertrees with m edges and matching number μ for 1 ≤ μ ≤ ⌊ m (k − 1) + 1 k ⌋ , respectively. [ABSTRACT FROM AUTHOR]

Swine acute diarrhoea syndrome coronavirus (SADS-CoV; Coronaviridae, Rhinacovirus) was detected in 2017 in Guangdong Province (China), where it caused high mortality rates in piglets. According to previous studies, SADS-CoV evolved from horseshoe bat reservoirs. Here, we report the first five Rhinacovirus genomes sequenced in horseshoe bats from Vietnam and their comparisons with data published in China. Our phylogenetic analyses provided evidence for four groups: rhinacoviruses from Rhinolphus pusillus bats, including one from Vietnam; bat rhinacoviruses from Hainan; bat rhinacoviruses from Yunnan showing a divergent synonymous nucleotide composition; and SADS-CoV and related bat viruses, including four rhinacoviruses from Vietnam sampled in Rhinolophus affinis and Rhinolophus thomasi. Our phylogeographic analyses showed that bat rhinacoviruses from Dien Bien (Vietnam) share more affinities with those from Yunnan (China) and that the ancestor of SADS-CoVs arose in Rhinolophus affinis circulating in Guangdong. We detected sequencing errors and artificial chimeric genomes in published data. The two SADS-CoV genomes previously identified as recombinant could also be problematic. The reliable data currently available, therefore, suggests that all SADS-CoV strains originate from a single bat source and that the virus has been spreading in pig farms in several provinces of China for at least seven years since the first outbreak in August 2016. [ABSTRACT FROM AUTHOR]

The algorithms for phylogenetic reconstruction are central to computational molecular evolution. The relentless pace of data acquisition has exposed their poor scalability and the conclusion that the conventional application of these methods is impractical and not justifiable from an energy usage perspective. Furthermore, the drive to improve the statistical performance of phylogenetic methods produces increasingly parameter-rich models of sequence evolution, which worsens the computational performance. Established theoretical and algorithmic results identify supertree methods as critical to divide-and-conquer strategies for improving scalability of phylogenetic reconstruction. Of particular importance is the ability to explicitly accommodate rooted topologies. These can arise from the more biologically plausible non-stationary models of sequence evolution. We make a contribution to addressing this challenge with Spectral Cluster Supertree, a novel supertree method for merging a set of overlapping rooted phylogenetic trees. It offers significant improvements over Min-Cut supertree and previous state-of-the-art methods in terms of both time complexity and overall topological accuracy, particularly for problems of large size. We perform comparisons against Min-Cut supertree and Bad Clade Deletion. Leveraging two tree topology distance metrics, we demonstrate that while Bad Clade Deletion generates more correct clades in its resulting supertree, Spectral Cluster Supertree’s generated tree is generally more topologically close to the true model tree. Over large datasets containing 10,000 taxa and ∼500 source trees, where Bad Clade Deletion usually takes ∼2 h to run, our method generates a supertree in on average 20 s. Spectral Cluster Supertree is released under an open source license and is available on the python package index as sc-supertree.

Chronograms—phylogenies with branch lengths proportional to time—represent key data on timing of evolutionary events, allowing us to study natural processes in many areas of biological research. Chronograms also provide valuable information that can be used for education, science communication, and conservation policy decisions. Yet, achieving a high-quality reconstruction of a chronogram is a difficult and resource-consuming task. Here we present DateLife, a phylogenetic software implemented as an R package and an R Shiny web application available at www.datelife.org , that provides services for efficient and easy discovery, summary, reuse, and reanalysis of node age data mined from a curated database of expert, peer-reviewed, and openly available chronograms. The main DateLife workflow starts with one or more scientific taxon names provided by a user. Names are processed and standardized to a unified taxonomy, allowing DateLife to run a name match across its local chronogram database that is curated from Open Tree of Life's phylogenetic repository, and extract all chronograms that contain at least two queried taxon names, along with their metadata. Finally, node ages from matching chronograms are mapped using the congruification algorithm to corresponding nodes on a tree topology, either extracted from Open Tree of Life's synthetic phylogeny or one provided by the user. Congruified node ages are used as secondary calibrations to date the chosen topology, with or without initial branch lengths, using different phylogenetic dating methods such as BLADJ, treePL, PATHd8, and MrBayes. We performed a cross-validation test to compare node ages resulting from a DateLife analysis (i.e, phylogenetic dating using secondary calibrations) to those from the original chronograms (i.e, obtained with primary calibrations), and found that DateLife's node age estimates are consistent with the age estimates from the original chronograms, with the largest variation in ages occurring around topologically deeper nodes. Because the results from any software for scientific analysis can only be as good as the data used as input, we highlight the importance of considering the results of a DateLife analysis in the context of the input chronograms. DateLife can help to increase awareness of the existing disparities among alternative hypotheses of dates for the same diversification events, and to support exploration of the effect of alternative chronogram hypotheses on downstream analyses, providing a framework for a more informed interpretation of evolutionary results. [ABSTRACT FROM AUTHOR]

Abstract Phylogenetic trees represent the evolutionary relationships and ancestry of various species or groups of organisms. Comparing these trees by measuring the distance between them is essential for applications such as tree clustering and the Tree of Life project. Many distance metrics for phylogenetic trees focus on trees defined on the same set of taxa. However, some problems require calculating distances between trees with different but overlapping sets of taxa. This study reviews state‐of‐the‐art distance measures for such trees, covering six major approaches, including the constraint‐based Robinson–Foulds (RF) distance RF(−), the completion‐based RF(+), the generalized RF (GRF), the dissimilarity measure, the vectorial tree distance, and the geodesic distance in the extended Billera‐Holmes‐Vogtmann tree space. Among these, three RF‐based methods, RF(−), RF(+), and GRF, were examined in detail on generated clusters of phylogenetic trees defined on different but mutually overlapping sets of taxa. Additionally, we reviewed nine related techniques, including leaf imputation methods, the tree edit distance, and visual comparison. A comparison of the related distance measures, highlighting their principal advantages and shortcomings, is provided. This review offers valuable insights into their applicability and performance, guiding the appropriate use of these metrics based on tree type (rooted or unrooted) and information type (topological or branch lengths).

The Open Tree of Life (OToL) project produces a supertree that summarizes phylogenetic knowledge from tree estimates published in the primary literature. The supertree construction algorithm iteratively calls Aho’s Build algorithm thousands of times in order to assess the compatability of different phylogenetic groupings. We describe an incrementalized version of the Build algorithm that is able to share work between successive calls to Build. We provide details that allow a programmer to implement the incremental algorithm BuildInc, including pseudo-code and a description of data structures. We assess the effect of BuildInc on our supertree algorithm by analyzing simulated data and by analyzing a supertree problem taken from the OpenTree 13.4 synthesis tree. We find that BuildInc provides up to 550-fold speedup for our supertree algorithm.

Chronograms-phylogenies with branch lengths proportional to time-represent key data on timing of evolutionary events, allowing us to study natural processes in many areas of biological research. Chronograms also provide valuable information that can be used for education, science communication, and conservation policy decisions. Yet, achieving a high-quality reconstruction of a chronogram is a difficult and resource-consuming task. Here we present DateLife, a phylogenetic software implemented as an R package and an R Shiny web application available at www.datelife.org, that provides services for efficient and easy discovery, summary, reuse, and reanalysis of node age data mined from a curated database of expert, peer-reviewed, and openly available chronograms. The main DateLife workflow starts with one or more scientific taxon names provided by a user. Names are processed and standardized to a unified taxonomy, allowing DateLife to run a name match across its local chronogram database that is curated from Open Tree of Life's phylogenetic repository, and extract all chronograms that contain at least two queried taxon names, along with their metadata. Finally, node ages from matching chronograms are mapped using the congruification algorithm to corresponding nodes on a tree topology, either extracted from Open Tree of Life's synthetic phylogeny or one provided by the user. Congruified node ages are used as secondary calibrations to date the chosen topology, with or without initial branch lengths, using different phylogenetic dating methods such as BLADJ, treePL, PATHd8, and MrBayes. We performed a cross-validation test to compare node ages resulting from a DateLife analysis (i.e, phylogenetic dating using secondary calibrations) to those from the original chronograms (i.e, obtained with primary calibrations), and found that DateLife's node age estimates are consistent with the age estimates from the original chronograms, with the largest variation in ages occurring around topologically deeper nodes. Because the results from any software for scientific analysis can only be as good as the data used as input, we highlight the importance of considering the results of a DateLife analysis in the context of the input chronograms. DateLife can help to increase awareness of the existing disparities among alternative hypotheses of dates for the same diversification events, and to support exploration of the effect of alternative chronogram hypotheses on downstream analyses, providing a framework for a more informed interpretation of evolutionary results., (© The Author(s) 2024. Published by Oxford University Press on behalf of the Society of Systematic Biologists.)

The Open Tree of Life (OToL) project produces a supertree that summarizes phylogenetic knowledge from tree estimates published in the primary literature. The supertree construction algorithm iteratively calls Aho's Build algorithm thousands of times in order to assess the compatability of different phylogenetic groupings. We describe an incrementalized version of the Build algorithm that is able to share work between successive calls to Build. We provide details that allow a programmer to implement the incremental algorithm BuildInc, including pseudo-code and a description of data structures. We assess the effect of BuildInc on our supertree algorithm by analyzing simulated data and by analyzing a supertree problem taken from the OpenTree 13.4 synthesis tree. We find that BuildInc provides up to 550-fold speedup for our supertree algorithm., Competing Interests: The authors declare there are no competing interests., (2024 Redelings and Holder.)