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1. A Phylogenomic Supertree of Birds

   https://doi.org/10.3390/d11070109  

   Kimball, Rebecca T.; Oliveros, Carl H.; Wang, Ning; White, Noor D.; Barker, F. Keith; Field, Daniel J.; Ksepka, Daniel T.; Chesser, R. Terry; Moyle, Robert G.; Braun, Michael J.; et al (July 2019, Diversity)

   It has long been appreciated that analyses of genomic data (e.g., whole genome sequencing or sequence capture) have the potential to reveal the tree of life, but it remains challenging to move from sequence data to a clear understanding of evolutionary history, in part due to the computational challenges of phylogenetic estimation using genome-scale data. Supertree methods solve that challenge because they facilitate a divide-and-conquer approach for large-scale phylogeny inference by integrating smaller subtrees in a computationally efficient manner. Here, we combined information from sequence capture and whole-genome phylogenies using supertree methods. However, the available phylogenomic trees had limited overlap so we used taxon-rich (but not phylogenomic) megaphylogenies to weave them together. This allowed us to construct a phylogenomic supertree, with support values, that included 707 bird species (~7% of avian species diversity). We estimated branch lengths using mitochondrial sequence data and we used these branch lengths to estimate divergence times. Our time-calibrated supertree supports radiation of all three major avian clades (Palaeognathae, Galloanseres, and Neoaves) near the Cretaceous-Paleogene (K-Pg) boundary. The approach we used will permit the continued addition of taxa to this supertree as new phylogenomic data are published, and it could be applied to other taxa as well. 

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2. Maximum Likelihood Estimation of Species Trees from Gene Trees in the Presence of Ancestral Population Structure

   https://doi.org/10.1093/gbe/evaa022  

   Koch, H; DeGiorgio, M (February 2020, Genome biology and evolution)

   Though large multilocus genomic data sets have led to overall improvements in phylogenetic inference, they have posed the new challenge of addressing conflicting signals across the genome. In particular, ancestral population structure, which has been uncovered in a number of diverse species, can skew gene tree frequencies, thereby hindering the performance of species tree estimators. Here we develop a novel maximum likelihood method, termed TASTI (Taxa with Ancestral structure Species Tree Inference), that can infer phylogenies under such scenarios, and find that it has increasing accuracy with increasing numbers of input gene trees, contrasting with the relatively poor performances of methods not tailored for ancestral structure. Moreover, we propose a supertree approach that allows TASTI to scale computationally with increasing numbers of input taxa. We use genetic simulations to assess TASTI’s performance in the three- and four-taxon settings and demonstrate the application of TASTI on a six-species Afrotropical mosquito data set. Finally, we have implemented TASTI in an open-source software package for ease of use by the scientific community. 

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3. DEPP: Deep Learning Enables Extending Species Trees using Single Genes

   https://doi.org/10.1093/sysbio/syac031  

   Jiang, Yueyu; Balaban, Metin; Zhu, Qiyun; Mirarab, Siavash; Solis-Lemus, ed., Claudia (April 2022, Systematic Biology)

   Abstract Placing new sequences onto reference phylogenies is increasingly used for analyzing environmental samples, especially microbiomes. Existing placement methods assume that query sequences have evolved under specific models directly on the reference phylogeny. For example, they assume single-gene data (e.g., 16S rRNA amplicons) have evolved under the GTR model on a gene tree. Placement, however, often has a more ambitious goal: extending a (genome-wide) species tree given data from individual genes without knowing the evolutionary model. Addressing this challenging problem requires new directions. Here, we introduce Deep-learning Enabled Phylogenetic Placement (DEPP), an algorithm that learns to extend species trees using single genes without prespecified models. In simulations and on real data, we show that DEPP can match the accuracy of model-based methods without any prior knowledge of the model. We also show that DEPP can update the multilocus microbial tree-of-life with single genes with high accuracy. We further demonstrate that DEPP can combine 16S and metagenomic data onto a single tree, enabling community structure analyses that take advantage of both sources of data. [Deep learning; gene tree discordance; metagenomics; microbiome analyses; neural networks; phylogenetic placement.] 

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4. A Shared-Memory Algorithm for Updating Tree-Based Properties of Large Dynamic Networks

   https://doi.org/10.1109/TBDATA.2018.2870136  

   Srinivasan, Sriram; Pollard, Samuel; Das, Sajal K.; Norris, Boyana; Bhowmick, Sanjukta (September 2018, IEEE Transactions on Big Data)

   In this paper, we present a network-based template for analyzing large-scale dynamic data. Specifically, we present a novel shared-memory parallel algorithm for updating treebased structures, including connected components (CC) and the minimum spanning tree (MST) on dynamic networks. We propose a rooted tree-based data structure to store the edges that are most relevant to the analysis. Our algorithm is based on updating the information stored in this rooted tree.In this paper, we present a network-based template for analyzing large-scale dynamic data. Specifically, we present a novel shared-memory parallel algorithm for updating tree-based structures, including connected components (CC) and the minimum spanning tree (MST) on dynamic networks. We propose a rooted tree-based data structure to store the edges that are most relevant to the analysis. Our algorithm is based on updating the information stored in this rooted tree. 

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5. Statistical summaries of unlabelled evolutionary trees

   https://doi.org/10.1093/biomet/asad025  

   Samyak, Rajanala; Palacios, Julia A (April 2023, Biometrika)

   Summary Rooted and ranked phylogenetic trees are mathematical objects that are useful in modelling hierarchical data and evolutionary relationships with applications to many fields such as evolutionary biology and genetic epidemiology. Bayesian phylogenetic inference usually explores the posterior distribution of trees via Markov chain Monte Carlo methods. However, assessing uncertainty and summarizing distributions remains challenging for these types of structures. While labelled phylogenetic trees have been extensively studied, relatively less literature exists for unlabelled trees that are increasingly useful, for example when one seeks to summarize samples of trees obtained with different methods, or from different samples and environments, and wishes to assess the stability and generalizability of these summaries. In our paper, we exploit recently proposed distance metrics of unlabelled ranked binary trees and unlabelled ranked genealogies, or trees equipped with branch lengths, to define the Fréchet mean, variance and interquartile sets as summaries of these tree distributions. We provide an efficient combinatorial optimization algorithm for computing the Fréchet mean of a sample or of distributions on unlabelled ranked tree shapes and unlabelled ranked genealogies. We show the applicability of our summary statistics for studying popular tree distributions and for comparing the SARS-CoV-2 evolutionary trees across different locations during the COVID-19 epidemic in 2020. Our current implementations are publicly available at https://github.com/RSamyak/fmatrix. 

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