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1. Weighting by Gene Tree Uncertainty Improves Accuracy of Quartet-based Species Trees

   https://doi.org/10.1093/molbev/msac215  

   Zhang, Chao ; Mirarab, Siavash ( December 2022 , Molecular Biology and Evolution)

   Takahashi, Aya (Ed.)

   Abstract Phylogenomic analyses routinely estimate species trees using methods that account for gene tree discordance. However, the most scalable species tree inference methods, which summarize independently inferred gene trees to obtain a species tree, are sensitive to hard-to-avoid errors introduced in the gene tree estimation step. This dilemma has created much debate on the merits of concatenation versus summary methods and practical obstacles to using summary methods more widely and to the exclusion of concatenation. The most successful attempt at making summary methods resilient to noisy gene trees has been contracting low support branches from the gene trees. Unfortunately, this approach requires arbitrary thresholds and poses new challenges. Here, we introduce threshold-free weighting schemes for the quartet-based species tree inference, the metric used in the popular method ASTRAL. By reducing the impact of quartets with low support or long terminal branches (or both), weighting provides stronger theoretical guarantees and better empirical performance than the unweighted ASTRAL. Our simulations show that weighting improves accuracy across many conditions and reduces the gap with concatenation in conditions with low gene tree discordance and high noise. On empirical data, weighting improves congruence with concatenation and increases support. Together, our results show that weighting, enabled by a new optimization algorithm we introduce, improves the utility of summary methods and can reduce the incongruence often observed across analytical pipelines. 

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2. QuCo: quartet-based co-estimation of species trees and gene trees

   https://doi.org/10.1093/bioinformatics/btac265  

   Rabiee, Maryam ; Mirarab, Siavash ( June 2022 , Bioinformatics)

   Phylogenomics faces a dilemma: on the one hand, most accurate species and gene tree estimation methods are those that co-estimate them; on the other hand, these co-estimation methods do not scale to moderately large numbers of species. The summary-based methods, which first infer gene trees independently and then combine them, are much more scalable but are prone to gene tree estimation error, which is inevitable when inferring trees from limited-length data. Gene tree estimation error is not just random noise and can create biases such as long-branch attraction.

   We introduce a scalable likelihood-based approach to co-estimation under the multi-species coalescent model. The method, called quartet co-estimation (QuCo), takes as input independently inferred distributions over gene trees and computes the most likely species tree topology and internal branch length for each quartet, marginalizing over gene tree topologies and ignoring branch lengths by making several simplifying assumptions. It then updates the gene tree posterior probabilities based on the species tree. The focus on gene tree topologies and the heuristic division to quartets enables fast likelihood calculations. We benchmark our method with extensive simulations for quartet trees in zones known to produce biased species trees and further with larger trees. We also run QuCo on a biological dataset of bees. Our results show better accuracy than the summary-based approach ASTRAL run on estimated gene trees.

   QuCo is available on https://github.com/maryamrabiee/quco.

   Supplementary data are available at Bioinformatics online.

    

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3. Species Tree Inference Methods Intended to Deal with Incomplete Lineage Sorting Are Robust to the Presence of Paralogs

   https://doi.org/10.1093/sysbio/syab056  

   Yan, Zhi ; Smith, Megan L ; Du, Peng ; Hahn, Matthew W ; Nakhleh, Luay ( July 2021 , Systematic Biology)

   Kubatko, Laura (Ed.)

   Abstract Many recent phylogenetic methods have focused on accurately inferring species trees when there is gene tree discordance due to incomplete lineage sorting (ILS). For almost all of these methods, and for phylogenetic methods in general, the data for each locus are assumed to consist of orthologous, single-copy sequences. Loci that are present in more than a single copy in any of the studied genomes are excluded from the data. These steps greatly reduce the number of loci available for analysis. The question we seek to answer in this study is: what happens if one runs such species tree inference methods on data where paralogy is present, in addition to or without ILS being present? Through simulation studies and analyses of two large biological data sets, we show that running such methods on data with paralogs can still provide accurate results. We use multiple different methods, some of which are based directly on the multispecies coalescent model, and some of which have been proven to be statistically consistent under it. We also treat the paralogous loci in multiple ways: from explicitly denoting them as paralogs, to randomly selecting one copy per species. In all cases, the inferred species trees are as accurate as equivalent analyses using single-copy orthologs. Our results have significant implications for the use of ILS-aware phylogenomic analyses, demonstrating that they do not have to be restricted to single-copy loci. This will greatly increase the amount of data that can be used for phylogenetic inference.[Gene duplication and loss; incomplete lineage sorting; multispecies coalescent; orthology; paralogy.] 

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4. ASTRAL-Pro 2: ultrafast species tree reconstruction from multi-copy gene family trees

   https://doi.org/10.1093/bioinformatics/btac620  

   Zhang, Chao ; Mirarab, Siavash ; Schwartz, ed., Russell ( September 2022 , Bioinformatics)

   Species tree inference from multi-copy gene trees has long been a challenge in phylogenomics. The recent method ASTRAL-Pro has made strides by enabling multi-copy gene family trees as input and has been quickly adopted. Yet, its scalability, especially memory usage, needs to improve to accommodate the ever-growing dataset size.

   We present ASTRAL-Pro 2, an ultrafast and memory efficient version of ASTRAL-Pro that adopts a placement-based optimization algorithm for significantly better scalability without sacrificing accuracy.

   The source code and binary files are publicly available at https://github.com/chaoszhang/ASTER; data are available at https://github.com/chaoszhang/A-Pro2_data.

   Supplementary data are available at Bioinformatics online.

    

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5. Using all Gene Families Vastly Expands Data Available for Phylogenomic Inference

   https://doi.org/10.1093/molbev/msac112  

   Smith, Megan L. ; Vanderpool, Dan ; Hahn, Matthew W. ( June 2022 , Molecular Biology and Evolution)

   Townsend, Jeffrey (Ed.)

   Abstract Traditionally, single-copy orthologs have been the gold standard in phylogenomics. Most phylogenomic studies identify putative single-copy orthologs using clustering approaches and retain families with a single sequence per species. This limits the amount of data available by excluding larger families. Recent advances have suggested several ways to include data from larger families. For instance, tree-based decomposition methods facilitate the extraction of orthologs from large families. Additionally, several methods for species tree inference are robust to the inclusion of paralogs and could use all of the data from larger families. Here, we explore the effects of using all families for phylogenetic inference by examining relationships among 26 primate species in detail and by analyzing five additional data sets. We compare single-copy families, orthologs extracted using tree-based decomposition approaches, and all families with all data. We explore several species tree inference methods, finding that identical trees are returned across nearly all subsets of the data and methods for primates. The relationships among Platyrrhini remain contentious; however, the species tree inference method matters more than the subset of data used. Using data from larger gene families drastically increases the number of genes available and leads to consistent estimates of branch lengths, nodal certainty and concordance, and inferences of introgression in primates. For the other data sets, topological inferences are consistent whether single-copy families or orthologs extracted using decomposition approaches are analyzed. Using larger gene families is a promising approach to include more data in phylogenomics without sacrificing accuracy, at least when high-quality genomes are available. 

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