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1. “Correcting” Gene Trees to be More Like Species Trees Frequently Increases Topological Error

   https://doi.org/10.1093/gbe/evad094  

   Yan, Zhi; Ogilvie, Huw A; Nakhleh, Luay (June 2023, Genome Biology and Evolution)

   Holland, Barbara (Ed.)

   Abstract The evolutionary histories of individual loci in a genome can be estimated independently, but this approach is error-prone due to the limited amount of sequence data available for each gene, which has led to the development of a diverse array of gene tree error correction methods which reduce the distance to the species tree. We investigate the performance of two representatives of these methods: TRACTION and TreeFix. We found that gene tree error correction frequently increases the level of error in gene tree topologies by “correcting” them to be closer to the species tree, even when the true gene and species trees are discordant. We confirm that full Bayesian inference of the gene trees under the multispecies coalescent model is more accurate than independent inference. Future gene tree correction approaches and methods should incorporate an adequately realistic model of evolution instead of relying on oversimplified heuristics. 

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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)

   Abstract MotivationPhylogenomics 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. ResultsWe 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. Availability and implementationQuCo is available on https://github.com/maryamrabiee/quco. Supplementary informationSupplementary data are available at Bioinformatics online. 

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3. 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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4. Practical Speedup of Bayesian Inference of Species Phylogenies by Restricting the Space of Gene Trees

   https://doi.org/10.1093/molbev/msaa045  

   Wang, Yaxuan; Ogilvie, Huw A; Nakhleh, Luay; Harris, Kelley (February 2020, Molecular Biology and Evolution)

   Abstract Species tree inference from multilocus data has emerged as a powerful paradigm in the postgenomic era, both in terms of the accuracy of the species tree it produces as well as in terms of elucidating the processes that shaped the evolutionary history. Bayesian methods for species tree inference are desirable in this area as they have been shown not only to yield accurate estimates, but also to naturally provide measures of confidence in those estimates. However, the heavy computational requirements of Bayesian inference have limited the applicability of such methods to very small data sets. In this article, we show that the computational efficiency of Bayesian inference under the multispecies coalescent can be improved in practice by restricting the space of the gene trees explored during the random walk, without sacrificing accuracy as measured by various metrics. The idea is to first infer constraints on the trees of the individual loci in the form of unresolved gene trees, and then to restrict the sampler to consider only resolutions of the constrained trees. We demonstrate the improvements gained by such an approach on both simulated and biological data. 

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5. Generation of accurate, expandable phylogenomic trees with uDance

   https://doi.org/10.1038/s41587-023-01868-8  

   Balaban, Metin; Jiang, Yueyu; Zhu, Qiyun; McDonald, Daniel; Knight, Rob; Mirarab, Siavash (May 2024, Nature Biotechnology)

   Phylogenetic trees provide a framework for organizing evolutionary histories across the tree of life and aid downstream comparative analyses such as metagenomic identification. Methods that rely on single-marker genes such as 16S rRNA have produced trees of limited accuracy with hundreds of thousands of organisms, whereas methods that use genome-wide data are not scalable to large numbers of genomes. We introduce updating trees using divide-and-conquer (uDance), a method that enables updatable genome-wide inference using a divide-and-conquer strategy that refines different parts of the tree independently and can build off of existing trees, with high accuracy and scalability. With uDance, we infer a species tree of roughly 200,000 genomes using 387 marker genes, totaling 42.5 billion amino acid residues. 

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