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1. ASTRAL-Pro: Quartet-Based Species-Tree Inference despite Paralogy

   https://doi.org/10.1093/molbev/msaa139  

   Zhang, Chao; Scornavacca, Celine; Molloy, Erin K; Mirarab, Siavash (September 2020, Molecular Biology and Evolution)

   Thorne, Jeffrey (Ed.)

   Abstract Phylogenetic inference from genome-wide data (phylogenomics) has revolutionized the study of evolution because it enables accounting for discordance among evolutionary histories across the genome. To this end, summary methods have been developed to allow accurate and scalable inference of species trees from gene trees. However, most of these methods, including the widely used ASTRAL, can only handle single-copy gene trees and do not attempt to model gene duplication and gene loss. As a result, most phylogenomic studies have focused on single-copy genes and have discarded large parts of the data. Here, we first propose a measure of quartet similarity between single-copy and multicopy trees that accounts for orthology and paralogy. We then introduce a method called ASTRAL-Pro (ASTRAL for PaRalogs and Orthologs) to find the species tree that optimizes our quartet similarity measure using dynamic programing. By studying its performance on an extensive collection of simulated data sets and on real data sets, we show that ASTRAL-Pro is more accurate than alternative methods. 

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2. 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 (April 2022, Systematic Biology)

   Solis-Lemus, Claudia (Ed.)

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

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

   Zhang, Chao; Mirarab, Siavash (September 2022, Bioinformatics)

   Schwartz, Russell (Ed.)

   Abstract Motivation 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. Results 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. Availability and implementation 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 information Supplementary data are available at Bioinformatics online. 

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4. 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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5. Polynomial-Time Statistical Estimation of Species Trees Under Gene Duplication and Loss

   Legried, Brandon; Molloy, Erin K.; Warnow, Tandy; Roch, Sebastien (January 2020, International Conference on Research in Computational Molecular Biology (RECOMB 2020))

   Phylogenomics---the estimation of species trees from multi-locus datasets---is a common step in many biological studies. However, this estimation is challenged by the fact that genes can evolve under processes, including incomplete lineage sorting (ILS) and gene duplication and loss (GDL), that make their trees different from the species tree. In this paper, we address the challenge of estimating the species tree under GDL. We show that species trees are identifiable under a standard stochastic model for GDL, and that the polynomial-time algorithm ASTRAL-multi, a recent development in the ASTRAL suite of methods, is statistically consistent under this GDL model. We also provide a simulation study evaluating ASTRAL-multi for species tree estimation under GDL. All scripts and datasets used in this study are available on the Illinois Data Bank: https://doi.org/10.13012/B2IDB-2626814_V1. 

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