Biology subjects

Tabatabaee, Y.

Publications and source records attributed to Tabatabaee, Y..

6 recordsLinked to original sources

bioRxiv2025

Coalescent-based branch length estimation improves dating of species trees

Species trees need to be dated for many downstream applications. Typical molecular dating methods take a phylogenetic tree with branch lengths in substitution units as well as a set of calibrations as input and convert the branch lengths of the species tree to the unit of time while being consistent with the pre-specified calibrations. When dating species trees from multi-locus genome-scale datasets, the branch lengths and sometimes the topology of the species tree are estimated using concatenation. However, concatenation does not address gene tree heterogeneity across the genome. While Bayesian dating methods can address some forms of gene tree heterogeneity, such as incomplete lineage sorting, they are not scalable to large datasets. In this paper, we introduce a new scalable pipeline for dating species trees that addresses gene tree discordance for both topology and branch length estimation. The pipeline uses discordance-aware methods that account for incomplete lineage sorting for estimating the topology and branch lengths and maximum likelihood-based methods for the dating step. Our simulation study on datasets with gene tree discordance shows that this pipeline produces more accurate and less biased dates than pipelines that use concatenation or unpartitioned Bayesian methods. Furthermore, it is substantially more scalable and can handle datasets with thousands of species and genes. Our results on two biological datasets show that this new pipeline improves the inference of node ages and branch lengths for some nodes, in particular extant taxa, and improves the downstream diversification analysis.

evolutionary biology↗

bioRxiv2025

Species tree branch length estimation despite incomplete lineage sorting, duplication, and loss

Phylogenetic branch lengths are essential for many analyses, such as estimating divergence times, analyzing rate changes, and studying adaptation. However, true gene tree heterogeneity due to incomplete lineage sorting (ILS), gene duplication and loss (GDL), and horizontal gene transfer (HGT) can complicate the estimation of species tree branch lengths. While several tools exist for estimating the topology of a species tree addressing various causes of gene tree discordance, much less attention has been paid to branch length estimation on multi-locus datasets. For single-copy gene trees, some methods are available that summarize gene tree branch lengths onto a species tree, including coalescent-based methods that account for heterogeneity due to ILS. However, no such branch length estimation method exists for multi-copy gene family trees that have evolved with gene duplication and loss. To address this gap, we introduce the CASTLES-Pro algorithm for estimating species tree branch lengths while accounting for both GDL and ILS. CASTLES-Pro improves on the existing coalescent-based branch length estimation method CASTLES by increasing its accuracy for single-copy gene trees and extends it to handle multi-copy ones. Our simulation studies show that CASTLES-Pro is generally more accurate than alternatives, eliminating the systematic bias toward overestimating terminal branch lengths often observed when using concatenation. Moreover, while not theoretically designed for HGT, we show that CASTLES-Pro maintains relatively high accuracy under high rates of random HGT. Code availabilityCASTLES-Pro is implemented inside the software package ASTER, available at https://github.com/chaoszhang/ASTER. Data availabilityThe datasets and scripts used in this study are available at https://github.com/ytabatabaee/CASTLES-Pro-paper.

evolutionary biology↗

bioRxiv2024

Advances in Estimating Level-1 Phylogenetic Networks from Unrooted SNPs

We address the problem of how to estimate a phylogenetic network when given SNPs (i.e., single nucleotide polymorphisms, or bi-allelic markers that have evolved under the infinite sites assumption). We focus on level-1 phylogenetic networks (i.e., networks where the cycles are node-disjoint), since more complex networks are unidentifiable. We provide a polynomial time quartet-based method that we prove correct for reconstructing the unrooted topology of any level-1 phylogenetic network N, if we are given a set of SNPs that covers all the bipartitions of N, even if the ancestral state is not known, provided that the cycles are of length at least 5; we also prove that an algorithm developed by Dan Gusfield in JCSS 2005 correctly recovers the unrooted topology in polynomial time in this case. To the best of our knowledge, this is the first result to establish that the unrooted topology of a level-1 network is uniquely recoverable from SNPs without known ancestral states. We also present a stochastic model for DNA evolution, and we prove that the two methods (our quartet-based method and Gusfields method) are statistically consistent estimators of the unrooted topology of the level-1 phylogenetic network. For the case of multi-state homoplasy-free characters, we prove that our quartet-based method correctly constructs the unrooted topology of level-1 networks under the required conditions (all cycles of length at least five), while Gusfields algorithm cannot be used in that condition. These results assume that we have access to an oracle for indicating which sites in the DNA alignment are homoplasy-free, and we show that the methods are robust, under some conditions, to oracle errors.

evolutionary biology↗

bioRxiv2023

Optimal Tree Metric Matching Enables Phylogenomic Branch Length Reconciliation

The abundant discordance between evolutionary relationships across the genome has rekindled interest in methods for comparing and averaging trees on a shared leaf set. However, compared to tree topology, where much progress has been made, handling branch lengths has been more challenging. Species tree branch lengths can be measured in various units, often different from gene trees. Moreover, rates of evolution change across the genome, the species tree, and specific branches of gene trees. These factors compound the stochasticity of coalescence times and estimation noise, making branch lengths highly heterogeneous across the genome. For many downstream applications in phylogenomic analyses, branch lengths are as important as the topology, and yet, existing tools to compare and combine weighted trees are limited. In this paper, we address the question of matching one tree to another, accounting for their branch lengths. We define a series of computational problems called Topology-Constrained Metric Matching (TCMM) that seek to transform the branch lengths of a query tree based on a reference tree. We show that TCMM problems can be solved efficiently using a linear algebraic formulation coupled with dynamic programming preprocessing. While many applications can be imagined for this framework, we explore two applications in this paper: embedding leaves of gene trees in Euclidean space to find outliers potentially indicative of errors, and summarizing gene tree branch lengths onto the species tree. In these applications, our method, when paired with existing methods, increases their accuracy at limited computational expense.

bioinformatics↗

bioRxiv2023

DISCO+QR: Rooting Species Trees in the Presence of GDL and ILS

AO_SCPLOWBSTRACTC_SCPLOWGenes evolve under processes such as gene duplication and loss (GDL), so that gene family trees are multi-copy, as well as incomplete lineage sorting (ILS); both processes produce gene trees that differ from the species tree. The estimation of species trees from sets of gene family trees is challenging, and the estimation of rooted species trees presents additional analytical challenges. Two of the methods developed for this problem are STRIDE (Emms and Kelly, MBE 2017), which roots species trees by considering GDL events, and Quintet Rooting (Tabatabaee et al., ISMB 2022 and Bioinformatics 2022), which roots species trees by considering ILS. We present DISCO+QR, a new method for rooting species trees in the presence of both GDL and ILS. DISCO+QR, operates by taking the input gene family trees and decomposing them into single-copy trees using DISCO (Willson et al., Systematic Biology 2022) and then roots the given species tree using the information in the single-copy gene trees using Quintet Rooting (QR). We show that the relative accuracy of STRIDE and DISCO+QR depend on properties of the dataset (number of species, genes, rate of gene duplication, degree of ILS, and gene tree estimation error), and that each provides advantages over the other under some conditions. Availability: DISCO and QR are available in GitHub. The supplementary materials are available at http://tandy.cs.illinois.edu/discoqr-suppl.pdf.

bioinformatics↗

bioRxiv2022

Statistically Consistent Rooting of Species Trees under the Multi-Species Coalescent Model

Rooted species trees are used in several downstream applications of phylogenetics. Most species tree estimation methods produce unrooted trees and additional methods are then used to root these unrooted trees. Recently, Quintet Rooting (QR) (Tabatabaee et al., ISMB and Bioinformatics 2022), a polynomial-time method for rooting an unrooted species tree given unrooted gene trees under the multispecies coalescent, was introduced. QR, which is based on a proof of identifiability of rooted 5-taxon trees in the presence of incomplete lineage sorting, was shown to have good accuracy, improving over other methods for rooting species trees when incomplete lineage sorting was the only cause of gene tree discordance, except when gene tree estimation error was very high. However, the statistical consistency of QR was left as an open question. Here, we present QR-STAR, a polynomial-time variant of QR that has an additional step for determining the rooted shape of each quintet tree. We prove that QR-STAR is statistically consistent under the multispecies coalescent model. Our simulation study under a variety of model conditions shows that QR-STAR matches or improves on the accuracy of QR. QR-STAR is available in open source form at https://github.com/ytabatabaee/Quintet-Rooting.

bioinformatics↗