Biology subjects

Arasti, S.

Publications and source records attributed to Arasti, S..

6 recordsLinked to original sources

bioRxiv2026

BROOQS: Spectral Methods Resolve Level-1 Hybridization Cycles without Tests of Symmetry

Modern phylogenomic analyses often seek to reconstruct both vertical and reticulate evolutionary histories. While the prevalence of non-vertical evolution is increasingly appreciated, inferring networks remains conceptually challenging and computationally demanding. Following the success of quartet-based methods for handling gene tree discordance, several quartet-based network inference methods have been developed. A key insight of these methods is that level-1 networks can be constructed by first building a multifurcating tree called tree-of-blobs and then resolving each polytomy into a cycle. This two-step approach makes the problem easier both conceptually and computationally. However, these quartet-based methods often rely on noisy statistical tests of asymmetry in quartet frequencies. Moreover, they either enumerate all quartets, losing some scalability, or subsample them, losing information. We introduce BROOQS, a quartet-based method for resolving trees of blobs into a level-1 phylogenetic network. BROOQS efficiently aggregates information from all quartets around a blob without enumerating them, builds a pairwise similarity matrix, and uses robust spectral ordering algorithms to recover the cyclic ordering without relying on individual quartet symmetry tests. We prove theoretically that our spectral method is consistent under the network multi-species coalescent (NMSC) model. Across simulated and empirical datasets, BROOQS consistently improves accuracy and scalability compared to existing methods and extends to thousands of taxa.

bioinformatics↗

bioRxiv2026

Phlag: Scalable detection of genomics regions with unexplained phylogenetic heterogeneity

MotivationPhylogenetic analyses of entire genomes (phylogenomics) have revealed abundant heterogeneity of evolutionary histories. While much has been done to model this heterogeneity and to infer species trees despite it, the current toolkit has a limitation. Most methods assume that gene trees across the genome differ but are all sampled from the same distribution, defined by models such as the multi-species coalescent (MSC), and parametrized consistently across the genome. Empirical data strongly suggest this assumption is often violated because the species tree, its parameters, or the process generating the gene trees can all change across the genome. Errors in the data can further compound this heterogeneity. ResultsTo address this challenge, we define the problem of detecting what segments of the genome are inconsistent with a putative species tree, even after allowing discordance according to MSC. We model gene trees not as a set, but rather as a series (a realization of a stochastic process) along genomic positions. We propose a Hidden Markov Model (HMM) approach applied to quartet statistics measured from gene trees and tie the model to MSC using simulations. The combined use of these three ideas leads to a scalable method called Phlag. On simulated and real data, we show that Phlag can detect many cases of change in underlying evolutionary processes, including reduced recombination rates, population size changes, and admixture, all using the same algorithm. Availability and ImplementationPhlag is available at github.com/bo1929/phlag. All results and scripts can be found at github.com/bo1929/shared.phlag.

evolutionary biology↗

bioRxiv2026

Deconvolving Phylogenetic Distance Mixtures

Mixtures of multiple constituent organisms are sequenced in several widely used applications, including metagenomics and metabarcoding. Characterizing the elements of the sequence mixture and their abundance with respect to a reference set of known organisms has been the subject of intense research across several domains, including microbiome analyses, and methods must overcome two key challenges. First, the mixture constituents are related to each other through an evolutionary history, and hence, should not be considered independent entities. Second, sequence data is noisy, with each short read providing a limited signal. While existing approaches attempt to address these challenges, addressing both challenges simultaneously has proved challenging. For evolutionary dependencies, methods either define hierarchical clusters (e.g., taxonomies or operational taxonomic/genomic units) or use phylogenetic trees. For the second challenge, they either assemble reads into contigs, use statistical priors to summarize read placements, or attempt to analyze all reads jointly using k-mers. Despite this rich literature, a natural approach to simultaneously address both challenges has been underexplored: compute a distance from the mixture to all references, deconvolve those distances, and place the sample on multiple branches of a reference phylogeny with associated abundances. This multi-placement approach is a natural extension of the single-read phylogenetic placement used in practice. We argue that by placing the entire sample on multiple branches instead of placing reads individually, we can obtain a less noisy profile of the mixture. We formalize this approach as the phylogenetic distance deconvolution (PDD) problem, show some limits on the identifiability of PDDs, propose a slow exact algorithm, and an efficient heuristic greedy algorithm with local refinements. Benchmarking shows that these heuristics are effective and that our implementation of the PDD approach (called DecoDiPhy) can accurately deconvolve phylogenetic mixture distances while scaling quadratically. Applied to metagenomics, DecoDiPhy consolidates reads mapped to a large number of branches on a reference tree to a much smaller number of placements. The consolidated placements improve the accuracy of downstream tasks, such as sample differentiation and detection of differentially abundant taxa.

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

Detecting interspecific positive selection using convolutional neural networks

Traditional statistical methods using maximum likelihood and Bayesian inference can detect positive selection from an interspecific phylogeny and a codon sequence alignment based on model assumptions, but they are prone to false positives due to alignment errors and can lack power. These problems are particularly pronounced when faced with high levels of indels and divergence. Leveraging the feature-detection capabilities of convolutional neural networks (CNNs), we achieve higher accuracy in detecting selection across a specific range of phylogenetic scenarios and evolutionary modes. This advantage is particularly evident with noisy data prone to misalignments. Our method shows some ability to account for these errors, where most statistical frameworks fail to do so in a tractable manner. We explore generalisability and identify future avenues to achieve broader utility. Once trained, our CNN model is faster at test time, making it a scalable alternative to traditional statistical methods for large-scale, multi-gene analyses. In addition to binary classification (inference of the presence or absence of positive selection during the evolution of the sequences), we use saliency maps to understand what the model learns and observe how this could be leveraged for sitewise inference of positive selection.

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↗