Biology subjects

Reaz, R.

Publications and source records attributed to Reaz, R..

6 recordsLinked to original sources

bioRxiv2026

STEQ: A statistically consistent quartet distance based species tree estimation method

Accurate estimation of large-scale species trees from multilocus data in the presence of gene tree discordance remains a major challenge in phylogenomics. Although maximum likelihood, Bayesian, and statistically consistent summary methods can infer species trees with high accuracy, most of these methods are slow and not scalable to large number of taxa and genes. One of the promising ways for enabling large-scale phylogeny estimation is distance based estimation methods. Here, we present STEQ, a new statistically consistent, fast, and accurate distance based method to estimate species trees from a collection of gene trees. We used a quartet based distance metric which is statistically consistent under the multi-species coalescent (MSC) model. The running time of STEQ scales as [O] (kn2 log n), for n taxa and k genes, which is asymptotically faster than the leading summary based methods such as ASTRAL. We evaluated the performance of STEQ in comparison with ASTRAL and wQFM-TREE - two of the most popular and accurate coalescent-based methods. Experimental findings on a collection of simulated and empirical datasets suggest that STEQ enables significantly faster inference of species trees while maintaining competitive accuracy with the best current methods. STEQ is publicly available at https://github.com/prottoysaha99/STEQ.

bioinformatics↗

bioRxiv2024

Leveraging weighted quartet distributions for enhanced species tree inference from genome-wide data

Species tree estimation from genes sampled from throughout the whole genome is challeng-ing in the presence of gene tree discordance, often caused by incomplete lineage sorting (ILS), where alleles can coexist in populations for periods that may span several speciation events. Quartet-based summary methods for estimating species trees from a collection of gene trees are becoming popular due to their high accuracy and theoretical guarantees of robustness to arbitrarily high amounts of ILS. ASTRAL, the most widely used quartet-based method, aims to infer species trees by maximizing the number of quartets in the gene trees that are consistent with the species tree. An alternative approach (as in wQFM) is to infer quartets for all subsets of four species and amalgamate them into a coherent species tree. While summary methods can be highly sensitive to gene tree estimation errors-especially when gene trees are derived from short alignments-quartet amalgamation offers an advantage by potentially bypassing the need for gene tree estimation. However, greatly understudied is the choice of weighted quar-tet inference method and downstream effects on species tree estimations under realistic model conditions. In this study, we investigated a broad range of methods for generating weighted quartets and critically assessed their impact on species tree inference. Our results on a collec-tion of simulated and empirical datasets suggest that amalgamating quartets weighted based on gene tree frequencies (GTF) typically produces more accurate trees than leading quartet-based methods like ASTRAL and SVDquartets. Further enhancements in GTF-based weighted quar-tet estimation were achieved by accounting for gene tree uncertainty, through the utilization of a distribution of trees for each gene (instead of a single tree), by employing traditional nonpara-metric bootstrapping methods or Bayesian MCMC sampling. Our study provides evidence that the careful generation and amalgamation of weighted quartets, as implemented in methods like wQFM, can lead to significantly more accurate trees compared to widely employed methods like ASTRAL, especially in the face of gene tree estimation errors.

bioinformatics↗

bioRxiv2024

wQFM-TREE: highly accurate and scalable quartet-based species tree inference from gene trees

Summary methods are becoming increasingly popular for species tree estimation from multi-locus data in the presence of gene tree discordance. ASTRAL, a leading method in this class, solves the Maximum Quartet Support Species Tree problem within a constrained solution space constructed from the input gene trees. In contrast, alternative heuristics such as wQFM and wQMC operate by taking a set of weighted quartets as input and employ a divide-and-conquer strategy to construct the species tree. Recent studies showed wQFM to be more accurate than ASTRAL and wQMC, though its scalability is hindered by the computational demands of explicitly generating and weighting {Theta}(n4) quartets. Here, we introduce wQFM-TREE, a novel summary method that enhances wQFM by circumventing the need for explicit quartet generation and weighting, thereby enabling its application to large datasets. Unlike wQFM, wQFM-TREE can also handle polytomies. Extensive simulations under diverse and challenging model conditions, with hundreds or thousands of taxa and genes, consistently demonstrate that wQFM-TREE matches or improves upon the accuracy of ASTRAL. Specifically, wQFM-TREE outperformed ASTRAL in 25 of 27 model conditions analyzed in this study involving 200-1000 taxa, with statistically significant differences in 20 of these conditions. Moreover, we applied wQFM-TREE to re-analyze the green plant dataset from the One Thousand Plant Transcriptomes Initiative. Its remarkable accuracy and scalability position wQFM-TREE as a highly competitive alternative to leading methods in the field. Additionally, the algorithmic and combinatorial innovations introduced in this study will benefit various quartet-based computations, advancing the state-of-the-art in phylogenetic estimations.

evolutionary biology↗

bioRxiv2021

QT-GILD: Quartet Based Gene Tree Imputation Using Deep Learning Improves Phylogenomic Analyses Despite Missing Data

Species tree estimation is frequently based on phylogenomic approaches that use multiple genes from throughout the genome. However, for a combination of reasons (ranging from sampling biases to more biological causes, as in gene birth and loss), gene trees are often incomplete, meaning that not all species of interest have a common set of genes. Incomplete gene trees can potentially impact the accuracy of phylogenomic inference. We, for the first time, introduce the problem of imputing the quartet distribution induced by a set of incomplete gene trees, which involves adding the missing quartets back to the quartet distribution. We present QT-GILD, an automated and specially tailored unsupervised deep learning technique, accompanied by cues from natural language processing (NLP), which learns the quartet distribution in a given set of incomplete gene trees and generates a complete set of quartets accordingly. QT-GILD is a general-purpose technique needing no explicit modeling of the subject system or reasons for missing data or gene tree heterogeneity. Experimental studies on a collection of simulated and empirical data sets suggest that QT-GILD can effectively impute the quartet distribution, which results in a dramatic improvement in the species tree accuracy. Remarkably, QT-GILD not only imputes the missing quartets but it can also account for gene tree estimation error. Therefore, QT-GILD advances the state-of-the-art in species tree estimation from gene trees in the face of missing data. QT-GILD is freely available in open source form at https://github.com/pythonLoader/QT-GILD.

bioinformatics↗

bioRxiv2020

wQFM: Statistically Consistent Genome-scale Species Tree Estimation from Weighted Quartets

MotivationSpecies tree estimation from genes sampled from throughout the whole genome is complicated due to the gene tree-species tree discordance. Incomplete lineage sorting (ILS) is one of the most frequent causes for this discordance, where alleles can coexist in populations for periods that may span several speciation events. Quartet-based summary methods for estimating species trees from a collection of gene trees are becoming popular due to their high accuracy and statistical guarantee under ILS. Generating quartets with appropriate weights, where weights correspond to the relative importance of quartets, and subsequently amalgamating the weighted quartets to infer a single coherent species tree allows for a statistically consistent way of estimating species trees. However, handling weighted quartets is challenging. ResultsWe propose wQFM, a highly accurate method for species tree estimation from multi-locus data, by extending the quartet FM (QFM) algorithm to a weighted setting. wQFM was assessed on a collection of simulated and real biological datasets, including the avian phylogenomic dataset which is one of the largest phylogenomic datasets to date. We compared wQFM with wQMC, which is the best alternate method for weighted quartet amalgamation, and with ASTRAL, which is one of the most accurate and widely used coalescent-based species tree estimation methods. Our results suggest that wQFM matches or improves upon the accuracy of wQMC and ASTRAL. AvailabilitywQFM is available in open source form at https://github.com/Mahim1997/wQFM-2020.

bioinformatics↗

bioRxiv2019

STELAR: A statistically consistent coalescent-based species tree estimation method by maximizing triplet consistency

BackgroundSpecies tree estimation is frequently based on phylogenomic approaches that use multiple genes from throughout the genome. However, estimating a species tree from a collection of gene trees can be complicated due to the presence of gene tree incongruence resulting from incomplete lineage sorting (ILS), which is modelled by the multi-species coalescent process. Maximum likelihood and Bayesian MCMC methods can potentially result in accurate trees, but they do not scale well to large datasets.\n\nResultsWe present STELAR (Species Tree Estimation by maximizing tripLet AgReement), a new fast and highly accurate statistically consistent coalescent-based method for estimating species trees from a collection of gene trees. We formalized the constrained triplet consensus (CTC) problem and showed that the solution to the CTC problem is a statistically consistent estimate of the species tree under the multi-species coalescent (MSC) model. STELAR is an efficient dynamic programming based solution to the CTC problem which is highly accurate and scalable. We evaluated the accuracy of STELAR in comparison with SuperTriplets, which is an alternate fast and highly accurate triplet-based supertree method, and with MP-EST and ASTRAL - two of the most popular and accurate coalescent-based methods. Experimental results suggest that STELAR matches the accuracy of ASTRAL and improves on MP-EST and SuperTriplets.\n\nConclusionsTheoretical and empirical results (on both simulated and real biological datasets) suggest that STELAR is a valuable technique for species tree estimation from gene tree distributions.

bioinformatics↗