Biology subjects

Bansal, M. S.

Publications and source records attributed to Bansal, M. S..

8 recordsLinked to original sources

bioRxiv2026

Optimizing copy number profiles for tumor cell phylogeny inference from low-coverage single-cell sequencing data

Motivation: Accurate reconstruction of tumor cell phylogenies is crucial for understanding cancer evolutionary dynamics, inferring intra-tumor heterogeneity, and designing effective and personalized cancer treatment strategies. One of the most promising and cost-effective approaches for reconstructing tumor phylogenies for large numbers of cells is to use copy number alteration (CNA) data obtained from low-coverage single-cell DNA sequencing of sampled tumor cells. However, error and uncertainty in inferred copy number profiles (CNPs), resulting from low sequence coverage, can have a drastic negative effect on the reconstruction accuracy of tumor cell phylogenies. Results: In this work, we show that careful selection of bin size, a critical tuning parameter for inferring CNPs, is necessary to maximize the accuracy of tumor cell phylogeny inference. Specifically, we (i) use three previously published low-coverage single-cell DNA sequencing datasets from real tumors to demonstrate that even small changes in bin size can have a drastic impact on downstream tumor phylogeny reconstruction, (ii) show that this effect persists even when using simulated low-coverage datasets generated under idealized conditions with minimal noise, and (iii) propose a simple computational approach to select bin sizes that maximize tumor phylogeny accuracy. Our experiments span a wide range of bin sizes and account for the impact of various factors including coverage-level, phylogeny reconstruction method, and CNP inference method, and explicitly account for phylogenetic reconstruction uncertainty. Our evaluation of the proposed computational approach for bin size selection shows that it can significantly improve tumor cell phylogeny reconstruction accuracy compared to baseline methods. Availability: Freely available from https://github.com/Tanveer14/BinSelector.

bioinformatics↗

bioRxiv2024

A systematic assessment of phylogenomic approaches for microbial species tree reconstruction

A key challenge in microbial phylogenomics is that microbial gene families are often affected by extensive horizontal gene transfer (HGT). As a result, most existing methods for microbial phylogenomics can only make use of a small subset of the gene families present in the microbial genomes under consideration, potentially biasing their results and affecting their accuracy. To address this challenge, several methods have recently been developed for inferring microbial species trees from genome-scale datasets of gene families affected by evolutionary events such as HGT, gene duplication, and gene loss. In this work, we use extensive simulated and real biological datasets to systematically assess the accuracies of four recently developed methods for microbial phylogenomics, SpeciesRax, ASTRAL-Pro 2, PhyloGTP, and AleRax, under a range of different conditions. Our analysis reveals important insights into the relative performance of these methods on datasets with different characteristics, identifies shared weaknesses when analyzing complex biological datasets, and demonstrates the importance of accounting for gene tree inference error/uncertainty for improved species tree reconstruction. Among other results, we find that (i) AleRax, the only method that explicitly accounts for gene tree inference error/uncertainty, shows the best species tree reconstruction accuracy among all tested methods, (ii) PhyloGTP (developed previously by the authors of this paper) shows the best overall accuracy among methods that do not account for gene tree error and uncertainty, (iii) ASTRAL-Pro 2 is less accurate than the other methods across nearly all tested conditions, and (iv) explicitly accounting for gene tree inference error/uncertainty can lead to substantial improvements in species tree reconstruction accuracy. Importantly, we also find that all methods, including AleRax and PhyloGTP, are susceptible to biases present in complex real biological datasets and can sometimes yield misleading phylogenies.

bioinformatics↗

bioRxiv2024

DupLoss-2: Improved Phylogenomic Species Tree Inference under Gene Duplication and Loss

Accurate species tree reconstruction in the presence of widespread gene duplication and loss is a challenging problem in eukaryote phylogenomics. Many phylogenomics methods have been developed over the years to address this challenge; these range from older methods based on gene tree parsimony to newer quartet-based methods. In this work, we introduce improved software for gene tree parsimony-based species tree reconstruction under gene duplication and loss. The new software, DupLoss-2, uses an improved procedure for computing gene losses and is far more accurate and much easier to use than its previous version released over a decade ago. We thoroughly evaluate DupLoss-2 and eight other existing methods, including ASTRAL-Pro, ASTRAL-Pro 2, DISCO-ASTRAL, DISCO-ASTRID, FastMulRFS, and SpeciesRax, using existing benchmarking data and find that DupLoss-2 outperforms all other methods on most of the datasets. It delivers an average of almost 30% reduction in reconstruction error compared to iGTP-Duploss, the previous version of this software, and a 10% reduction compared to the best performing existing method. DupLoss-2 is written in C++ and is freely available open-source.

bioinformatics↗

bioRxiv2024

DICE: Fast and Accurate Distance-Based Reconstruction of Single-Cell Copy Number Phylogenies

Somatic copy number alterations (sCNAs) are valuable phylogenetic markers for inferring evolutionary relationships among tumor cell subpopulations. Advances in single-cell DNA sequencing technologies are making it possible to obtain such sCNAs datasets at ever-larger scales. However, existing methods for reconstructing phylogenies from sCNAs are often too slow for large datasets. Moreover, the accuracies of many existing methods are highly sensitive to error and other features of the analyzed datasets. In this work, we propose two new distance-based approaches for reconstructing single-cell tumor phylogenies from sCNA data. The new methods, DICE-bar and DICE-star, are based on novel, easy-to-compute distance measures and drastically outperform the current state-of-the-art in terms of both accuracy and scalability. Using carefully simulated datasets, we find that DICE-bar and DICE-star significantly improve upon the accuracies of existing methods across a wide range of experimental conditions and error rates while simultaneously being orders of magnitude faster. Our experimental analysis also reveals how noise/error in copy number inference, as expected for real datasets, can drastically impact the accuracies of many existing methods. We apply DICE-star, the most accurate method on error-prone datasets, to two real single-cell breast cancer datasets and find that it helps identify previously unreported rare cell populations.

bioinformatics↗

bioRxiv2021

Phylogenetic reconciliation reveals extensive ancestral recombination in Sarbecoviruses and the SARS-CoV-2 lineage

An accurate understanding of the evolutionary history of rapidly-evolving viruses like SARS-CoV-2, responsible for the COVID-19 pandemic, is crucial to tracking and preventing the spread of emerging pathogens. However, viruses undergo frequent recombination, which makes it difficult to trace their evolutionary history using traditional phylogenetic methods. Here, we present a phylogenetic workflow, virDTL, for analyzing viral evolution in the presence of recombination. Our approach leverages reconciliation methods developed for inferring horizontal gene transfer in prokaryotes, and, compared to existing tools, is uniquely able to identify ancestral recombinations while accounting for several sources of inference uncertainty, including in the construction of a strain tree, estimation and rooting of gene family trees, and reconciliation itself. We apply this workflow to the Sarbecovirus subgenus and demonstrate how a principled analysis of predicted recombination gives insight into the evolution of SARS-CoV-2. In addition to providing confirming evidence for the horseshoe bat as its zoonotic origin, we identify several ancestral recombination events that merit further study.

evolutionary biology↗

bioRxiv2020

Systematic Detection of Large-Scale Multi-Gene Horizontal Transfer in Prokaryotes

Horizontal gene transfer (HGT) is central to prokaryotic evolution. However, little is known about the "scale" of individual HGT events. In this work, we introduce the first computational framework to help answer the following fundamental question: How often does more than one gene get horizontally transferred in a single HGT event? Our method, called HoMer, uses phylogenetic reconciliation to infer single-gene HGT events across a given set of species/strains, employs several techniques to account for inference error and uncertainty, combines that information with gene order information from extant genomes, and uses statistical analysis to identify candidate horizontal multi-gene transfers (HMGTs) in both extant and ancestral species/strains. HoMer is highly scalable and can be easily used to infer HMGTs across hundreds of genomes. We apply HoMer to a genome-scale dataset of over 22000 gene families from 103 Aeromonas genomes and identify a large number of plausible HMGTs of various scales at both small and large phylogenetic distances. Analysis of these HMGTs reveals interesting relationships between gene function, phylogenetic distance, and frequency of multi-gene transfer. Among other insights, we find that (i) the relative frequency of HMGT increases as divergence between genomes increases, (ii) HMGTs often have conserved gene functions, and (iii) rare genes are frequently acquired through HMGT. We also analyze in detail HMGTs involving the zonula occludens toxin and type III secretion systems. By enabling the systematic inference of HMGTs on a large scale, HoMer will facilitate a more accurate and more complete understanding of HGT and microbial evolution.

evolutionary biology↗

bioRxiv2020

On Inferring Additive and Replacing Horizontal Gene Transfers Through Phylogenetic Reconciliation

Horizontal gene transfer is one of the most important mechanisms for microbial evolution and adaptation. It is well known that horizontal gene transfer can be either additive or replacing depending on whether the transferred gene adds itself as a new gene in the recipient genome or replaces an existing homologous gene. Yet, all existing phylogenetic techniques for the inference of horizontal gene transfer assume either that all transfers are additive or that all transfers are replacing. This limitation not only affects the applicability and accuracy of these methods but also makes it difficult to distinguish between additive and replacing transfers. Here, we address this important problem by formalizing a phylogenetic reconciliation framework that simultaneously models both additive and replacing transfer events. Specifically, we (1) introduce the DTRL reconciliation framework that explicitly models both additive and replacing transfer events, along with gene duplications and losses, (2) prove that the underlying computational problem is NP-hard, (3) perform the first experimental study to assess the impact of replacing transfer events on the accuracy of the traditional DTL reconciliation model (which assumes that all transfers are additive) and demonstrate that traditional DTL reconciliation remains highly robust to the presence of replacing transfers, (4) propose a simple heuristic algorithm for DTRL reconciliation based on classifying transfer events inferred through DTL reconciliation as being replacing or additive, and (5) evaluate the classification accuracy of the heuristic under a range of evolutionary conditions. Thus, this work lays the methodological and algorithmic foundations for estimating DTRL reconciliations and distinguishing between additive and replacing transfers. An implementation of our heuristic for DTRL reconciliation is freely available open-source as part of the RANGER-DTL software package from https://compbio.engr.uconn.edu/software/ranger-dtl/.

bioinformatics↗

bioRxiv2019

TreeFix-TP: Phylogenetic Error-Correction for Infectious Disease Transmission Network Inference

BackgroundMany existing methods for estimation of infectious disease transmission networks use a phylogeny of the infecting strains as the basis for transmission network inference, and accurate network inference relies on accuracy of this underlying evolutionary history. However, phylogenetic reconstruction can be highly error prone and more sophisticated methods can fail to scale to larger outbreaks, negatively impacting downstream transmission network inference. Additionally, there are no currently available methods which are able to use within-host diversity to improve phylogenetic reconstruction.\n\nResultsWe introduce a new method, TreeFix-TP, for accurate and scalable reconstruction of transmission phylogenies based on an error-correction framework. Our method uses intra-host strain diversity and host information to balance a parsimonious evaluation of the implied transmission network with statistical hypothesis testing on sequence data likelihood. The reconstructed tree minimizes the number of required disease transmissions while being as well supported by sequence data as the maximum likelihood phylogeny. We use a simulation framework for viral transmission and evolution to demonstrate that TreeFix-TP improves phylogenetic accuracy and downstream transmission network accuracy. We also use real data from ten HCV outbreaks and demonstrate how error-correction improves source detection.\n\nConclusionsOur results show that using TreeFix-TP can lead to significant improvement in transmission phylogeny inference and that its performance is robust to variations in transmission and evolutionary parameters. Our experiments also demonstrate the importance of sampling multiple strain sequences from each infected host for accurate transmission network inference. TreeFix-TP is freely available open-source from https://compbio.engr.uconn.edu/software/treefix-tp/.

bioinformatics↗