scholarly journals Applicability of several rooted phylogenetic network algorithms for representing the evolutionary history of SARS-CoV-2

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Rosanne Wallin ◽  
Leo van Iersel ◽  
Steven Kelk ◽  
Leen Stougie
Keyword(s):  
Phylogenetic Trees ◽  
Evolutionary History ◽  
Network Inference ◽  
Phylogenetic Network ◽  
Phylogenetic Networks ◽  
Network Algorithms ◽  
Running Time ◽  
Inference Algorithms ◽  
History Of ◽  
The Impact

Abstract Background Rooted phylogenetic networks are used to display complex evolutionary history involving so-called reticulation events, such as genetic recombination. Various methods have been developed to construct such networks, using for example a multiple sequence alignment or multiple phylogenetic trees as input data. Coronaviruses are known to recombine frequently, but rooted phylogenetic networks have not yet been used extensively to describe their evolutionary history. Here, we created a workflow to compare the evolutionary history of SARS-CoV-2 with other SARS-like viruses using several rooted phylogenetic network inference algorithms. This workflow includes filtering noise from sets of phylogenetic trees by contracting edges based on branch length and bootstrap support, followed by resolution of multifurcations. We explored the running times of the network inference algorithms, the impact of filtering on the properties of the produced networks, and attempted to derive biological insights regarding the evolution of SARS-CoV-2 from them. Results The network inference algorithms are capable of constructing rooted phylogenetic networks for coronavirus data, although running-time limitations require restricting such datasets to a relatively small number of taxa. Filtering generally reduces the number of reticulations in the produced networks and increases their temporal consistency. Taxon bat-SL-CoVZC45 emerges as a major and structural source of discordance in the dataset. The tested algorithms often indicate that SARS-CoV-2/RaTG13 is a tree-like clade, with possibly some reticulate activity further back in their history. A smaller number of constructed networks posit SARS-CoV-2 as a possible recombinant, although this might be a methodological artefact arising from the interaction of bat-SL-CoVZC45 discordance and the optimization criteria used. Conclusion Our results demonstrate that as part of a wider workflow and with careful attention paid to running time, rooted phylogenetic network algorithms are capable of producing plausible networks from coronavirus data. These networks partly corroborate existing theories about SARS-CoV-2, and partly produce new avenues for exploration regarding the location and significance of reticulate activity within the wider group of SARS-like viruses. Our workflow may serve as a model for pipelines in which phylogenetic network algorithms can be used to analyse different datasets and test different hypotheses.

scholarly journals Comparing the topology of phylogenetic network generators

2021 ◽  
Author(s):  
Remie Janssen ◽  
Pengyu Liu
Keyword(s):  
Bayesian Methods ◽  
Phylogenetic Trees ◽  
Evolutionary History ◽  
Phylogenetic Network ◽  
Random Trees ◽  
Phylogenetic Networks ◽  
Phylogeny Reconstruction ◽  
Evolutionary Analysis ◽  
Summary Statistics ◽  
History Of

Phylogenetic networks represent evolutionary history of species and can record natural reticulate evolutionary processes such as horizontal gene transfer and gene recombination. This makes phylogenetic networks a more comprehensive representation of evolutionary history compared to phylogenetic trees. Stochastic processes for generating random trees or networks are important tools in evolutionary analysis, especially in phylogeny reconstruction where they can be utilized for validation or serve as priors for Bayesian methods. However, as more network generators are developed, there is a lack of discussion or comparison for different generators. To bridge this gap, we compare a set of phylogenetic network generators by profiling topological summary statistics of the generated networks over the number of reticulations and comparing the topological profiles.

INFERRING PHYLOGENETIC RELATIONSHIPS AVOIDING FORBIDDEN ROOTED TRIPLETS

2006 ◽  
Vol 04 (01) ◽  
pp. 59-74 ◽  
Author(s):  
YING-JUN HE ◽  
TRINH N. D. HUYNH ◽  
JESPER JANSSON ◽  
WING-KIN SUNG
Keyword(s):  
Approximation Algorithms ◽  
Phylogenetic Tree ◽  
Phylogenetic Trees ◽  
Evolutionary History ◽  
Phylogenetic Network ◽  
Evolutionary Relationships ◽  
Large Set ◽  
Tree Network ◽  
History Of ◽  
Overlapping Sets

To construct a phylogenetic tree or phylogenetic network for describing the evolutionary history of a set of species is a well-studied problem in computational biology. One previously proposed method to infer a phylogenetic tree/network for a large set of species is by merging a collection of known smaller phylogenetic trees on overlapping sets of species so that no (or as little as possible) branching information is lost. However, little work has been done so far on inferring a phylogenetic tree/network from a specified set of trees when in addition, certain evolutionary relationships among the species are known to be highly unlikely. In this paper, we consider the problem of constructing a phylogenetic tree/network which is consistent with all of the rooted triplets in a given set [Formula: see text] and none of the rooted triplets in another given set [Formula: see text]. Although NP-hard in the general case, we provide some efficient exact and approximation algorithms for a number of biologically meaningful variants of the problem.

scholarly journals Phylogenetic Trees and Networks Can Serve as Powerful and Complementary Approaches for Analysis of Genomic Data

2019 ◽  
Vol 69 (3) ◽  
pp. 593-601 ◽  
Author(s):  
Christopher Blair ◽  
Cécile Ané
Keyword(s):  
Gene Flow ◽  
Phylogenetic Trees ◽  
Evolutionary History ◽  
Incomplete Lineage Sorting ◽  
Gene Tree ◽  
Phylogenetic Network ◽  
Genomic Data ◽  
Point Of View ◽  
Phylogenetic Networks ◽  
Lineage Sorting

Abstract Genomic data have had a profound impact on nearly every biological discipline. In systematics and phylogenetics, the thousands of loci that are now being sequenced can be analyzed under the multispecies coalescent model (MSC) to explicitly account for gene tree discordance due to incomplete lineage sorting (ILS). However, the MSC assumes no gene flow post divergence, calling for additional methods that can accommodate this limitation. Explicit phylogenetic network methods have emerged, which can simultaneously account for ILS and gene flow by representing evolutionary history as a directed acyclic graph. In this point of view, we highlight some of the strengths and limitations of phylogenetic networks and argue that tree-based inference should not be blindly abandoned in favor of networks simply because they represent more parameter rich models. Attention should be given to model selection of reticulation complexity, and the most robust conclusions regarding evolutionary history are likely obtained when combining tree- and network-based inference.

scholarly journals Temporal phylogenetic networks and logic programming

2006 ◽  
Vol 6 (5) ◽  
pp. 539-558 ◽  
Author(s):  
ESRA ERDEM ◽  
VLADIMIR LIFSCHITZ ◽  
DON RINGE
Keyword(s):  
Mathematical Model ◽  
Logic Programming ◽  
Evolutionary History ◽  
Phylogenetic Network ◽  
Answer Set Programming ◽  
Phylogenetic Networks ◽  
Natural Languages ◽  
European Languages ◽  
History Of ◽  
Linguistic Communities

The concept of a temporal phylogenetic network is a mathematical model of evolution of a family of natural languages. It takes into account the fact that languages can trade their characteristics with each other when linguistic communities are in contact, and also that a contact is only possible when the languages are spoken at the same time. We show how computational methods of answer set programming and constraint logic programming can be used to generate plausible conjectures about contacts between prehistoric linguistic communities, and illustrate our approach by applying it to the evolutionary history of Indo-European languages.

scholarly journals A test statistic to quantify treelikeness in phylogenetics

2021 ◽  
Author(s):  
Caitlin Cherryh ◽  
Bui Quang Minh ◽  
Rob Lanfear
Keyword(s):  
Evolutionary History ◽  
Incomplete Lineage Sorting ◽  
Phylogenetic Analyses ◽  
Phylogenetic Network ◽  
Parametric Bootstrap ◽  
Lineage Sorting ◽  
Test Statistic ◽  
Sequence Alignments ◽  
Wide Range ◽  
History Of

AbstractMost phylogenetic analyses assume that the evolutionary history of an alignment (either that of a single locus, or of multiple concatenated loci) can be described by a single bifurcating tree, the so-called the treelikeness assumption. Treelikeness can be violated by biological events such as recombination, introgression, or incomplete lineage sorting, and by systematic errors in phylogenetic analyses. The incorrect assumption of treelikeness may then mislead phylogenetic inferences. To quantify and test for treelikeness in alignments, we develop a test statistic which we call the tree proportion. This statistic quantifies the proportion of the edge weights in a phylogenetic network that are represented in a bifurcating phylogenetic tree of the same alignment. We extend this statistic to a statistical test of treelikeness using a parametric bootstrap. We use extensive simulations to compare tree proportion to a range of related approaches. We show that tree proportion successfully identifies non-treelikeness in a wide range of simulation scenarios, and discuss its strengths and weaknesses compared to other approaches. The power of the tree-proportion test to reject non-treelike alignments can be lower than some other approaches, but these approaches tend to be limited in their scope and/or the ease with which they can be interpreted. Our recommendation is to test treelikeness of sequence alignments with both tree proportion and mosaic methods such as 3Seq. The scripts necessary to replicate this study are available at https://github.com/caitlinch/treelikeness

Assessing the fit of the multi-species network coalescent to multi-locus data

2020 ◽  
Author(s):  
Ruoyi Cai ◽  
Cécile Ané
Keyword(s):  
Model Selection ◽  
Goodness Of Fit ◽  
Network Inference ◽  
Phylogenetic Network ◽  
Phylogenetic Networks ◽  
Supplementary Information ◽  
Goodness Of Fit Test ◽  
Full Likelihood ◽  
Genome Wide ◽  
Inference Methods

Abstract Motivation With growing genome-wide molecular datasets from next-generation sequencing, phylogenetic networks can be estimated using a variety of approaches. These phylogenetic networks include events like hybridization, gene flow or horizontal gene transfer explicitly. However, the most accurate network inference methods are computationally heavy. Methods that scale to larger datasets do not calculate a full likelihood, such that traditional likelihood-based tools for model selection are not applicable to decide how many past hybridization events best fit the data. We propose here a goodness-of-fit test to quantify the fit between data observed from genome-wide multi-locus data, and patterns expected under the multi-species coalescent model on a candidate phylogenetic network. Results We identified weaknesses in the previously proposed TICR test, and proposed corrections. The performance of our new test was validated by simulations on real-world phylogenetic networks. Our test provides one of the first rigorous tools for model selection, to select the adequate network complexity for the data at hand. The test can also work for identifying poorly inferred areas on a network. Availability and implementation Software for the goodness-of-fit test is available as a Julia package at https://github.com/cecileane/QuartetNetworkGoodnessFit.jl. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals A New Borrelia Species Defined by Multilocus Sequence Analysis of Housekeeping Genes

2009 ◽  
Vol 75 (16) ◽  
pp. 5410-5416 ◽  
Author(s):  
Gabriele Margos ◽  
Stephanie A. Vollmer ◽  
Muriel Cornet ◽  
Martine Garnier ◽  
Volker Fingerle ◽  
...  
Keyword(s):  
Sequence Analysis ◽  
Lyme Borreliosis ◽  
Phylogenetic Trees ◽  
Evolutionary History ◽  
Housekeeping Genes ◽  
Multilocus Sequence Analysis ◽  
Species Status ◽  
Borrelia Garinii ◽  
Analysis Scheme ◽  
History Of

ABSTRACT Analysis of Lyme borreliosis (LB) spirochetes, using a novel multilocus sequence analysis scheme, revealed that OspA serotype 4 strains (a rodent-associated ecotype) of Borrelia garinii were sufficiently genetically distinct from bird-associated B. garinii strains to deserve species status. We suggest that OspA serotype 4 strains be raised to species status and named Borrelia bavariensis sp. nov. The rooted phylogenetic trees provide novel insights into the evolutionary history of LB spirochetes.

scholarly journals THE NET-HMM APPROACH: PHYLOGENETIC NETWORK INFERENCE BY COMBINING MAXIMUM LIKELIHOOD AND HIDDEN MARKOV MODELS

2009 ◽  
Vol 07 (04) ◽  
pp. 625-644 ◽  
Author(s):  
SAGI SNIR ◽  
TAMIR TULLER
Keyword(s):  
Network Inference ◽  
Markov Models ◽  
Hidden Markov ◽  
Bacterial Genome ◽  
Genetic Material ◽  
Phylogenetic Network ◽  
Significance Test ◽  
Amino Acid Sequences ◽  
Phylogenetic Networks ◽  
Significant Mechanism

Horizontal gene transfer (HGT) is the event of transferring genetic material from one lineage in the evolutionary tree to a different lineage. HGT plays a major role in bacterial genome diversification and is a significant mechanism by which bacteria develop resistance to antibiotics. Although the prevailing assumption is of complete HGT, cases of partial HGT (which are also named chimeric HGT) where only part of a gene is horizontally transferred, have also been reported, albeit less frequently. In this work we suggest a new probabilistic model, the NET-HMM, for analyzing and modeling phylogenetic networks. This new model captures the biologically realistic assumption that neighboring sites of DNA or amino acid sequences are not independent, which increases the accuracy of the inference. The model describes the phylogenetic network as a Hidden Markov Model (HMM), where each hidden state is related to one of the network's trees. One of the advantages of the NET-HMM is its ability to infer partial HGT as well as complete HGT. We describe the properties of the NET-HMM, devise efficient algorithms for solving a set of problems related to it, and implement them in software. We also provide a novel complementary significance test for evaluating the fitness of a model (NET-HMM) to a given dataset. Using NET-HMM, we are able to answer interesting biological questions, such as inferring the length of partial HGT's and the affected nucleotides in the genomic sequences, as well as inferring the exact location of HGT events along the tree branches. These advantages are demonstrated through the analysis of synthetical inputs and three different biological inputs.

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