scholarly journals Biochemical and phylogenetic networks-II: X-trees and phylogenetic trees

2021 ◽  
Vol 59 (3) ◽  
pp. 699-718
Author(s):  
R. Sundara Rajan ◽  
A. Arul Shantrinal ◽  
K. Jagadeesh Kumar ◽  
T. M. Rajalaxmi ◽  
Indra Rajasingh ◽  
...  
Keyword(s):  
Phylogenetic Trees ◽  
Phylogenetic Networks

scholarly journals IcyTree: Rapid browser-based visualization for phylogenetic trees and networks

2017 ◽  
Author(s):  
Timothy G. Vaughan
Keyword(s):  
Web Application ◽  
Phylogenetic Trees ◽  
Phylogenetic Networks ◽  
Web Browsers ◽  
Network Access ◽  
Online Tool ◽  
Link Type ◽  
Mozilla Firefox ◽  
Ancestral Recombination Graphs ◽  
Client Side

AbstractSummaryIcyTree is an easy-to-use application which can be used to visualize a wide variety of phylogenetic trees and networks. While numerous phylogenetic tree viewers exist already, IcyTree distinguishes itself by being a purely online tool, having a responsive user interface, supporting phylogenetic networks (ancestral recombination graphs in particular), and efficiently drawing trees that include information such as ancestral locations or trait values. IcyTree also provides intuitive panning and zooming utilities that make exploring large phylogenetic trees of many thousands of taxa feasible.Availability and ImplementationIcyTree is a web application and can be accessed directly at http://tgvaughan.github.com/icytree. Currently-supported web browsers include Mozilla Firefox and Google Chrome. IcyTree is written entirely in client-side JavaScript (no plugin required) and, once loaded, does not require network access to run. IcyTree is free software, and the source code is made available at http://github.com/tgvaughan/icytree under version 3 of the GNU General Public [email protected]

scholarly journals QUARTETS AND UNROOTED PHYLOGENETIC NETWORKS

2012 ◽  
Vol 10 (04) ◽  
pp. 1250004 ◽  
Author(s):  
PHILIPPE GAMBETTE ◽  
VINCENT BERRY ◽  
CHRISTOPHE PAUL
Keyword(s):  
Phylogenetic Trees ◽  
Genetic Material ◽  
Special Kind ◽  
Equivalence Theorem ◽  
Phylogenetic Networks ◽  
Combinatorial Properties ◽  
Split Systems ◽  
Level 1 ◽  
Coexisting Species ◽  
Split Networks

Phylogenetic networks were introduced to describe evolution in the presence of exchanges of genetic material between coexisting species or individuals. Split networks in particular were introduced as a special kind of abstract network to visualize conflicts between phylogenetic trees which may correspond to such exchanges. More recently, methods were designed to reconstruct explicit phylogenetic networks (whose vertices can be interpreted as biological events) from triplet data. In this article, we link abstract and explicit networks through their combinatorial properties, by introducing the unrooted analog of level-k networks. In particular, we give an equivalence theorem between circular split systems and unrooted level-1 networks. We also show how to adapt to quartets some existing results on triplets, in order to reconstruct unrooted level-k phylogenetic networks. These results give an interesting perspective on the combinatorics of phylogenetic networks and also raise algorithmic and combinatorial questions.

scholarly journals UNIQUENESS, INTRACTABILITY AND EXACT ALGORITHMS: REFLECTIONS ON LEVEL-K PHYLOGENETIC NETWORKS

2009 ◽  
Vol 07 (04) ◽  
pp. 597-623 ◽  
Author(s):  
LEO VAN IERSEL ◽  
STEVEN KELK ◽  
MATTHIAS MNICH
Keyword(s):  
Gene Transfer ◽  
Horizontal Gene Transfer ◽  
Phylogenetic Trees ◽  
Exact Algorithm ◽  
Exact Algorithms ◽  
Phylogenetic Networks ◽  
Np Hard ◽  
Level 1

Phylogenetic networks provide a way to describe and visualize evolutionary histories that have undergone so-called reticulate evolutionary events such as recombination, hybridization or horizontal gene transfer. The level k of a network determines how non-treelike the evolution can be, with level-0 networks being trees. We study the problem of constructing level-k phylogenetic networks from triplets, i.e. phylogenetic trees for three leaves (taxa). We give, for each k, a level-k network that is uniquely defined by its triplets. We demonstrate the applicability of this result by using it to prove that (1) for all k ≥ 1 it is NP-hard to construct a level-k network consistent with all input triplets, and (2) for all k ≥ 0 it is NP-hard to construct a level-k network consistent with a maximum number of input triplets, even when the input is dense. As a response to this intractability, we give an exact algorithm for constructing level-1 networks consistent with a maximum number of input triplets.

scholarly journals A Metric on the Space of Partly Reduced Phylogenetic Networks

2016 ◽  
Vol 2016 ◽  
pp. 1-9 ◽  
Author(s):  
Juan Wang
Keyword(s):  
Gene Transfer ◽  
Horizontal Gene Transfer ◽  
Polynomial Time ◽  
Phylogenetic Trees ◽  
Special Kind ◽  
Population Level ◽  
Phylogenetic Networks

Phylogenetic networks are a generalization of phylogenetic trees that allow for the representation of evolutionary events acting at the population level, such as recombination between genes, hybridization between lineages, and horizontal gene transfer. The researchers have designed several measures for computing the dissimilarity between two phylogenetic networks, and each measure has been proven to be a metric on a special kind of phylogenetic networks. However, none of the existing measures is a metric on the space of partly reduced phylogenetic networks. In this paper, we provide a metric,de-distance, on the space of partly reduced phylogenetic networks, which is polynomial-time computable.

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 Tree-Based Unrooted Phylogenetic Networks

2017 ◽  
Vol 80 (2) ◽  
pp. 404-416 ◽  
Author(s):  
A. Francis ◽  
K. T. Huber ◽  
V. Moulton
Keyword(s):  
Gene Transfer ◽  
Horizontal Gene Transfer ◽  
Phylogenetic Tree ◽  
Phylogenetic Trees ◽  
Phylogenetic Network ◽  
Simple Graph ◽  
Phylogenetic Networks ◽  
Underlying Graph ◽  
Finite Set ◽  
Computational Properties

Abstract Phylogenetic networks are a generalization of phylogenetic trees that are used to represent non-tree-like evolutionary histories that arise in organisms such as plants and bacteria, or uncertainty in evolutionary histories. An unrooted phylogenetic network on a non-empty, finite set X of taxa, or network, is a connected, simple graph in which every vertex has degree 1 or 3 and whose leaf set is X. It is called a phylogenetic tree if the underlying graph is a tree. In this paper we consider properties of tree-based networks, that is, networks that can be constructed by adding edges into a phylogenetic tree. We show that although they have some properties in common with their rooted analogues which have recently drawn much attention in the literature, they have some striking differences in terms of both their structural and computational properties. We expect that our results could eventually have applications to, for example, detecting horizontal gene transfer or hybridization which are important factors in the evolution of many organisms.

scholarly journals Constructing Phylogenetic Networks Based on the Isomorphism of Datasets

2016 ◽  
Vol 2016 ◽  
pp. 1-7
Author(s):  
Juan Wang ◽  
Zhibin Zhang ◽  
Yanjuan Li
Keyword(s):  
Molecular Evolution ◽  
Phylogenetic Trees ◽  
Phylogenetic Network ◽  
Phylogenetic Networks ◽  
The Relationship

Constructing rooted phylogenetic networks from rooted phylogenetic trees has become an important problem in molecular evolution. So far, many methods have been presented in this area, in which most efficient methods are based on the incompatible graph, such as the CASS, the LNETWORK,and the BIMLR. This paper will research the commonness of the methods based on the incompatible graph, the relationship between incompatible graph and the phylogenetic network, and the topologies of incompatible graphs. We can find out all the simplest datasets for a topologyGand construct a network for every dataset. For any one datasetC, we can compute a network from the network representing the simplest dataset which is isomorphic toC. This process will save more time for the algorithms when constructing networks.

scholarly journals Generating normal networks via leaf insertion and nearest neighbor interchange

2019 ◽  
Vol 20 (S20) ◽  
Author(s):  
Louxin Zhang
Keyword(s):  
Population Genetics ◽  
Phylogenetic Trees ◽  
Nearest Neighbor ◽  
Structural Condition ◽  
Phylogenetic Networks ◽  
Topological Structures ◽  
Theoretical Population ◽  
Theoretical Population Genetics ◽  
Normal Networks ◽  
Leaf Insertion

Abstract Background Galled trees are studied as a recombination model in theoretical population genetics. This class of phylogenetic networks has been generalized to tree-child networks and other network classes by relaxing a structural condition imposed on galled trees. Although these networks are simple, their topological structures have yet to be fully understood. Results It is well-known that all phylogenetic trees on n taxa can be generated by the insertion of the n-th taxa to each edge of all the phylogenetic trees on n−1 taxa. We prove that all tree-child (resp. normal) networks with k reticulate nodes on n taxa can be uniquely generated via three operations from all the tree-child (resp. normal) networks with k−1 or k reticulate nodes on n−1 taxa. Applying this result to counting rooted phylogenetic networks, we show that there are exactly $\frac {(2n)!}{2^{n} (n-1)!}-2^{n-1} n!$(2n)!2n(n−1)!−2n−1n! binary phylogenetic networks with one reticulate node on n taxa. Conclusions The work makes two contributions to understand normal networks. One is a generalization of an enumeration procedure for phylogenetic trees into one for normal networks. Another is simple formulas for counting normal networks and phylogenetic networks that have only one reticulate node.

scholarly journals Networks uncover hidden lexical borrowing in Indo-European language evolution

2010 ◽  
Vol 278 (1713) ◽  
pp. 1794-1803 ◽  
Author(s):  
Shijulal Nelson-Sathi ◽  
Johann-Mattis List ◽  
Hans Geisler ◽  
Heiner Fangerau ◽  
Russell D. Gray ◽  
...  
Keyword(s):  
Phylogenetic Trees ◽  
English Language ◽  
Language Evolution ◽  
Old French ◽  
Phylogenetic Networks ◽  
European Language ◽  
Lexical Borrowing ◽  
European Languages ◽  
Basic Vocabulary ◽  
The Impact

Language evolution is traditionally described in terms of family trees with ancestral languages splitting into descendent languages. However, it has long been recognized that language evolution also entails horizontal components, most commonly through lexical borrowing. For example, the English language was heavily influenced by Old Norse and Old French; eight per cent of its basic vocabulary is borrowed. Borrowing is a distinctly non-tree-like process—akin to horizontal gene transfer in genome evolution—that cannot be recovered by phylogenetic trees. Here, we infer the frequency of hidden borrowing among 2346 cognates (etymologically related words) of basic vocabulary distributed across 84 Indo-European languages. The dataset includes 124 (5%) known borrowings. Applying the uniformitarian principle to inventory dynamics in past and present basic vocabularies, we find that 1373 (61%) of the cognates have been affected by borrowing during their history. Our approach correctly identified 117 (94%) known borrowings. Reconstructed phylogenetic networks that capture both vertical and horizontal components of evolutionary history reveal that, on average, eight per cent of the words of basic vocabulary in each Indo-European language were involved in borrowing during evolution. Basic vocabulary is often assumed to be relatively resistant to borrowing. Our results indicate that the impact of borrowing is far more widespread than previously thought.

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.

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