scholarly journals On the Subnet Prune and Regraft Distance

10.37236/7860 ◽  
2019 ◽  
Vol 26 (2) ◽  
Author(s):  
Jonathan Klawitter ◽  
Simone Linz
Keyword(s):  
Gene Transfer ◽  
Horizontal Gene Transfer ◽  
Phylogenetic Tree ◽  
Phylogenetic Network ◽  
Directed Acyclic Graphs ◽  
Phylogenetic Networks ◽  
Evolutionary Relationships ◽  
Acyclic Graphs ◽  
Subtree Prune And Regraft

Phylogenetic networks are rooted directed acyclic graphs that represent evolutionary relationships between species whose past includes reticulation events such as hybridisation and horizontal gene transfer. To search the space of phylogenetic networks, the popular tree rearrangement operation rooted subtree prune and regraft (rSPR) was recently generalised to phylogenetic networks. This new operation – called subnet prune and regraft (SNPR) – induces a metric on the space of all phylogenetic networks as well as on several widely-used network classes. In this paper, we investigate several problems that arise in the context of computing the SNPR-distance. For a phylogenetic tree $T$ and a phylogenetic network $N$, we show how this distance can be computed by considering the set of trees that are embedded in $N$ and then use this result to characterise the SNPR-distance between $T$ and $N$ in terms of agreement forests. Furthermore, we analyse properties of shortest SNPR-sequences between two phylogenetic networks $N$ and $N'$, and answer the question whether or not any of the classes of tree-child, reticulation-visible, or tree-based networks isometrically embeds into the class of all phylogenetic networks under SNPR.

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 Display Sets of Normal and Tree-Child Networks

10.37236/9128 ◽  
2021 ◽  
Vol 28 (1) ◽  
Author(s):  
Janosch Döcker ◽  
Simone Linz ◽  
Charles Semple
Keyword(s):  
Decision Problem ◽  
Phylogenetic Trees ◽  
Phylogenetic Network ◽  
Polynomial Time Algorithm ◽  
Time Algorithm ◽  
Directed Acyclic Graphs ◽  
Phylogenetic Networks ◽  
Acyclic Graphs ◽  
Normal Network ◽  
Normal Networks

Phylogenetic networks are leaf-labelled directed acyclic graphs that are used in computational biology to analyse and represent the evolutionary relationships of a set of species or viruses. In contrast to phylogenetic trees, phylogenetic networks have vertices of in-degree at least two that represent reticulation events such as hybridisation, lateral gene transfer, or reassortment. By systematically deleting various combinations of arcs in a phylogenetic network $\mathcal N$, one derives a set of phylogenetic trees that are embedded in $\mathcal N$. We recently showed that the problem of deciding if two binary phylogenetic networks embed the same set of phylogenetic trees is computationally hard, in particular, we showed it to be $\Pi^P_2$-complete. In this paper, we establish a polynomial-time algorithm for this decision problem if the initial two networks consist of a normal network and a tree-child network; two well-studied topologically restricted subclasses of phylogenetic networks, with normal networks being more structurally constrained than tree-child networks. The running time of the algorithm is quadratic in the size of the leaf sets.

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.

A review of metrics measuring dissimilarity for rooted phylogenetic networks

2018 ◽  
Vol 20 (6) ◽  
pp. 1972-1980 ◽  
Author(s):  
Juan Wang ◽  
Maozu Guo
Keyword(s):  
Polynomial Time ◽  
Phylogenetic Network ◽  
Phylogenetic Networks ◽  
Evolutionary Relationships ◽  
Comprehensive Review ◽  
Phylogenic Analysis ◽  
The Past ◽  
Important Structure

Abstract A rooted phylogenetic network is an important structure in the description of evolutionary relationships. Computing the distance (topological dissimilarity) between two rooted phylogenetic networks is a fundamental in phylogenic analysis. During the past few decades, several polynomial-time computable metrics have been described. Here, we give a comprehensive review and analysis on those metrics, including the correlation among metrics and the distribution of distance values computed by each metric. Moreover, we describe the software and website, CDRPN (Computing Distance for Rooted Phylogenetic Networks), for measuring the topological dissimilarity between rooted phylogenetic networks. Availability http://bioinformatics.imu.edu.cn/distance/ Contact [email protected]

scholarly journals HGT-Gen: a tool for generating a phylogenetic tree with horizontal gene transfer

2011 ◽  
Vol 7 (5) ◽  
pp. 211-213 ◽  
Author(s):  
Tokumasa Horiike ◽  
Daisuke Miyata ◽  
Yoshio Tateno ◽  
Ryoichi Minai
Keyword(s):  
Gene Transfer ◽  
Horizontal Gene Transfer ◽  
Phylogenetic Tree

Transfer index, NetUniFrac and some useful shortest path-based distances for community analysis in sequence similarity networks

2020 ◽  
Vol 36 (9) ◽  
pp. 2740-2749
Author(s):  
Henry Xing ◽  
Steven W Kembel ◽  
Vladimir Makarenkov
Keyword(s):  
Gene Transfer ◽  
Horizontal Gene Transfer ◽  
Phylogenetic Tree ◽  
Shortest Path ◽  
Sequence Similarity ◽  
Community Analysis ◽  
Supplementary Information ◽  
Similarity Networks ◽  
Transfer Index ◽  
Sequence Similarity Networks

Abstract Motivation Phylogenetic trees and the methods for their analysis have played a key role in many evolutionary, ecological and bioinformatics studies. Alternatively, phylogenetic networks have been widely used to analyze and represent complex reticulate evolutionary processes which cannot be adequately studied using traditional phylogenetic methods. These processes include, among others, hybridization, horizontal gene transfer, and genetic recombination. Nowadays, sequence similarity and genome similarity networks have become an efficient tool for community analysis of large molecular datasets in comparative studies. These networks can be used for tackling a variety of complex evolutionary problems such as the identification of horizontal gene transfer events, the recovery of mosaic genes and genomes, and the study of holobionts. Results The shortest path in a phylogenetic tree is used to estimate evolutionary distances between species. We show how the shortest path concept can be extended to sequence similarity networks by defining five new distances, NetUniFrac, Spp, Spep, Spelp and Spinp, and the Transfer index, between species communities present in the network. These new distances can be seen as network analogs of the traditional UniFrac distance used to assess dissimilarity between species communities in a phylogenetic tree, whereas the Transfer index is intended for estimating the rate and direction of gene transfers, or species dispersal, between different phylogenetic, or ecological, species communities. Moreover, NetUniFrac and the Transfer index can be computed in linear time with respect to the number of edges in the network. We show how these new measures can be used to analyze microbiota and antibiotic resistance gene similarity networks. Availability and implementation Our NetFrac program, implemented in R and C, along with its source code, is freely available on Github at the following URL address: https://github.com/XPHenry/Netfrac. Supplementary information Supplementary data are available at Bioinformatics online.

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 MonoPhy: a simple R package to find and visualize monophyly issues

2016 ◽  
Vol 2 ◽  
pp. e56 ◽  
Author(s):  
Orlando Schwery ◽  
Brian C. O’Meara
Keyword(s):  
Gene Transfer ◽  
Horizontal Gene Transfer ◽  
Phylogenetic Tree ◽  
Incomplete Lineage Sorting ◽  
R Package ◽  
Higher Order ◽  
Input File ◽  
Lineage Sorting ◽  
Additional Input

Background.The monophyly of taxa is an important attribute of a phylogenetic tree. A lack of it may hint at shortcomings of either the tree or the current taxonomy, or can indicate cases of incomplete lineage sorting or horizontal gene transfer. Whichever is the reason, a lack of monophyly can misguide subsequent analyses. While monophyly is conceptually simple, it is manually tedious and time consuming to assess on modern phylogenies of hundreds to thousands of species.Results.The R packageMonoPhyallows assessment and exploration of monophyly of taxa in a phylogeny. It can assess the monophyly of genera using the phylogeny only, and with an additional input file any other desired higher order taxa or unranked groups can be checked as well.Conclusion.Summary tables, easily subsettable results and several visualization options allow quick and convenient exploration of monophyly issues, thus makingMonoPhya valuable tool for any researcher working with phylogenies.

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