Use of Phylogenetic Network and Its Reconstruction Algorithms
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Reticulate Evolution and Phylogenetic Networks 6
CHAPTER Reticulate Evolution and Phylogenetic Networks 6 So far, we have modeled the evolutionary process as a bifurcating treelike process. The main feature of trees is that the flow of information in one lineage is independent of that in another lineage. In real life, however, many process- es—including recombination, hybridization, polyploidy, introgression, genome fusion, endosymbiosis, and horizontal gene transfer—do not abide by lineage independence and cannot be modeled by trees. Reticulate evolution refers to the origination of lineages through the com- plete or partial merging of two or more ancestor lineages. The nonvertical con- nections between lineages are referred to as reticulations. In this chapter, we introduce the language of networks and explain how networks can be used to study non-treelike phylogenetic processes. A substantial part of this chapter will be dedicated to the evolutionary relationships among prokaryotes and the very intriguing question of the origin of the eukaryotic cell. Networks Networks were briefly introduced in Chapter 4 in the context of protein-protein interactions. Here, we provide a more formal description of networks and their use in phylogenetics. As mentioned in Chapter 5, trees are connected graphs in which any two nodes are connected by a single path. In phylogenetics, a network is a connected graph in which at least two nodes are connected by two or more pathways. In other words, a network is a connected graph that is not a tree. All networks contain at least one cycle, i.e., a path of branches that can be followed in one direction from any starting point back to the starting point. -
Splits and Phylogenetic Networks
SplitsSplits andand PhylogeneticPhylogenetic NetworksNetworks DanielDaniel H.H. HusonHuson Paris, June 21, 2005 1 ContentsContents 1.1. PhylogeneticPhylogenetic treestrees 2.2. SplitsSplits networksnetworks 3.3. ConsensusConsensus networksnetworks 4.4. HybridizationHybridization andand reticulatereticulate networksnetworks 5.5. RecombinationRecombination networksnetworks 2 PhylogeneticPhylogenetic NetworksNetworks Bandelt (1991): Network displaying evolutionary relationships Other types of Splits Phylogenetic Reticulate phylogenetic networks trees networks networks Median Consensus Hybridization Recombination Augmented networks (super) networks networks trees networks Special case: “Galled trees” Any graph Ancestor Split decomposition, representing Split decomposition, recombination evolutionary Neighbor-net graphs data 3 PhylogeneticPhylogenetic NetworksNetworks 22 11 Other types of Splits Phylogenetic Reticulate phylogenetic networks trees networks networks 33 44 55 Median Consensus Hybridization Recombination Augmented networks (super) networks networks trees networks from sequences Special case: “Galled trees” from trees Any graph Ancestor Split decomposition, representing Split decomposition, recombination evolutionary Neighbor-net graphs data from distances 4 PhylogeneticPhylogenetic NetworksNetworksDan Gusfield: “Phylogenetic network” 22 11 “Generalized Other types of Splits Phylogenetic Reticulate phylogeneticphylogenetic networks trees networks network”networks 33 44 \ 55 Median Consensus Hybridization Recombination“More generalizedAugmented -
The Probability of Monophyly of a Sample of Gene Lineages on a Species Tree
PAPER The probability of monophyly of a sample of gene COLLOQUIUM lineages on a species tree Rohan S. Mehtaa,1, David Bryantb, and Noah A. Rosenberga aDepartment of Biology, Stanford University, Stanford, CA 94305; and bDepartment of Mathematics and Statistics, University of Otago, Dunedin 9054, New Zealand Edited by John C. Avise, University of California, Irvine, CA, and approved April 18, 2016 (received for review February 5, 2016) Monophyletic groups—groups that consist of all of the descendants loci that are reciprocally monophyletic is informative about the of a most recent common ancestor—arise naturally as a conse- time since species divergence and can assist in representing the quence of descent processes that result in meaningful distinctions level of differentiation between groups (4, 18). between organisms. Aspects of monophyly are therefore central to Many empirical investigations of genealogical phenomena have fields that examine and use genealogical descent. In particular, stud- made use of conceptual and statistical properties of monophyly ies in conservation genetics, phylogeography, population genetics, (19). Comparisons of observed monophyly levels to model pre- species delimitation, and systematics can all make use of mathemat- dictions have been used to provide information about species di- ical predictions under evolutionary models about features of mono- vergence times (20, 21). Model-based monophyly computations phyly. One important calculation, the probability that a set of gene have been used alongside DNA sequence differences between and lineages is monophyletic under a two-species neutral coalescent within proposed clades to argue for the existence of the clades model, has been used in many studies. Here, we extend this calcu- (22), and tests involving reciprocal monophyly have been used to lation for a species tree model that contains arbitrarily many species. -
Stochastic Models in Phylogenetic Comparative Methods: Analytical Properties and Parameter Estimation
Thesis for the Degree of Doctor of Philosophy Stochastic models in phylogenetic comparative methods: analytical properties and parameter estimation Krzysztof Bartoszek Division of Mathematical Statistics Department of Mathematical Sciences Chalmers University of Technology and University of Gothenburg G¨oteborg, Sweden 2013 Stochastic models in phylogenetic comparative methods: analytical properties and parameter estimation Krzysztof Bartoszek Copyright c Krzysztof Bartoszek, 2013. ISBN 978–91–628–8740–7 Department of Mathematical Sciences Division of Mathematical Statistics Chalmers University of Technology and University of Gothenburg SE-412 96 GOTEBORG,¨ Sweden Phone: +46 (0)31-772 10 00 Author e-mail: [email protected] Typeset with LATEX. Department of Mathematical Sciences Printed in G¨oteborg, Sweden 2013 Stochastic models in phylogenetic comparative methods: analytical properties and parameter estimation Krzysztof Bartoszek Abstract Phylogenetic comparative methods are well established tools for using inter–species variation to analyse phenotypic evolution and adaptation. They are generally hampered, however, by predominantly univariate ap- proaches and failure to include uncertainty and measurement error in the phylogeny as well as the measured traits. This thesis addresses all these three issues. First, by investigating the effects of correlated measurement errors on a phylogenetic regression. Second, by developing a multivariate Ornstein– Uhlenbeck model combined with a maximum–likelihood estimation pack- age in R. This model allows, uniquely, a direct way of testing adaptive coevolution. Third, accounting for the often substantial phylogenetic uncertainty in comparative studies requires an explicit model for the tree. Based on recently developed conditioned branching processes, with Brownian and Ornstein–Uhlenbeck evolution on top, expected species similarities are derived, together with phylogenetic confidence intervals for the optimal trait value. -
Application of Phylogenetic Networks in Evolutionary Studies
Application of Phylogenetic Networks in Evolutionary Studies Daniel H. Huson* and David Bryant *Center for Bioinformatics (ZBIT), Tu¨bingen University, Tu¨bingen, Germany; and Department of Mathematics, Auckland University, Auckland, New Zealand The evolutionary history of a set of taxa is usually represented by a phylogenetic tree, and this model has greatly facilitated the discussion and testing of hypotheses. However, it is well known that more complex evolutionary scenarios are poorly described by such models. Further, even when evolution proceeds in a tree-like manner, analysis of the data may not be best served by using methods that enforce a tree structure but rather by a richer visualization of the data to evaluate its properties, at least as an essential first step. Thus, phylogenetic networks should be employed when reticulate events such as hybridization, horizontal gene transfer, recombination, or gene duplication and loss are believed to be involved, and, even in the absence of such events, phylogenetic networks have a useful role to play. This article reviews the terminology used for phylogenetic networks and covers both split networks and reticulate networks, how they are defined, and how they can be interpreted. Additionally, the article outlines the beginnings of a comprehensive statistical framework for applying split network methods. We show how split networks can represent confidence sets of trees and introduce a conservative statistical test for whether the conflicting signal in a network is treelike. Finally, this article describes a new program, SplitsTree4, an interactive and comprehensive tool for inferring different types of phylogenetic networks from sequences, distances, and trees. Introduction The familiar evolutionary model assumes a tree, a Finally, we present our new program SplitsTree4, model that has greatly facilitated the discussion and testing which provides a comprehensive and interactive frame- of hypotheses. -
Tree Editing & Visualization
Tree Editing & Visualization Lisa Pokorny & Marina Marcet-Houben (with help from Miguel Ángel Naranjo Ortiz) Data Visualization VISUALIZATION SWEET SPOT “Strive to give your viewer the greatest number of useful ideas in the shortest time with the least ink in the smallest space” Tufte, E. The Visual Display of Quantitative Information (Graphic Press, Cheshire, Connecticut, USA, 2007). http://mkweb.bcgsc.ca/vizbi/2013/krzywinski-visual-design-principles-vizbi2013.pdf Data Visualization ● Satisfy your audience, not yourself ● Don’t merely display data, explain it ● Be aware of bias in evaluating effectiveness of visual forms ● Patterns are hard to see when variation is due to both data and formatting http://mkweb.bcgsc.ca/vizbi/2013/krzywinski-visual-design-principles-vizbi2013.pdf Data Visualization ● Know your message and stick to it (context musn’t dilute message). ● Choose effective encoding (to explore your data) and design (to communicate concepts). Data Visualization How do we get from data to visualization? http://mkweb.bcgsc.ca/vizbi/2013/krzywinski-visual-design-principles-vizbi2013.pdf Data Visualization How do we get from data to visualization? ● properties of the data / data type ○ Phylogenies (cladograms, phylograms, chronograms, cloudograms, etc.) ○ Networks (reticulograms, tanglegrams, etc.) ● properties of the image / visual encoding ○ What? Points, lines, labels… ○ Where? 2D, 3D(?) ○ How? Size, shape, texture, color, hue... ● the rules of mapping data to image ○ Principles of grouping ○ etc. Krygier & Wood. 2011. Making -
Species Delimitation and DNA Barcoding of Atlantic Ensis (Bivalvia, Pharidae)
Species delimitation and DNA barcoding of Atlantic Ensis (Bivalvia, Pharidae) Joaquín Viernaa,b, Joël Cuperusc, Andrés Martínez-Lagea, Jeroen M. Jansenc, Alejandra Perinaa,b, Hilde Van Peltc, Ana M. González-Tizóna a Evolutionary Biology Group (GIBE), Department of Molecular and Cell Biology,Universidade da Coruña, A Fraga 10, A Coruña, E-15008, Spain b AllGenetics, Edificio de Servicios Centrales de Investigación, Campus de Elviña s/n, A Coruña, E-15008, Spain c IMARES Wageningen UR, Ambachtsweg 8a, Den Helder, NL-1785 AJ, The Netherlands Zoologica Scripta Volume 43, Issue 2, pages 161–171, March 2014 Issue online: 17 February 2014, Version of record online: 3 September 2013, Manuscript Accepted: 11 August 2013, Manuscript Received: 17 May 2013. This is the peer reviewed version of the following article: Vierna, J., Cuperus, J., Martínez-Lage, A., Jansen, J.M., Perina, A., Van Pelt, H. & González-Tizón, A.M. (2013). Species delimitation and DNA barcoding of Atlantic Ensis (Bivalvia, Pharidae). Zoologica Scripta, 43, 161–171. which has been published in final form at https://doi.org/10.1111/zsc.12038. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving. Abstract Ensis Schumacher, 1817 razor shells occur at both sides of the Atlantic and along the Pacific coasts of tropical west America, Peru, and Chile. Many of them are marketed in various regions. However, the absence of clear autapomorphies in the shell and the sympatric distributions of some species often prevent a correct identification of specimens. As a consequence, populations cannot be properly managed, and edible species are almost always mislabelled along the production chain. -
Computational Methods for Phylogenetic Analysis
Computational Methods for Phylogenetic Analysis Student: Mohd Abdul Hai Zahid Supervisors: Dr. R. C. Joshi and Dr. Ankush Mittal Phylogenetics is the study of relationship among species or genes with the combination of molecular biology and mathematics. Most of the present phy- logenetic analysis softwares and algorithms have limitations of low accuracy, restricting assumptions on size of the dataset, high time complexity, complex results which are difficult to interpret and several others which inhibits their widespread use by the researchers. In this work, we address several problems of phylogenetic analysis and propose better methods addressing prominent issues. It is well known that the network representation of the evolutionary rela- tionship provides a better understanding of the evolutionary process and the non-tree like events such as horizontal gene transfer, hybridization, recom- bination and homoplasy. A pattern recognition based sequence alignment algorithm is proposed which not only employs the similarity of SNP sites, as is generally done, but also the dissimilarity for the classification of the nodes into mutation and recombination nodes. Unlike the existing algo- rithms [1, 2, 3, 4, 5, 6], the proposed algorithm [7] conducts a row-based search to detect the recombination nodes. The existing algorithms search the columns for the detection of recombination. The number of columns 1 in a sequence may be far greater than the rows, which results in increased complexity of the previous algorithms. Most of the individual researchers and research teams are concentrating on the evolutionary pathways of specific phylogenetic groups. Many effi- cient phylogenetic reconstruction methods, such as Maximum Parsimony [8] and Maximum Likelihood [9], are available. -
Towards the Development of Computational Tools for Evaluating Phylogenetic Network Reconstruction Methods
Towards the Development of Computational Tools for Evaluating Phylogenetic Network Reconstruction Methods L. Nakhleh, J. Sun, T. Warnow, C.R. Linder, B.M.E. Moret, A. Tholse Pacific Symposium on Biocomputing 8:315-326(2003) TOWARDS THE DEVELOPMENT OF COMPUTATIONAL TOOLS FOR EVALUATING PHYLOGENETIC NETWORK RECONSTRUCTION METHODS LUAY NAKHLEH, JERRY SUN, TANDY WARNOW Dept. of Computer Sciences, U. of Texas, Austin, TX 78712 C. RANDAL LINDER Section of Integrative Biology, U. of Texas, Austin, TX 78712 BERNARD M.E. MORET, ANNA THOLSE Dept. of Computer Science, U. of New Mexico, Albuquerque, NM 87131 We report on a suite of algorithms and techniques that together provide a simulation flow for study- ing the topological accuracy of methods for reconstructing phylogenetic networks. We imple- mented those algorithms and techniques and used three phylogenetic reconstruction methods for a case study of our tools. We present the results of our experimental studies in analyzing the relative performance of these methods. Our results indicate that our simulator and our proposed measure of accuracy, the latter an extension of the widely used Robinson-Foulds measure, offer a robust platform for the evaluation of network reconstruction algorithms. 1 Introduction Phylogenies, i.e., the evolutionary histories of groups of organisms, play a major role in representing the interrelationships among biological entities. Many methods for reconstructing such phylogenies have been proposed, but almost all of them assume that the underlying evolutionary history of a given set of species can be represented by a tree. While this model gives a satisfactory first-order approximation for many families of organisms, other families exhibit evolutionary mechanisms that cannot be represented by a tree. -
A Note on Encodings of Phylogenetic Networks of Bounded Level
A NOTE ON ENCODINGS OF PHYLOGENETIC NETWORKS OF BOUNDED LEVEL PHILIPPE GAMBETTE, KATHARINA T. HUBER Abstract. Driven by the need for better models that allow one to shed light into the question how life’s diversity has evolved, phylogenetic networks have now joined phylogenetic trees in the center of phylogenetics research. Like phylogenetic trees, such networks canonically induce collections of phyloge- netic trees, clusters, and triplets, respectively. Thus it is not surprising that many network approaches aim to reconstruct a phylogenetic network from such collections. Related to the well-studied perfect phylogeny problem, the follow- ing question is of fundamental importance in this context: When does one of the above collections encode (i.e. uniquely describe) the network that induces it? In this note, we present a complete answer to this question for the special case of a level-1 (phylogenetic) network by characterizing those level-1 net- works for which an encoding in terms of one (or equivalently all) of the above collections exists. Given that this type of network forms the first layer of the rich hierarchy of level-k networks, k a non-negative integer, it is natural to wonder whether our arguments could be extended to members of that hierar- chy for higher values for k. By giving examples, we show that this is not the case. Keywords: Phylogeny, phylogenetic networks, triplets, clusters, supernet- work, level-1 network, perfect phylogeny problem. 1. Introduction An improved understanding of the complex processes that drive evolution has arXiv:0906.4324v1 [math.CO] 23 Jun 2009 lent support to the idea that reticulate evolutionary events such as lateral gene transfer or hybridization are more common than originally thought rendering a phylogenetic tree (essentially a rooted leaf labelled graph-theoretical tree) too sim- plistic a model to fully understand the complex processes that drive evolution. -
Anti-Consensus: Detecting Trees That Have an Evolutionary Signal That Is Lost in Consensus
bioRxiv preprint doi: https://doi.org/10.1101/706416; this version posted July 29, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license. Anti-consensus: detecting trees that have an evolutionary signal that is lost in consensus Daniel H. Huson1 ∗, Benjamin Albrecht1, Sascha Patz1 and Mike Steel2 1 Institute for Bioinformatics and Medical Informatics, University of T¨ubingen,Germany 2 Biomathematics Research Centre, University of Canterbury, Christchurch, New Zealand *Corresponding author, [email protected] Abstract In phylogenetics, a set of gene trees is often summarized by a consensus tree, such as the majority consensus, which is based on the set of all splits that are present in more than 50% of the input trees. A \consensus network" is obtained by lowering the threshold and considering all splits that are contained in 10% of the trees, say, and then computing the corresponding splits network. By construction and in practice, a consensus network usually shows the majority tree, extended by a number of rectangles that represent local rearrangements around internal nodes of the consensus tree. This may lead to the false conclusion that the input trees do not differ in a significant way because \even a phylogenetic network" does not display any large discrepancies. To harness the full potential of a phylogenetic network, we introduce the new concept of an anti-consensus network that aims at representing the largest interesting discrepancies found in a set of gene trees. -
User Manual for Splitstree4 V4.6
User Manual for SplitsTree4 V4.6 Daniel H. Huson and David Bryant August 4, 2006 Contents Contents 1 1 Introduction 4 2 Getting Started 5 3 Obtaining and Installing the Program 5 4 Program Overview 6 5 Splits, Trees and Networks 7 6 Opening, Reading and Writing Files 10 7 Estimating Distances 10 1 8 Building and Processing Trees 11 9 Building and Drawing Networks 11 10 Main Window 12 10.1 Network Tab ........................................ 13 10.2 Data Tab .......................................... 13 10.3 Source Tab ......................................... 13 10.4 Tool bar ........................................... 13 11 Graphical Interaction with the Network 13 12 Main Menus 14 12.1 File Menu .......................................... 14 12.2 Edit Menu .......................................... 16 12.3 View Menu ......................................... 16 12.4 Data Menu ......................................... 17 12.5 Distances Menu ....................................... 19 12.6 Trees Menu ......................................... 19 12.7 Network Menu ....................................... 20 12.8 Analysis Menu ....................................... 21 12.9 Draw Menu ......................................... 21 12.10Window Menu ....................................... 22 12.11Configuring Methods .................................... 23 13 Popup Menus 23 14 Tool Bar 24 15 Pipeline Window 24 15.1 Taxa Tab .......................................... 25 15.2 Unaligned Tab ....................................... 25 15.3 Characters Tab