Molecular Phylogenetics
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Selecting Molecular Markers for a Specific Phylogenetic Problem
MOJ Proteomics & Bioinformatics Review Article Open Access Selecting molecular markers for a specific phylogenetic problem Abstract Volume 6 Issue 3 - 2017 In a molecular phylogenetic analysis, different markers may yield contradictory Claudia AM Russo, Bárbara Aguiar, Alexandre topologies for the same diversity group. Therefore, it is important to select suitable markers for a reliable topological estimate. Issues such as length and rate of evolution P Selvatti Department of Genetics, Federal University of Rio de Janeiro, will play a role in the suitability of a particular molecular marker to unfold the Brazil phylogenetic relationships for a given set of taxa. In this review, we provide guidelines that will be useful to newcomers to the field of molecular phylogenetics weighing the Correspondence: Claudia AM Russo, Molecular Biodiversity suitability of molecular markers for a given phylogenetic problem. Laboratory, Department of Genetics, Institute of Biology, Block A, CCS, Federal University of Rio de Janeiro, Fundão Island, Keywords: phylogenetic trees, guideline, suitable genes, phylogenetics Rio de Janeiro, RJ, 21941-590, Brazil, Tel 21 991042148, Fax 21 39386397, Email [email protected] Received: March 14, 2017 | Published: November 03, 2017 Introduction concept that occupies a central position in evolutionary biology.15 Homology is a qualitative term, defined by equivalence of parts due Over the last three decades, the scientific field of molecular to inherited common origin.16–18 biology has experienced remarkable advancements -
Lecture Notes: the Mathematics of Phylogenetics
Lecture Notes: The Mathematics of Phylogenetics Elizabeth S. Allman, John A. Rhodes IAS/Park City Mathematics Institute June-July, 2005 University of Alaska Fairbanks Spring 2009, 2012, 2016 c 2005, Elizabeth S. Allman and John A. Rhodes ii Contents 1 Sequences and Molecular Evolution 3 1.1 DNA structure . .4 1.2 Mutations . .5 1.3 Aligned Orthologous Sequences . .7 2 Combinatorics of Trees I 9 2.1 Graphs and Trees . .9 2.2 Counting Binary Trees . 14 2.3 Metric Trees . 15 2.4 Ultrametric Trees and Molecular Clocks . 17 2.5 Rooting Trees with Outgroups . 18 2.6 Newick Notation . 19 2.7 Exercises . 20 3 Parsimony 25 3.1 The Parsimony Criterion . 25 3.2 The Fitch-Hartigan Algorithm . 28 3.3 Informative Characters . 33 3.4 Complexity . 35 3.5 Weighted Parsimony . 36 3.6 Recovering Minimal Extensions . 38 3.7 Further Issues . 39 3.8 Exercises . 40 4 Combinatorics of Trees II 45 4.1 Splits and Clades . 45 4.2 Refinements and Consensus Trees . 49 4.3 Quartets . 52 4.4 Supertrees . 53 4.5 Final Comments . 54 4.6 Exercises . 55 iii iv CONTENTS 5 Distance Methods 57 5.1 Dissimilarity Measures . 57 5.2 An Algorithmic Construction: UPGMA . 60 5.3 Unequal Branch Lengths . 62 5.4 The Four-point Condition . 66 5.5 The Neighbor Joining Algorithm . 70 5.6 Additional Comments . 72 5.7 Exercises . 73 6 Probabilistic Models of DNA Mutation 81 6.1 A first example . 81 6.2 Markov Models on Trees . 87 6.3 Jukes-Cantor and Kimura Models . -
Investgating Determinants of Phylogeneic Accuracy
IMPACT OF MOLECULAR EVOLUTIONARY FOOTPRINTS ON PHYLOGENETIC ACCURACY – A SIMULATION STUDY Dissertation Submitted to The College of Arts and Sciences of the UNIVERSITY OF DAYTON In Partial Fulfillment of the Requirements for The Degree Doctor of Philosophy in Biology by Bhakti Dwivedi UNIVERSITY OF DAYTON August, 2009 i APPROVED BY: _________________________ Gadagkar, R. Sudhindra Ph.D. Major Advisor _________________________ Robinson, Jayne Ph.D. Committee Member Chair Department of Biology _________________________ Nielsen, R. Mark Ph.D. Committee Member _________________________ Rowe, J. John Ph.D. Committee Member _________________________ Goldman, Dan Ph.D. Committee Member ii ABSTRACT IMPACT OF MOLECULAR EVOLUTIONARY FOOTPRINTS ON PHYLOGENETIC ACCURACY – A SIMULATION STUDY Dwivedi Bhakti University of Dayton Advisor: Dr. Sudhindra R. Gadagkar An accurately inferred phylogeny is important to the study of molecular evolution. Factors impacting the accuracy of a phylogenetic tree can be traced to several consecutive steps leading to the inference of the phylogeny. In this simulation-based study our focus is on the impact of the certain evolutionary features of the nucleotide sequences themselves in the alignment rather than any source of error during the process of sequence alignment or due to the choice of the method of phylogenetic inference. Nucleotide sequences can be characterized by summary statistics such as sequence length and base composition. When two or more such sequences need to be compared to each other (as in an alignment prior to phylogenetic analysis) additional evolutionary features come into play, such as the overall rate of nucleotide substitution, the ratio of two specific instantaneous, rates of substitution (rate at which transitions and transversions occur), and the shape parameter, of the gamma distribution (that quantifies the extent of iii heterogeneity in substitution rate among sites in an alignment). -
An Automated Pipeline for Retrieving Orthologous DNA Sequences from Genbank in R
life Technical Note phylotaR: An Automated Pipeline for Retrieving Orthologous DNA Sequences from GenBank in R Dominic J. Bennett 1,2,* ID , Hannes Hettling 3, Daniele Silvestro 1,2, Alexander Zizka 1,2, Christine D. Bacon 1,2, Søren Faurby 1,2, Rutger A. Vos 3 ID and Alexandre Antonelli 1,2,4,5 ID 1 Gothenburg Global Biodiversity Centre, Box 461, SE-405 30 Gothenburg, Sweden; [email protected] (D.S.); [email protected] (A.Z.); [email protected] (C.D.B.); [email protected] (S.F.); [email protected] (A.A.) 2 Department of Biological and Environmental Sciences, University of Gothenburg, Box 461, SE-405 30 Gothenburg, Sweden 3 Naturalis Biodiversity Center, P.O. Box 9517, 2300 RA Leiden, The Netherlands; [email protected] (H.H.); [email protected] (R.A.V.) 4 Gothenburg Botanical Garden, Carl Skottsbergsgata 22A, SE-413 19 Gothenburg, Sweden 5 Department of Organismic and Evolutionary Biology, Harvard University, 26 Oxford St., Cambridge, MA 02138 USA * Correspondence: [email protected] Received: 28 March 2018; Accepted: 1 June 2018; Published: 5 June 2018 Abstract: The exceptional increase in molecular DNA sequence data in open repositories is mirrored by an ever-growing interest among evolutionary biologists to harvest and use those data for phylogenetic inference. Many quality issues, however, are known and the sheer amount and complexity of data available can pose considerable barriers to their usefulness. A key issue in this domain is the high frequency of sequence mislabeling encountered when searching for suitable sequences for phylogenetic analysis. -
Phylogeny Inference Based on Parsimony and Other Methods Using Paup*
8 Phylogeny inference based on parsimony and other methods using Paup* THEORY David L. Swofford and Jack Sullivan 8.1 Introduction Methods for inferring evolutionary trees can be divided into two broad categories: thosethatoperateonamatrixofdiscretecharactersthatassignsoneormore attributes or character states to each taxon (i.e. sequence or gene-family member); and those that operate on a matrix of pairwise distances between taxa, with each distance representing an estimate of the amount of divergence between two taxa since they last shared a common ancestor (see Chapter 1). The most commonly employed discrete-character methods used in molecular phylogenetics are parsi- mony and maximum likelihood methods. For molecular data, the character-state matrix is typically an aligned set of DNA or protein sequences, in which the states are the nucleotides A, C, G, and T (i.e. DNA sequences) or symbols representing the 20 common amino acids (i.e. protein sequences); however, other forms of discrete data such as restriction-site presence/absence and gene-order information also may be used. Parsimony, maximum likelihood, and some distance methods are examples of a broader class of phylogenetic methods that rely on the use of optimality criteria. Methods in this class all operate by explicitly defining an objective function that returns a score for any input tree topology. This tree score thus allows any two or more trees to be ranked according to the chosen optimality criterion. Ordinarily, phylogenetic inference under criterion-based methods couples the selection of The Phylogenetic Handbook: a Practical Approach to Phylogenetic Analysis and Hypothesis Testing, Philippe Lemey, Marco Salemi, and Anne-Mieke Vandamme (eds.). -
In Defence of the Three-Domains of Life Paradigm P.T.S
van der Gulik et al. BMC Evolutionary Biology (2017) 17:218 DOI 10.1186/s12862-017-1059-z REVIEW Open Access In defence of the three-domains of life paradigm P.T.S. van der Gulik1*, W.D. Hoff2 and D. Speijer3* Abstract Background: Recently, important discoveries regarding the archaeon that functioned as the “host” in the merger with a bacterium that led to the eukaryotes, its “complex” nature, and its phylogenetic relationship to eukaryotes, have been reported. Based on these new insights proposals have been put forward to get rid of the three-domain Model of life, and replace it with a two-domain model. Results: We present arguments (both regarding timing, complexity, and chemical nature of specific evolutionary processes, as well as regarding genetic structure) to resist such proposals. The three-domain Model represents an accurate description of the differences at the most fundamental level of living organisms, as the eukaryotic lineage that arose from this unique merging event is distinct from both Archaea and Bacteria in a myriad of crucial ways. Conclusions: We maintain that “a natural system of organisms”, as proposed when the three-domain Model of life was introduced, should not be revised when considering the recent discoveries, however exciting they may be. Keywords: Eucarya, LECA, Phylogenetics, Eukaryogenesis, Three-domain model Background (English: domain) as a higher taxonomic level than The discovery that methanogenic microbes differ funda- regnum: introducing the three domains of cellular life, Ar- mentally from Bacteria such as Escherichia coli or Bacillus chaea, Bacteria and Eucarya. This paradigm was proposed subtilis constitutes one of the most important biological by Woese, Kandler and Wheelis in PNAS in 1990 [2]. -
Phylogenetics Topic 2: Phylogenetic and Genealogical Homology
Phylogenetics Topic 2: Phylogenetic and genealogical homology Phylogenies distinguish homology from similarity Previously, we examined how rooted phylogenies provide a framework for distinguishing similarity due to common ancestry (HOMOLOGY) from non-phylogenetic similarity (ANALOGY). Here we extend the concept of phylogenetic homology by making a further distinction between a HOMOLOGOUS CHARACTER and a HOMOLOGOUS CHARACTER STATE. This distinction is important to molecular evolution, as we often deal with data comprised of homologous characters with non-homologous character states. The figure below shows three hypothetical protein-coding nucleotide sequences (for simplicity, only three codons long) that are related to each other according to a phylogenetic tree. In the figure the nucleotide sequences are aligned to each other; in so doing we are making the implicit assumption that the characters aligned vertically are homologous characters. In the specific case of nucleotide and amino acid alignments this assumption is called POSITIONAL HOMOLOGY. Under positional homology it is implicit that a given position, say the first position in the gene sequence, was the same in the gene sequence of the common ancestor. In the figure below it is clear that some positions do not have identical character states (see red characters in figure below). In such a case the involved position is considered to be a homologous character, while the state of that character will be non-homologous where there are differences. Phylogenetic perspective on homologous characters and homologous character states ACG TAC TAA SYNAPOMORPHY: a shared derived character state in C two or more lineages. ACG TAT TAA These must be homologous in state. -
Phylogenetics of Buchnera Aphidicola Munson Et Al., 1991
Türk. entomol. derg., 2019, 43 (2): 227-237 ISSN 1010-6960 DOI: http://dx.doi.org/10.16970/entoted.527118 E-ISSN 2536-491X Original article (Orijinal araştırma) Phylogenetics of Buchnera aphidicola Munson et al., 1991 (Enterobacteriales: Enterobacteriaceae) based on 16S rRNA amplified from seven aphid species1 Farklı yaprak biti türlerinden izole edilen Buchnera aphidicola Munson et al., 1991 (Enterobacteriales: Enterobacteriaceae)’nın 16S rRNA’ya göre filogenetiği Gül SATAR2* Abstract The obligate symbiont, Buchnera aphidicola Munson et al., 1991 (Enterobacteriales: Enterobacteriaceae) is important for the physiological processes of aphids. Buchnera aphidicola genes detected in seven aphid species, collected in 2017 from different plants and altitudes in Adana Province, Turkey were analyzed to reveal phylogenetic interactions between Buchnera and aphids. The 16S rRNA gene was amplified and sequenced for this purpose and a phylogenetic tree built up by the neighbor-joining method. A significant correlation between B. aphidicola genes and the aphid species was revealed by this phylogenetic tree and the haplotype network. Specimens collected in Feke from Solanum melongena L. was distinguished from the other B. aphidicola genes on Aphis gossypii Glover, 1877 (Hemiptera: Aphididae) with a high bootstrap value of 99. Buchnera aphidicola in Myzus spp. was differentiated from others, and the difference between Myzus cerasi (Fabricius, 1775) and Myzus persicae (Sulzer, 1776) was clear. Although, B. aphidicola is specific to its host aphid, certain nucleotide differences obtained within the species could enable specification to geographic region or host plant in the future. Keywords: Aphid, genetic similarity, phylogenetics, symbiotic bacterium Öz Obligat simbiyont, Buchnera aphidicola Munson et al., 1991 (Enterobacteriales: Enterobacteriaceae), yaprak bitlerinin fizyolojik olaylarının sürdürülmesinde önemli bir rol oynar. -
An Introduction to Phylogenetic Analysis
This article reprinted from: Kosinski, R.J. 2006. An introduction to phylogenetic analysis. Pages 57-106, in Tested Studies for Laboratory Teaching, Volume 27 (M.A. O'Donnell, Editor). Proceedings of the 27th Workshop/Conference of the Association for Biology Laboratory Education (ABLE), 383 pages. Compilation copyright © 2006 by the Association for Biology Laboratory Education (ABLE) ISBN 1-890444-09-X All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted, in any form or by any means, electronic, mechanical, photocopying, recording, or otherwise, without the prior written permission of the copyright owner. Use solely at one’s own institution with no intent for profit is excluded from the preceding copyright restriction, unless otherwise noted on the copyright notice of the individual chapter in this volume. Proper credit to this publication must be included in your laboratory outline for each use; a sample citation is given above. Upon obtaining permission or with the “sole use at one’s own institution” exclusion, ABLE strongly encourages individuals to use the exercises in this proceedings volume in their teaching program. Although the laboratory exercises in this proceedings volume have been tested and due consideration has been given to safety, individuals performing these exercises must assume all responsibilities for risk. The Association for Biology Laboratory Education (ABLE) disclaims any liability with regards to safety in connection with the use of the exercises in this volume. The focus of ABLE is to improve the undergraduate biology laboratory experience by promoting the development and dissemination of interesting, innovative, and reliable laboratory exercises. -
Phylogeny Codon Models • Last Lecture: Poor Man’S Way of Calculating Dn/Ds (Ka/Ks) • Tabulate Synonymous/Non-Synonymous Substitutions • Normalize by the Possibilities
Phylogeny Codon models • Last lecture: poor man’s way of calculating dN/dS (Ka/Ks) • Tabulate synonymous/non-synonymous substitutions • Normalize by the possibilities • Transform to genetic distance KJC or Kk2p • In reality we use codon model • Amino acid substitution rates meet nucleotide models • Codon(nucleotide triplet) Codon model parameterization Stop codons are not allowed, reducing the matrix from 64x64 to 61x61 The entire codon matrix can be parameterized using: κ kappa, the transition/transversionratio ω omega, the dN/dS ratio – optimizing this parameter gives the an estimate of selection force πj the equilibrium codon frequency of codon j (Goldman and Yang. MBE 1994) Empirical codon substitution matrix Observations: Instantaneous rates of double nucleotide changes seem to be non-zero There should be a mechanism for mutating 2 adjacent nucleotides at once! (Kosiol and Goldman) • • Phylogeny • • Last lecture: Inferring distance from Phylogenetic trees given an alignment How to infer trees and distance distance How do we infer trees given an alignment • • Branch length Topology d 6-p E 6'B o F P Edo 3 vvi"oH!.- !fi*+nYolF r66HiH- .) Od-:oXP m a^--'*A ]9; E F: i ts X o Q I E itl Fl xo_-+,<Po r! UoaQrj*l.AP-^PA NJ o - +p-5 H .lXei:i'tH 'i,x+<ox;+x"'o 4 + = '" I = 9o FF^' ^X i! .poxHo dF*x€;. lqEgrE x< f <QrDGYa u5l =.ID * c 3 < 6+6_ y+ltl+5<->-^Hry ni F.O+O* E 3E E-f e= FaFO;o E rH y hl o < H ! E Y P /-)^\-B 91 X-6p-a' 6J. -
EVOLUTIONARY INFERENCE: Some Basics of Phylogenetic Analyses
EVOLUTIONARY INFERENCE: Some basics of phylogenetic analyses. Ana Rojas Mendoza CNIO-Madrid-Spain. Alfonso Valencia’s lab. Aims of this talk: • 1.To introduce relevant concepts of evolution to practice phylogenetic inference from molecular data. • 2.To introduce some of the most useful methods and computer programmes to practice phylogenetic inference. • • 3.To show some examples I’ve worked in. SOME BASICS 11--ConceptsConcepts ofof MolecularMolecular EvolutionEvolution • Homology vs Analogy. • Homology vs similarity. • Ortologous vs Paralogous genes. • Species tree vs genes tree. • Molecular clock. • Allele mutation vs allele substitution. • Rates of allele substitution. • Neutral theory of evolution. SOME BASICS Owen’s definition of homology Richard Owen, 1843 • Homologue: the same organ under every variety of form and function (true or essential correspondence). •Analogy: superficial or misleading similarity. SOME BASICS 1.Concepts1.Concepts ofof MolecularMolecular EvolutionEvolution • Homology vs Analogy. • Homology vs similarity. • Ortologous vs Paralogous genes. • Species tree vs genes tree. • Molecular clock. • Allele mutation vs allele substitution. • Rates of allele substitution. • Neutral theory of evolution. SOME BASICS Similarity ≠ Homology • Similarity: mathematical concept . Homology: biological concept Common Ancestry!!! SOME BASICS 1.Concepts1.Concepts ofof MolecularMolecular EvolutionEvolution • Homology vs Analogy. • Homology vs similarity. • Ortologous vs Paralogous genes. • Species tree vs genes tree. • Molecular clock. -
"Phylogenetic Analysis of Protein Sequence Data Using The
Phylogenetic Analysis of Protein Sequence UNIT 19.11 Data Using the Randomized Axelerated Maximum Likelihood (RAXML) Program Antonis Rokas1 1Department of Biological Sciences, Vanderbilt University, Nashville, Tennessee ABSTRACT Phylogenetic analysis is the study of evolutionary relationships among molecules, phenotypes, and organisms. In the context of protein sequence data, phylogenetic analysis is one of the cornerstones of comparative sequence analysis and has many applications in the study of protein evolution and function. This unit provides a brief review of the principles of phylogenetic analysis and describes several different standard phylogenetic analyses of protein sequence data using the RAXML (Randomized Axelerated Maximum Likelihood) Program. Curr. Protoc. Mol. Biol. 96:19.11.1-19.11.14. C 2011 by John Wiley & Sons, Inc. Keywords: molecular evolution r bootstrap r multiple sequence alignment r amino acid substitution matrix r evolutionary relationship r systematics INTRODUCTION the baboon-colobus monkey lineage almost Phylogenetic analysis is a standard and es- 25 million years ago, whereas baboons and sential tool in any molecular biologist’s bioin- colobus monkeys diverged less than 15 mil- formatics toolkit that, in the context of pro- lion years ago (Sterner et al., 2006). Clearly, tein sequence analysis, enables us to study degree of sequence similarity does not equate the evolutionary history and change of pro- with degree of evolutionary relationship. teins and their function. Such analysis is es- A typical phylogenetic analysis of protein sential to understanding major evolutionary sequence data involves five distinct steps: (a) questions, such as the origins and history of data collection, (b) inference of homology, (c) macromolecules, developmental mechanisms, sequence alignment, (d) alignment trimming, phenotypes, and life itself.