Phylogenetic Analysis of the Baltic Skeletonema Marinoi
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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. -
MAST/IOC Advanced Phytoplankton Course on Taxonomy and Syste
Optical Character Recognition (OCR) document. WARNING! Spelling errors might subsist. In order to access to the original document in image form, click on "Original" button on 1st page. Intergovernmental Oceanographic Commission Training Course Reports 36 MAST-IOC Advanced Phytoplankton Course on Taxonomy and Systematics Marine Botany Laboratory Stazione Zoologica ‘A. Dohrn’ di Napoli Casamicciola Terme (Island of Ischia), Naples, Italy 24 September - 14 October 1995 UNESCO Optical Character Recognition (OCR) document. WARNING! Spelling errors might subsist. In order to access to the original document in image form, click on "Original" button on 1st page. IOC Training Course Report No. 36 page (i) TABLE OF CONTENTS Page iii ABSTRACT 1. BACKGROUND, ORGANIZATION AND GOALS 1 1.1 BACKGROUND 1 1.2 ORGANIZATION 1 1.3 GOALS 2 2. CONTENT 2 2.1 OPENING AND INTRODUCTION 3 2.2 MANUALS 3 2.3 THEORETICAL SESSIONS 3 2.4 PRACTICAL SESSIONS 3 2.4.1. Species observation 3 2.4.2. Techniques 4 2.4.3. Exercises 4 2.5 FIELD TRIP AND OBSERVATION OF LIVE NATURAL SAMPLES 4 2.6 SCANNING (SEM) AND TRANSMISSION ELECTRON MICROSCOPE (TEM) DEMONSTRATIONS 5 2.7 WORKSHOPS 5 2.8 REGIONAL REPORTS ON HARMFUL ALGAL BLOOMS 5 2.9 SEMINARS 5 3. QUESTIONNAIRE AND CONCLUDING REMARKS 6 3.1 QUESTIONNAIRE 6 3.1 CONCLUDING REMARKS 6 ANNEXES I Programme II Faculty III Participants to the five previous Courses IV Organizing Committee V List of participants VI Financial statement VII Workshops VIII Regional reports IX Questionnaire X List of Acronyms Optical Character Recognition (OCR) document. WARNING! Spelling errors might subsist. -
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. -
Math/C SC 5610 Computational Biology Lecture 12: Phylogenetics
Math/C SC 5610 Computational Biology Lecture 12: Phylogenetics Stephen Billups University of Colorado at Denver Math/C SC 5610Computational Biology – p.1/25 Announcements Project Guidelines and Ideas are posted. (proposal due March 8) CCB Seminar, Friday (Mar. 4) Speaker: Jack Horner, SAIC Title: Phylogenetic Methods for Characterizing the Signature of Stage I Ovarian Cancer in Serum Protein Mas Time: 11-12 (Followed by lunch) Place: Media Center, AU008 Math/C SC 5610Computational Biology – p.2/25 Outline Distance based methods for phylogenetics UPGMA WPGMA Neighbor-Joining Character based methods Maximum Likelihood Maximum Parsimony Math/C SC 5610Computational Biology – p.3/25 Review: Distance Based Clustering Methods Main Idea: Requires a distance matrix D, (defining distances between each pair of elements). Repeatedly group together closest elements. Different algorithms differ by how they treat distances between groups. UPGMA (unweighted pair group method with arithmetic mean). WPGMA (weighted pair group method with arithmetic mean). Math/C SC 5610Computational Biology – p.4/25 UPGMA 1. Initialize C to the n singleton clusters f1g; : : : ; fng. 2. Initialize dist(c; d) on C by defining dist(fig; fjg) = D(i; j): 3. Repeat n ¡ 1 times: (a) determine pair c; d of clusters in C such that dist(c; d) is minimal; define dmin = dist(c; d). (b) define new cluster e = c S d; update C = C ¡ fc; dg Sfeg. (c) define a node with label e and daughters c; d, where e has distance dmin=2 to its leaves. (d) define for all f 2 C with f 6= e, dist(c; f) + dist(d; f) dist(e; f) = dist(f; e) = : (avg. -
PHYCOLOGICAL REVIEWS 18 the Species Concept in Diatoms
Phycologia (1999) Volume 38 (6), 437-495 Published 10 December 1999 PHYCOLOGICAL REVIEWS 18 The species concept in diatoms DAVID G. MANN* Royal Botanic Garden, Edinburgh EH3 5LR, Scotland, UK D.G. MANN. 1999. Phycological reviews 18. The species concept in diatoms. Phycologia 38: 437-495. Diatoms are the most species-rich group of algae. They are ecologically widespread and have global significance in the carbon and silicon cycles, and are used increasingly in ecological monitoring, paleoecological reconstruction, and stratigraphic corre lation. Despite this, the species taxonomy of diatoms is messy and lacks a satisfactory practical or conceptual basis, hindering further advances in all aspects of diatom biology. Several model systems have provided valuable insights into the nature of diatom species. A consilience of evidence (the 'Waltonian species concept') from morphology, genetic data, mating systems, physiology, ecology, and crossing behavior suggests that species boundaries have traditionally been drawn too broadly; many species probably contain several reproductively isolated entities that are worth taxonomic recognition at species level. Pheno typic plasticity, although present, is not a serious problem for diatom taxonomy. However, although good data are now available for demes living in sympatry, we have barely begun to extend studies to take into account variation between allopatric demes, which is necessary if a global taxonomy is to be built. Endemism has been seriously underestimated among diatoms, but biogeographical and stratigraphic patterns are difficult to discern, because of a lack of trustwOlthy data and because the taxonomic concepts of many authors are undocumented. Morphological diversity may often be a largely accidental consequence of physiological differentiation, as a result of the peculiarities of diatom cell division and the life cycle. -
Clustering and Phylogenetic Approaches to Classification: Illustration on Stellar Tracks Didier Fraix-Burnet, Marc Thuillard
Clustering and Phylogenetic Approaches to Classification: Illustration on Stellar Tracks Didier Fraix-Burnet, Marc Thuillard To cite this version: Didier Fraix-Burnet, Marc Thuillard. Clustering and Phylogenetic Approaches to Classification: Il- lustration on Stellar Tracks. 2014. hal-01703341 HAL Id: hal-01703341 https://hal.archives-ouvertes.fr/hal-01703341 Preprint submitted on 7 Feb 2018 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. Clustering and Phylogenetic Approaches to Classification: Illustration on Stellar Tracks D. Fraix-Burnet1, M. Thuillard2 1 Univ. Grenoble Alpes, CNRS, IPAG, 38000 Grenoble, France email: [email protected] 2 La Colline, 2072 St-Blaise, Switzerland February 7, 2018 This pedagogicalarticle was written in 2014 and is yet unpublished. Partofit canbefoundin Fraix-Burnet (2015). Abstract Classifying objects into groups is a natural activity which is most often a prerequisite before any physical analysis of the data. Clustering and phylogenetic approaches are two different and comple- mentary ways in this purpose: the first one relies on similarities and the second one on relationships. In this paper, we describe very simply these approaches and show how phylogenetic techniques can be used in astrophysics by using a toy example based on a sample of stars obtained from models of stellar evolution. -
Diatom Distribution in the Lower Save River, Mozambique
Department of Physical Geography Diatom distribution in the lower Save River, Mozambique Taxonomy, salinity gradient and taphonomy Marie Christiansson Master’s thesis NKA 156 Physical Geography and Quaternary Geology, 60 Credits 2016 Preface This Master’s thesis is Marie Christiansson’s degree project in Physical Geography and Quaternary Geology at the Department of Physical Geography, Stockholm University. The Master’s thesis comprises 60 credits (two terms of full-time studies). Supervisor has been Jan Risberg at the Department of Physical Geography, Stockholm University. Examiner has been Stefan Wastegård at the Department of Physical Geography, Stockholm University. The author is responsible for the contents of this thesis. Stockholm, 11 September 2016 Steffen Holzkämper Director of studies Abstract In this study diatom distribution within the lower Save River, Mozambique, has been identified from surface sediments, surface water, mangrove cortex and buried sediments. Sandy units, bracketing a geographically extensive clay layer, have been dated with optical stimulated luminescence (OSL). Diatom analysis has been used to interpret the spatial salinity gradient and to discuss taphonomic processes within the delta. Previously, one study has been performed in the investigated area and it is of great importance to continue to identify diatom distributions since siliceous microfossils are widely used for paleoenvironmental research. Two diatom taxa, which were not possible to classify to species level have been identified; Cyclotella sp. and Diploneis sp. It is suggested that these represent species not earlier described; however they are assigned a brackish water affinity. Diatom analysis from surface water, surface sediments and mangrove cortex indicate a transition from ocean water to a dominance of freshwater taxa c. -
Phylogenetics
Phylogenetics What is phylogenetics? • Study of branching patterns of descent among lineages • Lineages – Populations – Species – Molecules • Shift between population genetics and phylogenetics is often the species boundary – Distantly related populations also show patterning – Patterning across geography What is phylogenetics? • Goal: Determine and describe the evolutionary relationships among lineages – Order of events – Timing of events • Visualization: Phylogenetic trees – Graph – No cycles Phylogenetic trees • Nodes – Terminal – Internal – Degree • Branches • Topology Phylogenetic trees • Rooted or unrooted – Rooted: Precisely 1 internal node of degree 2 • Node that represents the common ancestor of all taxa – Unrooted: All internal nodes with degree 3+ Stephan Steigele Phylogenetic trees • Rooted or unrooted – Rooted: Precisely 1 internal node of degree 2 • Node that represents the common ancestor of all taxa – Unrooted: All internal nodes with degree 3+ Phylogenetic trees • Rooted or unrooted – Rooted: Precisely 1 internal node of degree 2 • Node that represents the common ancestor of all taxa – Unrooted: All internal nodes with degree 3+ • Binary: all speciation events produce two lineages from one • Cladogram: Topology only • Phylogram: Topology with edge lengths representing time or distance • Ultrametric: Rooted tree with time-based edge lengths (all leaves equidistant from root) Phylogenetic trees • Clade: Group of ancestral and descendant lineages • Monophyly: All of the descendants of a unique common ancestor • Polyphyly: -
VII EUROPEAN CONGRESS of PROTISTOLOGY in Partnership with the INTERNATIONAL SOCIETY of PROTISTOLOGISTS (VII ECOP - ISOP Joint Meeting)
See discussions, stats, and author profiles for this publication at: https://www.researchgate.net/publication/283484592 FINAL PROGRAMME AND ABSTRACTS BOOK - VII EUROPEAN CONGRESS OF PROTISTOLOGY in partnership with THE INTERNATIONAL SOCIETY OF PROTISTOLOGISTS (VII ECOP - ISOP Joint Meeting) Conference Paper · September 2015 CITATIONS READS 0 620 1 author: Aurelio Serrano Institute of Plant Biochemistry and Photosynthesis, Joint Center CSIC-Univ. of Seville, Spain 157 PUBLICATIONS 1,824 CITATIONS SEE PROFILE Some of the authors of this publication are also working on these related projects: Use Tetrahymena as a model stress study View project Characterization of true-branching cyanobacteria from geothermal sites and hot springs of Costa Rica View project All content following this page was uploaded by Aurelio Serrano on 04 November 2015. The user has requested enhancement of the downloaded file. VII ECOP - ISOP Joint Meeting / 1 Content VII ECOP - ISOP Joint Meeting ORGANIZING COMMITTEES / 3 WELCOME ADDRESS / 4 CONGRESS USEFUL / 5 INFORMATION SOCIAL PROGRAMME / 12 CITY OF SEVILLE / 14 PROGRAMME OVERVIEW / 18 CONGRESS PROGRAMME / 19 Opening Ceremony / 19 Plenary Lectures / 19 Symposia and Workshops / 20 Special Sessions - Oral Presentations / 35 by PhD Students and Young Postdocts General Oral Sessions / 37 Poster Sessions / 42 ABSTRACTS / 57 Plenary Lectures / 57 Oral Presentations / 66 Posters / 231 AUTHOR INDEX / 423 ACKNOWLEDGMENTS-CREDITS / 429 President of the Organizing Committee Secretary of the Organizing Committee Dr. Aurelio Serrano -
The Generalized Neighbor Joining Method
Ruriko Yoshida The Generalized Neighbor Joining method Ruriko Yoshida Dept. of Statistics University of Kentucky Joint work with Dan Levy and Lior Pachter www.math.duke.edu/~ruriko Oxford 1 Ruriko Yoshida Challenge We would like to assemble the fungi tree of life. Francois Lutzoni and Rytas Vilgalys Department of Biology, Duke University 1500+ fungal species http://ocid.nacse.org/research/aftol/about.php Oxford 2 Ruriko Yoshida Many problems to be solved.... http://tolweb.org/tree?group=fungi Zygomycota is not monophyletic. The position of some lineages such as that of Glomales and of Engodonales-Mortierellales is unclear, but they may lie outside Zygomycota as independent lineages basal to the Ascomycota- Basidiomycota lineage (Bruns et al., 1993). Oxford 3 Ruriko Yoshida Phylogeny Phylogenetic trees describe the evolutionary relations among groups of organisms. Oxford 4 Ruriko Yoshida Constructing trees from sequence data \Ten years ago most biologists would have agreed that all organisms evolved from a single ancestral cell that lived 3.5 billion or more years ago. More recent results, however, indicate that this family tree of life is far more complicated than was believed and may not have had a single root at all." (W. Ford Doolittle, (June 2000) Scienti¯c American). Since the proliferation of Darwinian evolutionary biology, many scientists have sought a coherent explanation from the evolution of life and have tried to reconstruct phylogenetic trees. Oxford 5 Ruriko Yoshida Methods to reconstruct a phylogenetic tree from DNA sequences include: ² The maximum likelihood estimation (MLE) methods: They describe evolution in terms of a discrete-state continuous-time Markov process. -
Rapid Neighbour-Joining
Rapid Neighbour-Joining Martin Simonsen, Thomas Mailund and Christian N. S. Pedersen Bioinformatics Research Center (BIRC), University of Aarhus, C. F. Møllers All´e,Building 1110, DK-8000 Arhus˚ C, Denmark. fkejseren,mailund,[email protected] Abstract. The neighbour-joining method reconstructs phylogenies by iteratively joining pairs of nodes until a single node remains. The cri- terion for which pair of nodes to merge is based on both the distance between the pair and the average distance to the rest of the nodes. In this paper, we present a new search strategy for the optimisation criteria used for selecting the next pair to merge and we show empirically that the new search strategy is superior to other state-of-the-art neighbour- joining implementations. 1 Introduction The neighbour-joining (NJ) method by Saitou and Nei [8] is a widely used method for phylogenetic reconstruction, made popular by a combination of com- putational efficiency combined with reasonable accuracy. With its cubic running time by Studier and Kepler [11], the method scales to hundreds of species, and while it is usually possible to infer phylogenies with thousands of species, tens or hundreds of thousands of species is infeasible. Various approaches have been taken to improve the running time for neighbour-joining. QuickTree [5] was an attempt to improve performance by making an efficient implementation. It out- performed the existing implementations but still performs as a O n3 time algo- rithm. QuickJoin [6, 7], instead, uses an advanced search heuristic when searching for the pair of nodes to join. Where the straight-forward search takes time O n2 per join, QuickJoin on average reduces this to O (n) and can reconstruct large trees in a small fraction of the time needed by QuickTree. -
A Comparative Analysis of Popular Phylogenetic Reconstruction Algorithms
A Comparative Analysis of Popular Phylogenetic Reconstruction Algorithms Evan Albright, Jack Hessel, Nao Hiranuma, Cody Wang, and Sherri Goings Department of Computer Science Carleton College MN, 55057 [email protected] Abstract Understanding the evolutionary relationships between organisms by comparing their genomic sequences is a focus of modern-day computational biology research. Estimating evolutionary history in this way has many applications, particularly in analyzing the progression of infectious, viral diseases. Phylogenetic reconstruction algorithms model evolutionary history using tree-like structures that describe the estimated ancestry of a given set of species. Many methods exist to infer phylogenies from genes, but no one technique is definitively better for all types of sequences and organisms. Here, we implement and analyze several popular tree reconstruction methods and compare their effectiveness on both synthetic and real genomic sequences. Our synthetic data set aims to simulate a variety of research conditions, and includes inputs that vary in number of species. For our case-study, we use the genes of 53 apes and compare our reconstructions against the well-studied evolutionary history of primates. Though our implementations often represent the simplest manifestations of these complex methods, our results are suggestive of fundamental advantages and disadvantages that underlie each of these techniques. Introduction A phylogenetic tree is a tree structure that represents evolutionary relationship among both extant and extinct species [1]. Each node in a tree represents a different species, and internal nodes in a tree represent most common ancestors of their direct child nodes. The length of a branch in a phylogenetic tree is an indication of evolutionary distance.