Phylogenetics and the Human Microbiome
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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. -
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. -
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. -
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: -
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. -
A Fast Neighbor Joining Method
Methodology A fast neighbor joining method J.F. Li School of Computer Science and Technology, Civil Aviation University of China, Tianjin, China Corresponding author: J.F. Li E-mail: [email protected] Genet. Mol. Res. 14 (3): 8733-8743 (2015) Received March 2, 2015 Accepted April 6, 2015 Published July 31, 2015 DOI http://dx.doi.org/10.4238/2015.July.31.22 ABSTRACT. With the rapid development of sequencing technologies, an increasing number of sequences are available for evolutionary tree reconstruction. Although neighbor joining is regarded as the most popular and fastest evolutionary tree reconstruction method [its time complexity is O(n3), where n is the number of sequences], it is not sufficiently fast to infer evolutionary trees containing more than a few hundred sequences. To increase the speed of neighbor joining, we herein propose FastNJ, a fast implementation of neighbor joining, which was motivated by RNJ and FastJoin, two improved versions of conventional neighbor joining. The main difference between FastNJ and conventional neighbor joining is that, in the former, many pairs of nodes selected by the rule used in RNJ are joined in each iteration. In theory, the time complexity of FastNJ can reach O(n2) in the best cases. Experimental results show that FastNJ yields a significant increase in speed compared to RNJ and conventional neighbor joining with a minimal loss of accuracy. Key words: Evolutionary tree reconstruction; Neighbor joining; FastNJ Genetics and Molecular Research 14 (3): 8733-8743 (2015) ©FUNPEC-RP www.funpecrp.com.br J.F. Li 8734 INTRODUCTION Evolutionary tree reconstruction is a basic and important research field in bioinfor- matics. -
Microbial Evolution: Concepts and Controversies
Conference abstracts from the Colloqium Microbial Evolution: Concepts and Controversies organised by The Canada Research Chair in the history of biology at the Université du Québec à Montréal, from October 17 to 19 2002 Beyond neo-Darwinism: The Origins of Microbial Phylogenetics Jan Sapp Department fo History, Université du Québec à Montréal, CIRST Chairholder of the Canada Research Chair in the History of Biology The neo-Darwinian evolutionary synthesis of the 1930s and 1940s dealt with the evolution of plants and animals over the last 560 million years. It did not address the evolution of microorganisms and the previous 3000 million years of evolutionary change on earth. During the last two decades of the twentieth century, biologists developed new comparative molecular techniques and concepts to trace life back thousands of millions of years to investigate early microbial evolution with the aim to create a universal phylogeny. Studies of microbial phylogeny have brought about a conceptual revolution in the way in which evolutionary change occurs in microbes with the evidence for the fundamental importance of symbiotic mergers, fusions, and various other mechanisms for horizontal gene transfer. The scope and significance of these mechanisms remain subjects of controversy. The Origin of Intermediate Metabolism Harold Morowitz Krasnow Institute, George Mason University, Fairfax, VA 2030, USA The case is made for autotrophs preceding heterotrophs, chemoautorophs preceding photoautotrophs, and the reductive tricarboxylic acid cycle preceding the Calvin-Benson cycle. The acetyl Co-A pathway is less certain. A group of universal features of the primary chart of autotrophic metabolism is discussed. This includes the universal nitrogen entry point and the universal sulfur entry point. -
Investigation of South African Estuarine Microbial Species and Genome Diversity
Investigation of South African Estuarine Microbial species and Genome diversity By Ms. Eveline Kaambo Submitted in partial fulfillment of the requirement for the degree of Magister Scientiae (M.Sc) in the Department of Biotechnology, University of the Western Cape Supervisor: Professor D.A. Cowan November 2006 Abstract A study of the microbial diversity in sediments of the Great Berg River estuary is carried out using modern molecular phylogenetic methods. The aim of the study is to determine the effect of (pollution by) the effluents of the fish industry on the composition of the microbial community in the sediments. The diversity in microbial groups of sediment samples that received wastewater from the local fishing industry is investigated by a PCR-DGGE (polymerase chain reaction-denaturing gradient gel electrophoresis) approach and compared to an unaffected site. DGGE is used for the separation of 16S rDNA amplified from metagenomic DNA, which is expected to provide qualitative information on sediment microbial community composition. The DGGE method is also applied to monitor changes of the microbial community at different depths in the estuarine sediment. Two primer sets is used in this study, one specific for 16S rDNA from the domain Bacteria and the other for DNA from the domain Archaea, which allowed the depth profiles for these groups of organisms to be compared. The DGGE profiles representing the bacteria revealed a decrease in diversity with depth at the downstream site of the wastewater outlet. In contrast, the archaeal diversity increases with depth. In addition to the DGGE analyses, 16S rDNA clone libraries were constructed from both sampling sites. -
Diversity of Microorganisms Within Rock Varnish in the Whipple Mountains, California† K
APPLIED AND ENVIRONMENTAL MICROBIOLOGY, Feb. 2006, p. 1708–1715 Vol. 72, No. 2 0099-2240/06/$08.00ϩ0 doi:10.1128/AEM.72.2.1708–1715.2006 Copyright © 2006, American Society for Microbiology. All Rights Reserved. Diversity of Microorganisms within Rock Varnish in the Whipple Mountains, California† K. R. Kuhlman,1* W. G. Fusco,2 M. T. La Duc,1 L. B. Allenbach,2 C. L. Ball,2 G. M. Kuhlman,1 R. C. Anderson,1 I. K. Erickson,3 T. Stuecker,1 J. Benardini,2 J. L. Strap,2 and R. L. Crawford2 Jet Propulsion Laboratory, California Institute of Technology, Pasadena, California 911091; Environmental Biotechnology Institute, University of Idaho, Moscow, Idaho 83844-10522; and Department of Biological Sciences, University of Idaho, Moscow, Idaho 83844-30513 Received 16 February 2005/Accepted 19 October 2005 Rock varnish from Arizona’s Whipple Mountains harbors a microbial community containing about 108 microorganisms g؊1 of varnish. Analyses of varnish phospholipid fatty acids and rRNA gene libraries reveal a community comprised of mostly Proteobacteria but also including Actinobacteria, eukaryota, and a few members of the Archaea. Rock varnish represents a significant niche for microbial colonization. Rock varnish (also known as desert varnish) is a dark, thin forms very slowly at rates thought to be between Ͻ1 to about (usually 5 to 500 m thick), layered veneer composed of clay 40 m per 1,000 years (50), thus archeologists have been in- minerals cemented together by oxides and hydroxides of man- terested in dating the age of varnishes to place petroglyphs ganese and iron (11, 20, 56, 63, 64).