Clustering and Phylogenetic Approaches to Classification: Illustration on Stellar Tracks Didier Fraix-Burnet, Marc Thuillard
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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). -
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.). -
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. -
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. -
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: -
Molecular Phylogenetics: Principles and Practice
REVIEWS STUDY DESIGNS Molecular phylogenetics: principles and practice Ziheng Yang1,2 and Bruce Rannala1,3 Abstract | Phylogenies are important for addressing various biological questions such as relationships among species or genes, the origin and spread of viral infection and the demographic changes and migration patterns of species. The advancement of sequencing technologies has taken phylogenetic analysis to a new height. Phylogenies have permeated nearly every branch of biology, and the plethora of phylogenetic methods and software packages that are now available may seem daunting to an experimental biologist. Here, we review the major methods of phylogenetic analysis, including parsimony, distance, likelihood and Bayesian methods. We discuss their strengths and weaknesses and provide guidance for their use. statistical Systematics Before the advent of DNA sequencing technologies, phylogenetics, creating the emerging field of 2,18,19 The inference of phylogenetic phylogenetic trees were used almost exclusively to phylogeography. In species tree methods , the gene relationships among species describe relationships among species in systematics and trees at individual loci may not be of direct interest and and the use of such information taxonomy. Today, phylogenies are used in almost every may be in conflict with the species tree. By averaging to classify species. branch of biology. Besides representing the relation- over the unobserved gene trees under the multi-species 20 Taxonomy ships among species on the tree of life, phylogenies -
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.