# Consensus Methods. * You are only responsible for the first two

Size: px
Start display at page:

Download "Consensus Methods. * You are only responsible for the first two"

Transcription

1 Consensus Trees * consensus trees reconcile clades from different trees * consensus is a conservative estimate of phylogeny that emphasizes points of agreement * philosophy: agreement among data sets is more important than agreement within data sets * a position of safety - defensible and pragmatic starting point especially if you are proposing a new classification or testing a hypothesis

2 Consensus Trees (1) Different data sets same taxa; different character systems e.g., larval and adult data for insects e.g., molecular sequence data versus morphology - prevent molecular characters from swamping out morphological data e.g., must use consensus methods for some data sets; - distance plus discrete character data (2) Comparing results from different algorithms same taxa, same data; different algorithms e.g., distance vs parsimony or likelihood trees - one scenario here is when you have some long branch problems and algorithms deal with them differently

3 Consensus Trees (3) Choosing among trees of equal stature same taxa, same data, same algorithm, different trees e.g., have set of equally MPTs, but need a summary solution e.g., need to summarizing set of bootstrap replicates of your data Note: even when topologies are exactly the same tree can differ in * how character are plotted (reconstructed) on the trees * how branch lengths are fitted

4 Consensus Methods Label all components: each distinct component (clade) is given a unique number. Different algorithms/methods work with these numbers (have different rules) *Strict Consensus (Nelson 1979, Sokal & Rohlf 1981): only those components (clades) shared by all trees are considered; components must be exactly replicated among all trees. Most restrictive approach. *Consensus n-trees (Margush & McMorris 1981): accepts all nodes/resolutions that are present in n% or more of the trees. Usually n=50 and referred to as majority rule consensus. Adam's Consensus (Adams 1972, McMorris et al. 1982): pulls down components to the first node to which there will be no conflict. Most unrestrictive approach. Preserves structure. * You are only responsible for the first two

5 strict Adam s majority rule From Quicke Principles and Techniques of Contemporary Taxonomy

6 Majority-rule consensus PAUP output

7 Contrived example with rogue taxa A simple, yet starkly contrasting, example for which the strict consensus [of a, b, and c] returns a star tree, but for which our algorithm correctly identifies the rogue taxa and produces a fully resolved tree (e) reproduced from Pattengale et al. (2010) Uncovering hidden phylogenetic consensus.

8 Criticisms of Consensus Methods (1) consensus method lose character information and therefore descriptive and explanatory power-- relative to any one of the minimal length trees. (2) too much resolution--majority rule consensus trees indicate(monophyletic) groups not present in the set of best trees. (Something to keep in mind.)

9 Combined Data Approach = total evidence approach = analyze all data together if you can (and if it makes sense to do so) * many examples of morphology & molecular data sets where morphology has served an important role in determining topological relationships

10 Combined Data Approach * data set combination may yield more resolved tree than either data set alone, e.g., where one data set provides data for terminals and second data set provides characters for basal nodes. * may have weak but true signals in (both) data sets * even possible for combined data to conflict with consensus tree, e.g., in Barrett et al. (1991) where combined data tree is not found among any of the MPTs or consensus trees

11 Barrett et al. (1991) Syst. Biol.

12 Types of Error in Phylogenetic Inference Random Error: estimate of mean is not an accurate estimate of true population mean * most sites saturated * too small a sample size estimate of true phylogeny is off * random error disappears with larger data sets Systemic Error: where more data leads to more support for the wrong tree, i.e., it leads to inconsistency * non-independent substitutions (where characters/bases are under selection) * long branches (inconsistent for parsimony; problematic for all methods) * paralogy (different gene copies are being compared) * ancestral polymorphism (lineage sorting) * hybridization * horizontal gene transfer (xenology)

13 Concerted Evolution (A Paralogy Problem) Concerted evolution is a process that may explain the observation that paralogous genes within one species are more closely related to each other than to members of the same gene family in another species, even though the gene duplication event preceded the speciation event. The high sequence similarity between paralogs is maintained by homologous recombination events that lead to gene conversion, effectively copying some sequence from one and overwriting the homologous region in the other. An example can be seen in bacteria: Escherichia coli has seven operons encoding various Ribosomal RNA. For each of these genes, rdna sequences are essentially identical among all of the seven operons (sequence divergence of only 0.195%). In a closely related species, Haemophilus influenzae its six ribosomal RNA operons are entirely identical. When the 2 species are compared together however, the sequence divergence of the 16S rrna gene between them is 5.90%. [1]

14

15 When to Combine and When to Use Consensus * If there is error in your data set then watch out for combined data approaches * If one data set is providing wrong signal, isn't it better to have one right and one wrong answer from separate analyses, than a single incorrect one? * If different trees result from different data sets and each is strongly supported (earmarks of systematic error) use consensus methods that only accept groups found in set of best trees.

16 Data Partitions and Congruence * Issues of data combination and consensus are also relevant to single data sets * Think about different data partitions of a single data set: e.g. different genes e.g. process partitions - coding and non-coding sections of a gene - 1 st, 2 nd, and 3 rd positions - non-synonymous vs synonymous nucleotide changes - different classes of amino acid substitutions * Bull et al. (1993) recommend doing homogeneity tests to look for data set agreement

17 Bull et al. (1993) Syst.Biol.

18 Comparing Trees There are many circumstances when we need to compare trees: * choosing best model of nucleotide substitution (nested models) * testing tree against favored/alternative hypothesis (topology) (e.g., the existing classification) * testing goodness of best vs. suboptimal trees * in likelihood and Bayesian frameworks comparing branch length estimates * comparing combinability of trees (homogeneity tests) * comparing trees without the intention of combining: e.g., comparing tree structures to biogeographic pattern, or for evidence of co-cladogenesis (co-speciation) * etc.

19 Incongruence Length Difference Test Farris et al. (1994) (tests whether data partitions can be combined in parsimony analysis) D XY = L (X + Y) - (L X + L Y ) where L (X + Y) = length of tree from combined data L X = length of tree from data set X L Y = length of tree from data set Y D XY = measure of incongruence D XY = is large, when combining data creates substantial (additional) homoplasy L (X + Y) exceeds (L X + L Y ) by the amount of extra homoplasy required by the combined approach.

20 Incongruence Length Difference Test * A significance test of the data partition incongruence can be generated by comparing the combined data tree length to values generated by random partitionings of the data sets. * 100 to 1000 random partitions (P P, P Q ) are made by generating data sets (matrices) identical in size to the original data sets (but now with random mixes of characters from the two original data sets) * tree lengths (L P, L Q ), for random partitions are calculated for each partition * because the structure (signal) in the original partitions is randomized the new partitions and new (randomized) trees may get longer (esp. if the partitions are incongruent) * but combining them will no longer add much additional homoplasy * so D PQ values will be small * compare the number of times D XY > D PQ * if D XY is greater than D PQ more than 95% of the time do not combine partitions

21 Incongruence Length Difference Test * implemented in PAUP as Partition Homogeneity Test * sensitive to invariant sites and inclusion of autapomorphies * not a indicator of data set congruence, if evolutionary rates for the data sets are different (Barker and Lutzoni 2002 and Darlu and Lecointre 2002) * even with significant partition heterogeneity, often possible to combine data and get improved phylogenetic accuracy (Barker and Lutzoni, 2002)

22 Data Partitions there is no set rule for number sometimes partition assigned for each gene even more important is to assign a partition for each codon position or both of the above the biggest advantage to partitioning is that each partition can be modeled separately i.e., one can employ a different evolutionary model: e.g., morphology versus individual genes (e.g., in MrBayes)

23 Model Tests (optional) hierarchical likelihood ratio test AIC Akaike Information Criterion (measures loss of information when wrong model is used) Bayesian Test of Models can employ Bayes factors, posterior probabilities, or Bayes Information Criterion Decision theory methods jmodeltest and jmodeltest2 (Posada et al. 2012) see ( MrModeltest (for MrBayes Vers. 3) (Nylander 2008) ( html) See Posada, D Selecting models of evolution. pp In The Phylogenetic Handbook: A Practical Approach to Phylogenetic Analysis. P. Lemey, M. Salemi, and A Vandemme (eds.). Cambridge Univ. Press.

24 Hierarchical Likelihood Ratio Test * use when models are nested (i.e., one case is a special case of the other) * hierarchical likelihood ratio test hlrt = 2(lnL 1 lnl 2 ) where lnl 1 maximum log likelihood under more parameter rich model and lnl 2 maximum log likelihood under less parameter-rich model * essentially a X² distribution (with degrees of freedom equal to number of extra parameters in more complex model) * use lnl 1 (more complex model) when LRT is sufficiently large

25 Consider two hierarchically nested substitution models: HKY85 and GTR. The GTR model differs from HKY85 by the addition of four additional rate parameters. Imagine we had the likelihood scores of the two models for a neighbor-joining tree: HKY85 -lnl = GTR -lnl = Then, LRT = 2 ( ) = 4.53 df = 4 (GTR adds 4 additional parameters to HKY85) critical value (P = 0.05) = 9.49 In this case, GTR does not fit the data significantly better than HKY85, and we infer that the four rate additional rate parameters are not biologically meaningful (given our power to detect such differences). source:

26 Maximum likelihood inference(aic) (Akaike, 1974) * AIC = -2lnL + 2n * Where, lnl is the maximum likelihood value of a specific model of nucleotide sequence evolution and tree topology given the data. * n = the number of parameters free to vary * Smaller AIC indicates a better model source:

27 Comparing Tree Topologies (lab) * Kishino-Hasegawa (KH) test * Shimodaira-Hasegawa test * Weighted test variants * Approximately unbiased (AU) test * Swofford-Olsen-Waddell-Hillis (SOWH) * Software: Consel: Tree Puzzle: See Schmidt Testing Tree topologies. pp In The Phylogenetic Handbook: A Practical Approach to Phylogenetic Analysis. P. Lemey, M. Salemi, and A Vandemme (eds.). Cambridge Univ. Press.

28 Tree comparing software: Consel and Tree Puzzle

29 Quantifying Incongruence Sometimes we have no intention of combining the trees and simply want to know to what extent our trees agree or disagree. For example: 1. You have a tree and a biogeographic pattern and want to know how well they fit or how much they disagree? 2. You have host and parasite and want to quantify degree of fit/mismatch to evaluate evidence for co-speciation? Becerra Science 276:

30 From Page (2003) Tangled Trees

31 Insects on Plants: Macroevolutionary Chemical Trends in Host Use Leaf beetle phylogeny (above) vs phenogram of plant secondary chemical profile (right). Becerra Science 276: Leaf beetle phylogeny (above) vs hostplant phylogeny (right)

### C3020 Molecular Evolution. Exercises #3: Phylogenetics

C3020 Molecular Evolution Exercises #3: Phylogenetics Consider the following sequences for five taxa 1-5 and the known outgroup O, which has the ancestral states (note that sequence 3 has changed from

### Phylogenetics. Applications of phylogenetics. Unrooted networks vs. rooted trees. Outline

Phylogenetics Todd Vision iology 522 March 26, 2007 pplications of phylogenetics Studying organismal or biogeographic history Systematics ating events in the fossil record onservation biology Studying

### Amira A. AL-Hosary PhD of infectious diseases Department of Animal Medicine (Infectious Diseases) Faculty of Veterinary Medicine Assiut

Amira A. AL-Hosary PhD of infectious diseases Department of Animal Medicine (Infectious Diseases) Faculty of Veterinary Medicine Assiut University-Egypt Phylogenetic analysis Phylogenetic Basics: Biological

### POPULATION GENETICS Winter 2005 Lecture 17 Molecular phylogenetics

POPULATION GENETICS Winter 2005 Lecture 17 Molecular phylogenetics - in deriving a phylogeny our goal is simply to reconstruct the historical relationships between a group of taxa. - before we review the

### Constructing Evolutionary/Phylogenetic Trees

Constructing Evolutionary/Phylogenetic Trees 2 broad categories: istance-based methods Ultrametric Additive: UPGMA Transformed istance Neighbor-Joining Character-based Maximum Parsimony Maximum Likelihood

### CHAPTERS 24-25: Evidence for Evolution and Phylogeny

CHAPTERS 24-25: Evidence for Evolution and Phylogeny 1. For each of the following, indicate how it is used as evidence of evolution by natural selection or shown as an evolutionary trend: a. Paleontology

### Biology 559R: Introduction to Phylogenetic Comparative Methods Topics for this week (Jan 27 & 29):

Biology 559R: Introduction to Phylogenetic Comparative Methods Topics for this week (Jan 27 & 29): Statistical estimation of models of sequence evolution Phylogenetic inference using maximum likelihood:

### Introduction to characters and parsimony analysis

Introduction to characters and parsimony analysis Genetic Relationships Genetic relationships exist between individuals within populations These include ancestordescendent relationships and more indirect

### Dr. Amira A. AL-Hosary

Phylogenetic analysis Amira A. AL-Hosary PhD of infectious diseases Department of Animal Medicine (Infectious Diseases) Faculty of Veterinary Medicine Assiut University-Egypt Phylogenetic Basics: Biological

### Constructing Evolutionary/Phylogenetic Trees

Constructing Evolutionary/Phylogenetic Trees 2 broad categories: Distance-based methods Ultrametric Additive: UPGMA Transformed Distance Neighbor-Joining Character-based Maximum Parsimony Maximum Likelihood

### Phylogenetics: Building Phylogenetic Trees

1 Phylogenetics: Building Phylogenetic Trees COMP 571 Luay Nakhleh, Rice University 2 Four Questions Need to be Answered What data should we use? Which method should we use? Which evolutionary model should

### Phylogenetics: Building Phylogenetic Trees. COMP Fall 2010 Luay Nakhleh, Rice University

Phylogenetics: Building Phylogenetic Trees COMP 571 - Fall 2010 Luay Nakhleh, Rice University Four Questions Need to be Answered What data should we use? Which method should we use? Which evolutionary

### 8/23/2014. Phylogeny and the Tree of Life

Phylogeny and the Tree of Life Chapter 26 Objectives Explain the following characteristics of the Linnaean system of classification: a. binomial nomenclature b. hierarchical classification List the major

### Lecture 27. Phylogeny methods, part 7 (Bootstraps, etc.) p.1/30

Lecture 27. Phylogeny methods, part 7 (Bootstraps, etc.) Joe Felsenstein Department of Genome Sciences and Department of Biology Lecture 27. Phylogeny methods, part 7 (Bootstraps, etc.) p.1/30 A non-phylogeny

### Maximum Likelihood Tree Estimation. Carrie Tribble IB Feb 2018

Maximum Likelihood Tree Estimation Carrie Tribble IB 200 9 Feb 2018 Outline 1. Tree building process under maximum likelihood 2. Key differences between maximum likelihood and parsimony 3. Some fancy extras

### MOLECULAR SYSTEMATICS: A SYNTHESIS OF THE COMMON METHODS AND THE STATE OF KNOWLEDGE

CELLULAR & MOLECULAR BIOLOGY LETTERS http://www.cmbl.org.pl Received: 16 August 2009 Volume 15 (2010) pp 311-341 Final form accepted: 01 March 2010 DOI: 10.2478/s11658-010-0010-8 Published online: 19 March

### A Phylogenetic Network Construction due to Constrained Recombination

A Phylogenetic Network Construction due to Constrained Recombination Mohd. Abdul Hai Zahid Research Scholar Research Supervisors: Dr. R.C. Joshi Dr. Ankush Mittal Department of Electronics and Computer

### Integrative Biology 200 "PRINCIPLES OF PHYLOGENETICS" Spring 2016 University of California, Berkeley. Parsimony & Likelihood [draft]

Integrative Biology 200 "PRINCIPLES OF PHYLOGENETICS" Spring 2016 University of California, Berkeley K.W. Will Parsimony & Likelihood [draft] 1. Hennig and Parsimony: Hennig was not concerned with parsimony

### Molecular phylogeny How to infer phylogenetic trees using molecular sequences

Molecular phylogeny How to infer phylogenetic trees using molecular sequences ore Samuelsson Nov 2009 Applications of phylogenetic methods Reconstruction of evolutionary history / Resolving taxonomy issues

### Molecular phylogeny How to infer phylogenetic trees using molecular sequences

Molecular phylogeny How to infer phylogenetic trees using molecular sequences ore Samuelsson Nov 200 Applications of phylogenetic methods Reconstruction of evolutionary history / Resolving taxonomy issues

### Phylogenetic Analysis

Phylogenetic Analysis Aristotle Through classification, one might discover the essence and purpose of species. Nelson & Platnick (1981) Systematics and Biogeography Carl Linnaeus Swedish botanist (1700s)

### Phylogenetic Analysis

Phylogenetic Analysis Aristotle Through classification, one might discover the essence and purpose of species. Nelson & Platnick (1981) Systematics and Biogeography Carl Linnaeus Swedish botanist (1700s)

### Chapter 26: Phylogeny and the Tree of Life Phylogenies Show Evolutionary Relationships

Chapter 26: Phylogeny and the Tree of Life You Must Know The taxonomic categories and how they indicate relatedness. How systematics is used to develop phylogenetic trees. How to construct a phylogenetic

### "PRINCIPLES OF PHYLOGENETICS: ECOLOGY AND EVOLUTION" Integrative Biology 200B Spring 2009 University of California, Berkeley

"PRINCIPLES OF PHYLOGENETICS: ECOLOGY AND EVOLUTION" Integrative Biology 200B Spring 2009 University of California, Berkeley B.D. Mishler Jan. 22, 2009. Trees I. Summary of previous lecture: Hennigian

### Phylogenetic Tree Reconstruction

I519 Introduction to Bioinformatics, 2011 Phylogenetic Tree Reconstruction Yuzhen Ye (yye@indiana.edu) School of Informatics & Computing, IUB Evolution theory Speciation Evolution of new organisms is driven

### Algorithmic Methods Well-defined methodology Tree reconstruction those that are well-defined enough to be carried out by a computer. Felsenstein 2004,

Tracing the Evolution of Numerical Phylogenetics: History, Philosophy, and Significance Adam W. Ferguson Phylogenetic Systematics 26 January 2009 Inferring Phylogenies Historical endeavor Darwin- 1837

### 1 ATGGGTCTC 2 ATGAGTCTC

We need an optimality criterion to choose a best estimate (tree) Other optimality criteria used to choose a best estimate (tree) Parsimony: begins with the assumption that the simplest hypothesis that

### Letter to the Editor. Department of Biology, Arizona State University

Letter to the Editor Traditional Phylogenetic Reconstruction Methods Reconstruct Shallow and Deep Evolutionary Relationships Equally Well Michael S. Rosenberg and Sudhir Kumar Department of Biology, Arizona

### Phylogenetic Analysis

Phylogenetic Analysis Aristotle Through classification, one might discover the essence and purpose of species. Nelson & Platnick (1981) Systematics and Biogeography Carl Linnaeus Swedish botanist (1700s)

### Phylogenetic analysis. Characters

Typical steps: Phylogenetic analysis Selection of taxa. Selection of characters. Construction of data matrix: character coding. Estimating the best-fitting tree (model) from the data matrix: phylogenetic

### UoN, CAS, DBSC BIOL102 lecture notes by: Dr. Mustafa A. Mansi. The Phylogenetic Systematics (Phylogeny and Systematics)

- Phylogeny? - Systematics? The Phylogenetic Systematics (Phylogeny and Systematics) - Phylogenetic systematics? Connection between phylogeny and classification. - Phylogenetic systematics informs the

### Phylogenetic relationship among S. castellii, S. cerevisiae and C. glabrata.

Supplementary Note S2 Phylogenetic relationship among S. castellii, S. cerevisiae and C. glabrata. Phylogenetic trees reconstructed by a variety of methods from either single-copy orthologous loci (Class

### Questions we can ask. Recall. Accuracy and Precision. Systematics - Bio 615. Outline

Outline 1. Mechanistic comparison with Parsimony - branch lengths & parameters 2. Performance comparison with Parsimony - Desirable attributes of a method - The Felsenstein and Farris zones - Heterotachous

### Today's project. Test input data Six alignments (from six independent markers) of Curcuma species

DNA sequences II Analyses of multiple sequence data datasets, incongruence tests, gene trees vs. species tree reconstruction, networks, detection of hybrid species DNA sequences II Test of congruence of

### What is Phylogenetics

What is Phylogenetics Phylogenetics is the area of research concerned with finding the genetic connections and relationships between species. The basic idea is to compare specific characters (features)

### Elements of Bioinformatics 14F01 TP5 -Phylogenetic analysis

Elements of Bioinformatics 14F01 TP5 -Phylogenetic analysis 10 December 2012 - Corrections - Exercise 1 Non-vertebrate chordates generally possess 2 homologs, vertebrates 3 or more gene copies; a Drosophila

### Intraspecific gene genealogies: trees grafting into networks

Intraspecific gene genealogies: trees grafting into networks by David Posada & Keith A. Crandall Kessy Abarenkov Tartu, 2004 Article describes: Population genetics principles Intraspecific genetic variation

### Phylogenetic inference

Phylogenetic inference Bas E. Dutilh Systems Biology: Bioinformatic Data Analysis Utrecht University, March 7 th 016 After this lecture, you can discuss (dis-) advantages of different information types

Tree of Life iological Sequence nalysis Chapter http://tolweb.org/tree/ Phylogenetic Prediction ll organisms on Earth have a common ancestor. ll species are related. The relationship is called a phylogeny

### Theory of Evolution Charles Darwin

Theory of Evolution Charles arwin 858-59: Origin of Species 5 year voyage of H.M.S. eagle (83-36) Populations have variations. Natural Selection & Survival of the fittest: nature selects best adapted varieties

### A data based parsimony method of cophylogenetic analysis

Blackwell Science, Ltd A data based parsimony method of cophylogenetic analysis KEVIN P. JOHNSON, DEVIN M. DROWN & DALE H. CLAYTON Accepted: 20 October 2000 Johnson, K. P., Drown, D. M. & Clayton, D. H.

### Molecular phylogeny - Using molecular sequences to infer evolutionary relationships. Tore Samuelsson Feb 2016

Molecular phylogeny - Using molecular sequences to infer evolutionary relationships Tore Samuelsson Feb 2016 Molecular phylogeny is being used in the identification and characterization of new pathogens,

### Estimating Evolutionary Trees. Phylogenetic Methods

Estimating Evolutionary Trees v if the data are consistent with infinite sites then all methods should yield the same tree v it gets more complicated when there is homoplasy, i.e., parallel or convergent

### Homoplasy. Selection of models of molecular evolution. Evolutionary correction. Saturation

Homoplasy Selection of models of molecular evolution David Posada Homoplasy indicates identity not produced by descent from a common ancestor. Graduate class in Phylogenetics, Campus Agrário de Vairão,

### Phylogeny and systematics. Why are these disciplines important in evolutionary biology and how are they related to each other?

Phylogeny and systematics Why are these disciplines important in evolutionary biology and how are they related to each other? Phylogeny and systematics Phylogeny: the evolutionary history of a species

### Evaluating phylogenetic hypotheses

Evaluating phylogenetic hypotheses Methods for evaluating topologies Topological comparisons: e.g., parametric bootstrapping, constrained searches Methods for evaluating nodes Resampling techniques: bootstrapping,

### Inferring phylogeny. Constructing phylogenetic trees. Tõnu Margus. Bioinformatics MTAT

Inferring phylogeny Constructing phylogenetic trees Tõnu Margus Contents What is phylogeny? How/why it is possible to infer it? Representing evolutionary relationships on trees What type questions questions

### (Stevens 1991) 1. morphological characters should be assumed to be quantitative unless demonstrated otherwise

Bot 421/521 PHYLOGENETIC ANALYSIS I. Origins A. Hennig 1950 (German edition) Phylogenetic Systematics 1966 B. Zimmerman (Germany, 1930 s) C. Wagner (Michigan, 1920-2000) II. Characters and character states

### A (short) introduction to phylogenetics

A (short) introduction to phylogenetics Thibaut Jombart, Marie-Pauline Beugin MRC Centre for Outbreak Analysis and Modelling Imperial College London Genetic data analysis with PR Statistics, Millport Field

### Using phylogenetics to estimate species divergence times... Basics and basic issues for Bayesian inference of divergence times (plus some digression)

Using phylogenetics to estimate species divergence times... More accurately... Basics and basic issues for Bayesian inference of divergence times (plus some digression) "A comparison of the structures

### Computational aspects of host parasite phylogenies Jamie Stevens Date received (in revised form): 14th June 2004

Jamie Stevens is a lecturer in parasitology and evolution in the Department of Biological Sciences, University of Exeter. His research interests in molecular evolutionary parasitology range from human

### "Nothing in biology makes sense except in the light of evolution Theodosius Dobzhansky

MOLECULAR PHYLOGENY "Nothing in biology makes sense except in the light of evolution Theodosius Dobzhansky EVOLUTION - theory that groups of organisms change over time so that descendeants differ structurally

### Bioinformatics tools for phylogeny and visualization. Yanbin Yin

Bioinformatics tools for phylogeny and visualization Yanbin Yin 1 Homework assignment 5 1. Take the MAFFT alignment http://cys.bios.niu.edu/yyin/teach/pbb/purdue.cellwall.list.lignin.f a.aln as input and

### Phylogenetic Networks, Trees, and Clusters

Phylogenetic Networks, Trees, and Clusters Luay Nakhleh 1 and Li-San Wang 2 1 Department of Computer Science Rice University Houston, TX 77005, USA nakhleh@cs.rice.edu 2 Department of Biology University

### Phylogenetics. BIOL 7711 Computational Bioscience

Consortium for Comparative Genomics! University of Colorado School of Medicine Phylogenetics BIOL 7711 Computational Bioscience Biochemistry and Molecular Genetics Computational Bioscience Program Consortium

### Reconstructing the history of lineages

Reconstructing the history of lineages Class outline Systematics Phylogenetic systematics Phylogenetic trees and maps Class outline Definitions Systematics Phylogenetic systematics/cladistics Systematics

### 9/30/11. Evolution theory. Phylogenetic Tree Reconstruction. Phylogenetic trees (binary trees) Phylogeny (phylogenetic tree)

I9 Introduction to Bioinformatics, 0 Phylogenetic ree Reconstruction Yuzhen Ye (yye@indiana.edu) School of Informatics & omputing, IUB Evolution theory Speciation Evolution of new organisms is driven by

### Biology 211 (2) Week 1 KEY!

Biology 211 (2) Week 1 KEY Chapter 1 KEY FIGURES: 1.2, 1.3, 1.4, 1.5, 1.6, 1.7 VOCABULARY: Adaptation: a trait that increases the fitness Cells: a developed, system bound with a thin outer layer made of

### Lecture V Phylogeny and Systematics Dr. Kopeny

Delivered 1/30 and 2/1 Lecture V Phylogeny and Systematics Dr. Kopeny Lecture V How to Determine Evolutionary Relationships: Concepts in Phylogeny and Systematics Textbook Reading: pp 425-433, 435-437

### Assessing an Unknown Evolutionary Process: Effect of Increasing Site- Specific Knowledge Through Taxon Addition

Assessing an Unknown Evolutionary Process: Effect of Increasing Site- Specific Knowledge Through Taxon Addition David D. Pollock* and William J. Bruno* *Theoretical Biology and Biophysics, Los Alamos National

### Systematics - Bio 615

Bayesian Phylogenetic Inference 1. Introduction, history 2. Advantages over ML 3. Bayes Rule 4. The Priors 5. Marginal vs Joint estimation 6. MCMC Derek S. Sikes University of Alaska 7. Posteriors vs Bootstrap

### Concepts and Methods in Molecular Divergence Time Estimation

Concepts and Methods in Molecular Divergence Time Estimation 26 November 2012 Prashant P. Sharma American Museum of Natural History Overview 1. Why do we date trees? 2. The molecular clock 3. Local clocks

### RECONSTRUCTING THE HISTORY OF HOST-PARASITE ASSOCIATIONS USING GENF, RAIJRED PARSIMONY

Cladistics (1995) 11:73-89 RECONSTRUCTING THE HISTORY OF HOST-PARASITE ASSOCIATIONS USING GENF, RAIJRED PARSIMONY Fredrik Ronquist Department of Entomology, Swedish Museum of Natural History, Box 50007,

### Consensus methods. Strict consensus methods

Consensus methods A consensus tree is a summary of the agreement among a set of fundamental trees There are many consensus methods that differ in: 1. the kind of agreement 2. the level of agreement Consensus

### Phylogenetic methods in molecular systematics

Phylogenetic methods in molecular systematics Niklas Wahlberg Stockholm University Acknowledgement Many of the slides in this lecture series modified from slides by others www.dbbm.fiocruz.br/james/lectures.html

### Lab 9: Maximum Likelihood and Modeltest

Integrative Biology 200A University of California, Berkeley "PRINCIPLES OF PHYLOGENETICS" Spring 2010 Updated by Nick Matzke Lab 9: Maximum Likelihood and Modeltest In this lab we re going to use PAUP*

### Appendix from L. J. Revell, On the Analysis of Evolutionary Change along Single Branches in a Phylogeny

008 by The University of Chicago. All rights reserved.doi: 10.1086/588078 Appendix from L. J. Revell, On the Analysis of Evolutionary Change along Single Branches in a Phylogeny (Am. Nat., vol. 17, no.

### "PRINCIPLES OF PHYLOGENETICS: ECOLOGY AND EVOLUTION" Integrative Biology 200 Spring 2018 University of California, Berkeley

"PRINCIPLES OF PHYLOGENETICS: ECOLOGY AND EVOLUTION" Integrative Biology 200 Spring 2018 University of California, Berkeley D.D. Ackerly Feb. 26, 2018 Maximum Likelihood Principles, and Applications to

### PHYLOGENY AND SYSTEMATICS

AP BIOLOGY EVOLUTION/HEREDITY UNIT Unit 1 Part 11 Chapter 26 Activity #15 NAME DATE PERIOD PHYLOGENY AND SYSTEMATICS PHYLOGENY Evolutionary history of species or group of related species SYSTEMATICS Study

### METHODS FOR DETERMINING PHYLOGENY. In Chapter 11, we discovered that classifying organisms into groups was, and still is, a difficult task.

Chapter 12 (Strikberger) Molecular Phylogenies and Evolution METHODS FOR DETERMINING PHYLOGENY In Chapter 11, we discovered that classifying organisms into groups was, and still is, a difficult task. Modern

### Phylogeny: building the tree of life

Phylogeny: building the tree of life Dr. Fayyaz ul Amir Afsar Minhas Department of Computer and Information Sciences Pakistan Institute of Engineering & Applied Sciences PO Nilore, Islamabad, Pakistan

### BINF6201/8201. Molecular phylogenetic methods

BINF60/80 Molecular phylogenetic methods 0-7-06 Phylogenetics Ø According to the evolutionary theory, all life forms on this planet are related to one another by descent. Ø Traditionally, phylogenetics

### 7. Tests for selection

Sequence analysis and genomics 7. Tests for selection Dr. Katja Nowick Group leader TFome and Transcriptome Evolution Bioinformatics group Paul-Flechsig-Institute for Brain Research www. nowicklab.info

### Chapter 19: Taxonomy, Systematics, and Phylogeny

Chapter 19: Taxonomy, Systematics, and Phylogeny AP Curriculum Alignment Chapter 19 expands on the topics of phylogenies and cladograms, which are important to Big Idea 1. In order for students to understand

### Phylogenetics: Bayesian Phylogenetic Analysis. COMP Spring 2015 Luay Nakhleh, Rice University

Phylogenetics: Bayesian Phylogenetic Analysis COMP 571 - Spring 2015 Luay Nakhleh, Rice University Bayes Rule P(X = x Y = y) = P(X = x, Y = y) P(Y = y) = P(X = x)p(y = y X = x) P x P(X = x 0 )P(Y = y X

### Integrative Biology 200A "PRINCIPLES OF PHYLOGENETICS" Spring 2012 University of California, Berkeley

Integrative Biology 200A "PRINCIPLES OF PHYLOGENETICS" Spring 2012 University of California, Berkeley B.D. Mishler April 12, 2012. Phylogenetic trees IX: Below the "species level;" phylogeography; dealing

### GENETICS - CLUTCH CH.22 EVOLUTIONARY GENETICS.

!! www.clutchprep.com CONCEPT: OVERVIEW OF EVOLUTION Evolution is a process through which variation in individuals makes it more likely for them to survive and reproduce There are principles to the theory

### Integrative Biology 200 "PRINCIPLES OF PHYLOGENETICS" Spring 2018 University of California, Berkeley

Integrative Biology 200 "PRINCIPLES OF PHYLOGENETICS" Spring 2018 University of California, Berkeley B.D. Mishler Feb. 14, 2018. Phylogenetic trees VI: Dating in the 21st century: clocks, & calibrations;

### Theory of Evolution. Charles Darwin

Theory of Evolution harles arwin 858-59: Origin of Species 5 year voyage of H.M.S. eagle (8-6) Populations have variations. Natural Selection & Survival of the fittest: nature selects best adapted varieties

### Molecular Phylogenetics (part 1 of 2) Computational Biology Course João André Carriço

Molecular Phylogenetics (part 1 of 2) Computational Biology Course João André Carriço jcarrico@fm.ul.pt Charles Darwin (1809-1882) Charles Darwin s tree of life in Notebook B, 1837-1838 Ernst Haeckel (1934-1919)

### Anatomy of a species tree

Anatomy of a species tree T 1 Size of current and ancestral Populations (N) N Confidence in branches of species tree t/2n = 1 coalescent unit T 2 Branch lengths and divergence times of species & populations

### Maximum Likelihood Until recently the newest method. Popularized by Joseph Felsenstein, Seattle, Washington.

Maximum Likelihood This presentation is based almost entirely on Peter G. Fosters - "The Idiot s Guide to the Zen of Likelihood in a Nutshell in Seven Days for Dummies, Unleashed. http://www.bioinf.org/molsys/data/idiots.pdf

### C.DARWIN ( )

C.DARWIN (1809-1882) LAMARCK Each evolutionary lineage has evolved, transforming itself, from a ancestor appeared by spontaneous generation DARWIN All organisms are historically interconnected. Their relationships

### Comparative Bioinformatics Midterm II Fall 2004

Comparative Bioinformatics Midterm II Fall 2004 Objective Answer, part I: For each of the following, select the single best answer or completion of the phrase. (3 points each) 1. Deinococcus radiodurans

### "PRINCIPLES OF PHYLOGENETICS: ECOLOGY AND EVOLUTION" Integrative Biology 200B Spring 2011 University of California, Berkeley

"PRINCIPLES OF PHYLOGENETICS: ECOLOGY AND EVOLUTION" Integrative Biology 200B Spring 2011 University of California, Berkeley B.D. Mishler March 31, 2011. Reticulation,"Phylogeography," and Population Biology:

### Phylogenetics Todd Vision Spring Some applications. Uncultured microbial diversity

Phylogenetics Todd Vision Spring 2008 Tree basics Sequence alignment Inferring a phylogeny Neighbor joining Maximum parsimony Maximum likelihood Rooting trees and measuring confidence Software and file

### Phylogenetic analyses. Kirsi Kostamo

Phylogenetic analyses Kirsi Kostamo The aim: To construct a visual representation (a tree) to describe the assumed evolution occurring between and among different groups (individuals, populations, species,

### ESS 345 Ichthyology. Systematic Ichthyology Part II Not in Book

ESS 345 Ichthyology Systematic Ichthyology Part II Not in Book Thought for today: Now, here, you see, it takes all the running you can do, to keep in the same place. If you want to get somewhere else,

### Additive distances. w(e), where P ij is the path in T from i to j. Then the matrix [D ij ] is said to be additive.

Additive distances Let T be a tree on leaf set S and let w : E R + be an edge-weighting of T, and assume T has no nodes of degree two. Let D ij = e P ij w(e), where P ij is the path in T from i to j. Then

### Some of these slides have been borrowed from Dr. Paul Lewis, Dr. Joe Felsenstein. Thanks!

Some of these slides have been borrowed from Dr. Paul Lewis, Dr. Joe Felsenstein. Thanks! Paul has many great tools for teaching phylogenetics at his web site: http://hydrodictyon.eeb.uconn.edu/people/plewis

### Molecular Clocks. The Holy Grail. Rate Constancy? Protein Variability. Evidence for Rate Constancy in Hemoglobin. Given

Molecular Clocks Rose Hoberman The Holy Grail Fossil evidence is sparse and imprecise (or nonexistent) Predict divergence times by comparing molecular data Given a phylogenetic tree branch lengths (rt)

### Bioinformatics 1. Sepp Hochreiter. Biology, Sequences, Phylogenetics Part 4. Bioinformatics 1: Biology, Sequences, Phylogenetics

Bioinformatics 1 Biology, Sequences, Phylogenetics Part 4 Sepp Hochreiter Klausur Mo. 30.01.2011 Zeit: 15:30 17:00 Raum: HS14 Anmeldung Kusss Contents Methods and Bootstrapping of Maximum Methods Methods

### Homework Assignment, Evolutionary Systems Biology, Spring Homework Part I: Phylogenetics:

Homework Assignment, Evolutionary Systems Biology, Spring 2009. Homework Part I: Phylogenetics: Introduction. The objective of this assignment is to understand the basics of phylogenetic relationships

### How should we organize the diversity of animal life?

How should we organize the diversity of animal life? The difference between Taxonomy Linneaus, and Cladistics Darwin What are phylogenies? How do we read them? How do we estimate them? Classification (Taxonomy)

### Algorithms in Bioinformatics

Algorithms in Bioinformatics Sami Khuri Department of Computer Science San José State University San José, California, USA khuri@cs.sjsu.edu www.cs.sjsu.edu/faculty/khuri Distance Methods Character Methods

### Many of the slides that I ll use have been borrowed from Dr. Paul Lewis, Dr. Joe Felsenstein. Thanks!

Many of the slides that I ll use have been borrowed from Dr. Paul Lewis, Dr. Joe Felsenstein. Thanks! Paul has many great tools for teaching phylogenetics at his web site: http://hydrodictyon.eeb.uconn.edu/people/plewis

### Using models of nucleotide evolution to build phylogenetic trees

Developmental and Comparative Immunology 29 (2005) 211 227 www.elsevier.com/locate/devcompimm Using models of nucleotide evolution to build phylogenetic trees David H. Bos a, *, David Posada b a School

### Microbial Taxonomy and the Evolution of Diversity

19 Microbial Taxonomy and the Evolution of Diversity Copyright McGraw-Hill Global Education Holdings, LLC. Permission required for reproduction or display. 1 Taxonomy Introduction to Microbial Taxonomy