Maximum Likelihood Tree Estimation. Carrie Tribble IB Feb 2018
|
|
- Victoria Riley
- 5 years ago
- Views:
Transcription
1 Maximum Likelihood Tree Estimation Carrie Tribble IB Feb 2018
2 Outline 1. Tree building process under maximum likelihood 2. Key differences between maximum likelihood and parsimony 3. Some fancy extras
3 It [maximum likelihood] involves finding that evolutionary tree which yields the highest probability of evolving the observed data (Felsenstein 1981).
4 Maximum Likelihood Likelihood = P(D M) Probability Data Model
5 Maximum Likelihood Likelihood = P(D M) Probability Alignment Topology (Ψ), Branch lengths (v), Model Parameters (θ)
6 Toss a coin 10 times: H T H H T T H H H H Probability of heads is p Probability of tails is 1-p What is the probability of our data given our model of a two-sided coin? H T H H T T H H H H L = p * (1-p) * p * p *(1-p) * (1-p) * p * p * p * p L = p 7 * (1-p) 3
7 p = seq(from = 0, to = 1, by = 0.01) L = p^7 * (1 p)^3
8 Our new approach 1. Build a model of character evolution 2. Use that model to calculate the likelihood of a particular phylogeny 3. Find the tree/ parameter values with the highest likelihood (maximum likelihood)
9 Our new approach: Step 1 Build a model of character evolution What is a model? Parameters vs. parameter values A T C G A???? T???? C???? G????
10 Our new approach: Step 1 Build a model of character evolution What is a model? Parameters vs. parameter values A T C G A - a a a T a - a a C a a - a G a a a -
11 Our new approach: Step 1 Build a model of character evolution What is a model? Parameters vs. parameter values A T C G A T C G
12 Our new approach: Step 1 Build a model of character evolution What is a model? Parameters vs. parameter values Modified from Huelsenbeck 2017
13 Q = Jukes Cantor (JC69) A G C T A - μ/3 μ/3 μ/3 G μ/3 - μ/3 μ/3 C μ/3 μ/3 - μ/3 T μ/3 μ/3 μ/3 - Kimura (K80) A G C T A - K 1 1 G K C K T 1 1 K - * μ A <-> G: Transition (purine purine) C <-> T: Transition (pyrimadine pyrimadine) Felsenstein (F81) A G C T A - π G π C π T G π A - π C π T C π A π G - π T T π A π G π C - Hasegawa, Kishino, Yano (HKY85) A G C T A - kπ G π C π T * μ G kπ A - π C π T * μ C π A π G - kπ T T π A π G kπ C - Base frequencies vary
14 General Time Reversible (GTR): A G C T A - απ G βπ C γπ T G απ A - δπ C επ T * μ C βπ A δπ G - ηπ T T γπ A επ G ηπ C - Rate parameters: α = A <-> G β = A <-> C γ = A <-> T δ = C <-> G ε = T <-> G η = T <-> C Stationary base frequencies: π A = frequency of A π G = frequency of G π C = frequency of A π T = frequency of T
15 General Time Reversible (GTR): Markov models are memoryless A G C T A - απ G βπ C γπ T G απ A - δπ C επ T * μ C βπ A δπ G - ηπ T T γπ A επ G ηπ C - Rate parameters: α = A <-> G β = A <-> C γ = A <-> T δ = C <-> G ε = T <-> G η = T <-> C Stationary base frequencies: π A = frequency of A π G = frequency of G π C = frequency of A π T = frequency of T
16
17 Our new approach: Step 2 It [maximum likelihood] involves finding that evolutionary tree which yields the highest probability of evolving the observed data (Felsenstein 1981). GAATC GTATC AAATC GAATC GTATC AAATC GAATC GTATC AAATC Let s start with one site in the alignment
18 Our new approach: Step 2 P ij (t) π s0 Felsenstein 1981
19 Our new approach: Step Probability of node for all possible base pairs Felsenstein s pruning algorithm Figured modified from Huelsenbeck 2017
20 Where do we get P ij (t) and π s0? Q = A G C T A -3λ λ λ λ G λ -3λ λ λ C λ λ -3λ λ T λ λ λ -3λ A G C T P(t) = e Qt = A p 0 (t) p 1 (t) p 1 (t) p 1 (t) G p 1 (t) p 0 (t) p 1 (t) p 1 (t) C p 1 (t) p 1 (t) p 0 (t) p 1 (t) with, p 0 (t) = ¼ + ¾e -4λt p 1 (t) = ¼ - ¼e -4λt T p 1 (t) p 1 (t) p 1 (t) p 0 (t)
21 Pick a tree & parameter values Use Felsenstein s Pruning Algorithm & your model of evolution to calculate the likelihood of a tree for a single site in an alignment Assume all sites in alignment are independent & multiply likelihoods of all sites to calculate the likelihood of the tree given the alignment Vary parameter values for that topology to find the maximum likelihood Pick a new tree, repeat! Overview:
22 This process is often untenable computationally Number of rooted trees increases A LOT with increasing number of taxa multiplying small number -> smaller numbers so we use log likelihood
23
24 Main differences: Branch length matter!!!!! Estimation of parameters Model underlies the process
25 Main differences: Branch length matter!!!!! ATTCG ATTCG Time What happened along the branches? ATTCG ATTCG
26 Main differences: Branch length matter!!!!! ATTCG ATTCG Time C -> G What happened along the branches? ATTCG G -> C ATTCG
27 Main differences: Branch length matter!!!!! Estimation of parameters Model underlies the process
28 Main differences: Branch lengths matter!!!!! Estimation of parameters How does this compare to weighting in parsimony?
29 Fancy Things Error? Rate heterogeneity Model testing Additional models (codon, amino acid, etc.)
30 Get idea of error through bootstrapping But what error is this measuring?
31 Rate Heterogeneity: Partitions gene1 gene2 gene3 gene4 gene5 A G C T A -3λ λ λ λ G λ -3λ λ λ C λ λ -3λ λ T λ λ λ -3λ
32 Rate Heterogeneity: Partitions A G C T A G C T A -3λ λ λ λ A -3λ λ λ λ G λ -3λ λ λ G λ -3λ λ λ C λ λ -3λ λ C λ λ -3λ λ T λ λ λ -3λ T λ λ λ -3λ gene1 gene2 gene3 gene4 gene5 A G C T A -3λ λ λ λ G λ -3λ λ λ C λ λ -3λ λ T λ λ λ -3λ A G C T A -3λ λ λ λ G λ -3λ λ λ C λ λ -3λ λ T λ λ λ -3λ A G C T A -3λ λ λ λ G λ -3λ λ λ C λ λ -3λ λ T λ λ λ -3λ Estimate model parameters separately for different genes!
33 Rate Heterogeneity: Partitions gene1 gene2 gene3 gene4 gene5 Estimate model parameters separately for different codon positions!
34 Rate Heterogeneity: gamma (Γ)
35 Model testing Likelihood ratio test for nested models: LR = 2 * (-lnl1 -lnl2) HKY85 -lnl = GTR -lnl = LR = 2( ) = 4.53, df = 4 critical value (P = 0.05) = 9.49 (~chi 2 distribution)
36 Model testing Aikake Information Criterion (AIC) (non nested): Find model with lowest AIC Models need not be nested Can correct for small sample sizes (AICc) BIC is a similar alternative
37 Additional models Amino Acid Codon non-markov What would a model of morphology look like?
Lecture 24. Phylogeny methods, part 4 (Models of DNA and protein change) p.1/22
Lecture 24. Phylogeny methods, part 4 (Models of DNA and protein change) Joe Felsenstein Department of Genome Sciences and Department of Biology Lecture 24. Phylogeny methods, part 4 (Models of DNA and
More informationLecture 27. Phylogeny methods, part 4 (Models of DNA and protein change) p.1/26
Lecture 27. Phylogeny methods, part 4 (Models of DNA and protein change) Joe Felsenstein Department of Genome Sciences and Department of Biology Lecture 27. Phylogeny methods, part 4 (Models of DNA and
More informationLecture 4. Models of DNA and protein change. Likelihood methods
Lecture 4. Models of DNA and protein change. Likelihood methods Joe Felsenstein Department of Genome Sciences and Department of Biology Lecture 4. Models of DNA and protein change. Likelihood methods p.1/36
More informationMaximum 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
More informationProbabilistic modeling and molecular phylogeny
Probabilistic modeling and molecular phylogeny Anders Gorm Pedersen Molecular Evolution Group Center for Biological Sequence Analysis Technical University of Denmark (DTU) What is a model? Mathematical
More informationPhylogenetics: 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
More informationEstimating Phylogenies (Evolutionary Trees) II. Biol4230 Thurs, March 2, 2017 Bill Pearson Jordan 6-057
Estimating Phylogenies (Evolutionary Trees) II Biol4230 Thurs, March 2, 2017 Bill Pearson wrp@virginia.edu 4-2818 Jordan 6-057 Tree estimation strategies: Parsimony?no model, simply count minimum number
More informationInferring Molecular Phylogeny
Dr. Walter Salzburger he tree of life, ustav Klimt (1907) Inferring Molecular Phylogeny Inferring Molecular Phylogeny 55 Maximum Parsimony (MP): objections long branches I!! B D long branch attraction
More informationPhylogenetics: 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
More informationPHYLOGENY ESTIMATION AND HYPOTHESIS TESTING USING MAXIMUM LIKELIHOOD
Annu. Rev. Ecol. Syst. 1997. 28:437 66 Copyright c 1997 by Annual Reviews Inc. All rights reserved PHYLOGENY ESTIMATION AND HYPOTHESIS TESTING USING MAXIMUM LIKELIHOOD John P. Huelsenbeck Department of
More informationPreliminaries. Download PAUP* from: Tuesday, July 19, 16
Preliminaries Download PAUP* from: http://people.sc.fsu.edu/~dswofford/paup_test 1 A model of the Boston T System 1 Idea from Paul Lewis A simpler model? 2 Why do models matter? Model-based methods including
More informationLab 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*
More informationWeek 5: Distance methods, DNA and protein models
Week 5: Distance methods, DNA and protein models Genome 570 February, 2016 Week 5: Distance methods, DNA and protein models p.1/69 A tree and the expected distances it predicts E A 0.08 0.05 0.06 0.03
More informationBMI/CS 776 Lecture 4. Colin Dewey
BMI/CS 776 Lecture 4 Colin Dewey 2007.02.01 Outline Common nucleotide substitution models Directed graphical models Ancestral sequence inference Poisson process continuous Markov process X t0 X t1 X t2
More informationSome 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
More informationSubstitution = Mutation followed. by Fixation. Common Ancestor ACGATC 1:A G 2:C A GAGATC 3:G A 6:C T 5:T C 4:A C GAAATT 1:G A
GAGATC 3:G A 6:C T Common Ancestor ACGATC 1:A G 2:C A Substitution = Mutation followed 5:T C by Fixation GAAATT 4:A C 1:G A AAAATT GAAATT GAGCTC ACGACC Chimp Human Gorilla Gibbon AAAATT GAAATT GAGCTC ACGACC
More informationPhylogenetics: Distance Methods. COMP Spring 2015 Luay Nakhleh, Rice University
Phylogenetics: Distance Methods COMP 571 - Spring 2015 Luay Nakhleh, Rice University Outline Evolutionary models and distance corrections Distance-based methods Evolutionary Models and Distance Correction
More informationPhylogenetics: Likelihood
1 Phylogenetics: Likelihood COMP 571 Luay Nakhleh, Rice University The Problem 2 Input: Multiple alignment of a set S of sequences Output: Tree T leaf-labeled with S Assumptions 3 Characters are mutually
More informationLecture 4. Models of DNA and protein change. Likelihood methods
Lecture 4. Models of DNA and protein change. Likelihood methods Joe Felsenstein Department of Genome Sciences and Department of Biology Lecture 4. Models of DNA and protein change. Likelihood methods p.1/39
More informationConsensus Methods. * You are only responsible for the first two
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 informationMolecular Evolution, course # Final Exam, May 3, 2006
Molecular Evolution, course #27615 Final Exam, May 3, 2006 This exam includes a total of 12 problems on 7 pages (including this cover page). The maximum number of points obtainable is 150, and at least
More informationAdditive 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
More informationHow should we go about modeling this? Model parameters? Time Substitution rate Can we observe time or subst. rate? What can we observe?
How should we go about modeling this? gorilla GAAGTCCTTGAGAAATAAACTGCACACACTGG orangutan GGACTCCTTGAGAAATAAACTGCACACACTGG Model parameters? Time Substitution rate Can we observe time or subst. rate? What
More informationPhylogenetic Assumptions
Substitution Models and the Phylogenetic Assumptions Vivek Jayaswal Lars S. Jermiin COMMONWEALTH OF AUSTRALIA Copyright htregulation WARNING This material has been reproduced and communicated to you by
More informationConsensus 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
More informationConstructing 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
More informationPhylogeny Estimation and Hypothesis Testing using Maximum Likelihood
Phylogeny Estimation and Hypothesis Testing using Maximum Likelihood For: Prof. Partensky Group: Jimin zhu Rama Sharma Sravanthi Polsani Xin Gong Shlomit klopman April. 7. 2003 Table of Contents Introduction...3
More informationPOPULATION 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
More informationConstructing 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
More informationIntegrative 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
More informationAmira 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
More informationC.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
More informationSpecial features of phangorn (Version 2.3.1)
Special features of phangorn (Version 2.3.1) Klaus P. Schliep November 1, 2017 Introduction This document illustrates some of the phangorn [4] specialised features which are useful but maybe not as well-known
More informationLecture Notes: Markov chains
Computational Genomics and Molecular Biology, Fall 5 Lecture Notes: Markov chains Dannie Durand At the beginning of the semester, we introduced two simple scoring functions for pairwise alignments: a similarity
More informationDr. 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
More informationPhylogenetic Trees. What They Are Why We Do It & How To Do It. Presented by Amy Harris Dr Brad Morantz
Phylogenetic Trees What They Are Why We Do It & How To Do It Presented by Amy Harris Dr Brad Morantz Overview What is a phylogenetic tree Why do we do it How do we do it Methods and programs Parallels
More informationNatural selection on the molecular level
Natural selection on the molecular level Fundamentals of molecular evolution How DNA and protein sequences evolve? Genetic variability in evolution } Mutations } forming novel alleles } Inversions } change
More informationLie Markov models. Jeremy Sumner. School of Physical Sciences University of Tasmania, Australia
Lie Markov models Jeremy Sumner School of Physical Sciences University of Tasmania, Australia Stochastic Modelling Meets Phylogenetics, UTAS, November 2015 Jeremy Sumner Lie Markov models 1 / 23 The theory
More informationEvolutionary Genomics: Phylogenetics
Evolutionary Genomics: Phylogenetics Nicolas Salamin nicolas.salamin@unil.ch Department of Computational Biology, University of Lausanne Swiss Institute of Bioinformatics March 16th, 2017 Tree of Life
More informationPhylogenetic Inference and Hypothesis Testing. Catherine Lai (92720) BSc(Hons) Department of Mathematics and Statistics University of Melbourne
Phylogenetic Inference and Hypothesis Testing Catherine Lai (92720) BSc(Hons) Department of Mathematics and Statistics University of Melbourne November 13, 2003 Contents 1 Introduction 4 2 Molecular Phylogenetics
More informationEvolutionary Analysis of Viral Genomes
University of Oxford, Department of Zoology Evolutionary Biology Group Department of Zoology University of Oxford South Parks Road Oxford OX1 3PS, U.K. Fax: +44 1865 271249 Evolutionary Analysis of Viral
More informationConcepts 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
More informationTaming the Beast Workshop
Workshop David Rasmussen & arsten Magnus June 27, 2016 1 / 31 Outline of sequence evolution: rate matrices Markov chain model Variable rates amongst different sites: +Γ Implementation in BES2 2 / 31 genotype
More informationPhylogenetic Inference using RevBayes
Phylogenetic Inference using RevBayes Model section using Bayes factors Sebastian Höhna 1 Overview This tutorial demonstrates some general principles of Bayesian model comparison, which is based on estimating
More informationPhylogenetics: Parsimony and Likelihood. COMP Spring 2016 Luay Nakhleh, Rice University
Phylogenetics: Parsimony and Likelihood COMP 571 - Spring 2016 Luay Nakhleh, Rice University The Problem Input: Multiple alignment of a set S of sequences Output: Tree T leaf-labeled with S Assumptions
More informationPhylogenetics. 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
More informationReconstruire le passé biologique modèles, méthodes, performances, limites
Reconstruire le passé biologique modèles, méthodes, performances, limites Olivier Gascuel Centre de Bioinformatique, Biostatistique et Biologie Intégrative C3BI USR 3756 Institut Pasteur & CNRS Reconstruire
More informationIntroduction to MEGA
Introduction to MEGA Download at: http://www.megasoftware.net/index.html Thomas Randall, PhD tarandal@email.unc.edu Manual at: www.megasoftware.net/mega4 Use of phylogenetic analysis software tools Bioinformatics
More informationSome 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
More informationPhylogenetics. 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
More informationChapter 7: Models of discrete character evolution
Chapter 7: Models of discrete character evolution pdf version R markdown to recreate analyses Biological motivation: Limblessness as a discrete trait Squamates, the clade that includes all living species
More informationBioinformatics 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
More informationEVOLUTIONARY DISTANCES
EVOLUTIONARY DISTANCES FROM STRINGS TO TREES Luca Bortolussi 1 1 Dipartimento di Matematica ed Informatica Università degli studi di Trieste luca@dmi.units.it Trieste, 14 th November 2007 OUTLINE 1 STRINGS:
More informationMOLECULAR 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
More information1. Can we use the CFN model for morphological traits?
1. Can we use the CFN model for morphological traits? 2. Can we use something like the GTR model for morphological traits? 3. Stochastic Dollo. 4. Continuous characters. Mk models k-state variants of the
More informationMassachusetts Institute of Technology Computational Evolutionary Biology, Fall, 2005 Notes for November 7: Molecular evolution
Massachusetts Institute of Technology 6.877 Computational Evolutionary Biology, Fall, 2005 Notes for November 7: Molecular evolution 1. Rates of amino acid replacement The initial motivation for the neutral
More informationDiscrete & continuous characters: The threshold model
Discrete & continuous characters: The threshold model Discrete & continuous characters: the threshold model So far we have discussed continuous & discrete character models separately for estimating ancestral
More information"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 Feb. 1, 2011. Qualitative character evolution (cont.) - comparing
More informationMaximum Likelihood in Phylogenetics
Maximum Likelihood in Phylogenetics June 1, 2009 Smithsonian Workshop on Molecular Evolution Paul O. Lewis Department of Ecology & Evolutionary Biology University of Connecticut, Storrs, CT Copyright 2009
More informationIntegrative 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;
More informationAn Introduction to Bayesian Inference of Phylogeny
n Introduction to Bayesian Inference of Phylogeny John P. Huelsenbeck, Bruce Rannala 2, and John P. Masly Department of Biology, University of Rochester, Rochester, NY 427, U.S.. 2 Department of Medical
More informationWho was Bayes? Bayesian Phylogenetics. What is Bayes Theorem?
Who was Bayes? Bayesian Phylogenetics Bret Larget Departments of Botany and of Statistics University of Wisconsin Madison October 6, 2011 The Reverand Thomas Bayes was born in London in 1702. He was the
More informationBayesian Phylogenetics
Bayesian Phylogenetics Bret Larget Departments of Botany and of Statistics University of Wisconsin Madison October 6, 2011 Bayesian Phylogenetics 1 / 27 Who was Bayes? The Reverand Thomas Bayes was born
More informationThe Idiot s Guide to the Zen of Likelihood in a Nutshell in Seven Days for Dummies, Unleashed
The Idiot s Guide to the Zen of Likelihood in a Nutshell in Seven Days for Dummies, Unleashed A gentle introduction, for those of us who are small of brain, to the calculation of the likelihood of molecular
More informationT R K V CCU CG A AAA GUC T R K V CCU CGG AAA GUC. T Q K V CCU C AG AAA GUC (Amino-acid
Lecture 11 Increasing Model Complexity I. Introduction. At this point, we ve increased the complexity of models of substitution considerably, but we re still left with the assumption that rates are uniform
More informationTree 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
More informationPhylogenetic 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
More informationPhylogenetic Inference using RevBayes
Phylogenetic Inference using RevBayes Substitution Models Sebastian Höhna 1 Overview This tutorial demonstrates how to set up and perform analyses using common nucleotide substitution models. The substitution
More informationMutation models I: basic nucleotide sequence mutation models
Mutation models I: basic nucleotide sequence mutation models Peter Beerli September 3, 009 Mutations are irreversible changes in the DNA. This changes may be introduced by chance, by chemical agents, or
More informationInferring Complex DNA Substitution Processes on Phylogenies Using Uniformization and Data Augmentation
Syst Biol 55(2):259 269, 2006 Copyright c Society of Systematic Biologists ISSN: 1063-5157 print / 1076-836X online DOI: 101080/10635150500541599 Inferring Complex DNA Substitution Processes on Phylogenies
More informationLetter 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
More informationLecture 4: Evolutionary Models and Substitution Matrices (PAM and BLOSUM)
Bioinformatics II Probability and Statistics Universität Zürich and ETH Zürich Spring Semester 2009 Lecture 4: Evolutionary Models and Substitution Matrices (PAM and BLOSUM) Dr Fraser Daly adapted from
More informationMolecular Evolution and Comparative Genomics
Molecular Evolution and Comparative Genomics --- the phylogenetic HMM model 10-810, CMB lecture 5---Eric Xing Some important dates in history (billions of years ago) Origin of the universe 15 ±4 Formation
More informationWhat Is Conservation?
What Is Conservation? Lee A. Newberg February 22, 2005 A Central Dogma Junk DNA mutates at a background rate, but functional DNA exhibits conservation. Today s Question What is this conservation? Lee A.
More informationThe Phylo- HMM approach to problems in comparative genomics, with examples.
The Phylo- HMM approach to problems in comparative genomics, with examples. Keith Bettinger Introduction The theory of evolution explains the diversity of organisms on Earth by positing that earlier species
More informationMolecular Evolution and Phylogenetic Tree Reconstruction
1 4 Molecular Evolution and Phylogenetic Tree Reconstruction 3 2 5 1 4 2 3 5 Orthology, Paralogy, Inparalogs, Outparalogs Phylogenetic Trees Nodes: species Edges: time of independent evolution Edge length
More informationMaximum Likelihood in Phylogenetics
Maximum Likelihood in Phylogenetics 26 January 2011 Workshop on Molecular Evolution Český Krumlov, Česká republika Paul O. Lewis Department of Ecology & Evolutionary Biology University of Connecticut,
More information"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
More informationInferring phylogeny. Today s topics. Milestones of molecular evolution studies Contributions to molecular evolution
Today s topics Inferring phylogeny Introduction! Distance methods! Parsimony method!"#$%&'(!)* +,-.'/01!23454(6!7!2845*0&4'9#6!:&454(6 ;?@AB=C?DEF Overview of phylogenetic inferences Methodology Methods
More informationUsing 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
More informationPhylogenetics and Darwin. An Introduction to Phylogenetics. Tree of Life. Darwin s Trees
Phylogenetics and Darwin An Introduction to Phylogenetics Bret Larget larget@stat.wisc.edu Departments of Botany and of Statistics University of Wisconsin Madison February 4, 2008 A phylogeny is a tree
More informationLecture 4: Evolutionary models and substitution matrices (PAM and BLOSUM).
1 Bioinformatics: In-depth PROBABILITY & STATISTICS Spring Semester 2011 University of Zürich and ETH Zürich Lecture 4: Evolutionary models and substitution matrices (PAM and BLOSUM). Dr. Stefanie Muff
More information"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
More informationEfficiencies of maximum likelihood methods of phylogenetic inferences when different substitution models are used
Molecular Phylogenetics and Evolution 31 (2004) 865 873 MOLECULAR PHYLOGENETICS AND EVOLUTION www.elsevier.com/locate/ympev Efficiencies of maximum likelihood methods of phylogenetic inferences when different
More informationLecture 6 Phylogenetic Inference
Lecture 6 Phylogenetic Inference From Darwin s notebook in 1837 Charles Darwin Willi Hennig From The Origin in 1859 Cladistics Phylogenetic inference Willi Hennig, Cladistics 1. Clade, Monophyletic group,
More information7. 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
More informationKaKs Calculator: Calculating Ka and Ks Through Model Selection and Model Averaging
Method KaKs Calculator: Calculating Ka and Ks Through Model Selection and Model Averaging Zhang Zhang 1,2,3#, Jun Li 2#, Xiao-Qian Zhao 2,3, Jun Wang 1,2,4, Gane Ka-Shu Wong 2,4,5, and Jun Yu 1,2,4 * 1
More informationHMM for modeling aligned multiple sequences: phylo-hmm & multivariate HMM
I529: Machine Learning in Bioinformatics (Spring 2017) HMM for modeling aligned multiple sequences: phylo-hmm & multivariate HMM Yuzhen Ye School of Informatics and Computing Indiana University, Bloomington
More informationBayesian Models for Phylogenetic Trees
Bayesian Models for Phylogenetic Trees Clarence Leung* 1 1 McGill Centre for Bioinformatics, McGill University, Montreal, Quebec, Canada ABSTRACT Introduction: Inferring genetic ancestry of different species
More informationA Bayesian Approach to Phylogenetics
A Bayesian Approach to Phylogenetics Niklas Wahlberg Based largely on slides by Paul Lewis (www.eeb.uconn.edu) An Introduction to Bayesian Phylogenetics Bayesian inference in general Markov chain Monte
More informationHomoplasy. 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,
More informationAssessing 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
More informationBayesian Analysis of Elapsed Times in Continuous-Time Markov Chains
Bayesian Analysis of Elapsed Times in Continuous-Time Markov Chains Marco A. R. Ferreira 1, Marc A. Suchard 2,3,4 1 Department of Statistics, University of Missouri at Columbia, USA 2 Department of Biomathematics
More informationAre Guinea Pigs Rodents? The Importance of Adequate Models in Molecular Phylogenetics
Journal of Mammalian Evolution, Vol. 4, No. 2, 1997 Are Guinea Pigs Rodents? The Importance of Adequate Models in Molecular Phylogenetics Jack Sullivan1'2 and David L. Swofford1 The monophyly of Rodentia
More informationInferring Speciation Times under an Episodic Molecular Clock
Syst. Biol. 56(3):453 466, 2007 Copyright c Society of Systematic Biologists ISSN: 1063-5157 print / 1076-836X online DOI: 10.1080/10635150701420643 Inferring Speciation Times under an Episodic Molecular
More informationThe Phylogenetic Handbook
The Phylogenetic Handbook A Practical Approach to DNA and Protein Phylogeny Edited by Marco Salemi University of California, Irvine and Katholieke Universiteit Leuven, Belgium and Anne-Mieke Vandamme Rega
More informationBiology 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:
More informationMixture Models in Phylogenetic Inference. Mark Pagel and Andrew Meade Reading University.
Mixture Models in Phylogenetic Inference Mark Pagel and Andrew Meade Reading University m.pagel@rdg.ac.uk Mixture models in phylogenetic inference!some background statistics relevant to phylogenetic inference!mixture
More informationWeek 8: Testing trees, Bootstraps, jackknifes, gene frequencies
Week 8: Testing trees, ootstraps, jackknifes, gene frequencies Genome 570 ebruary, 2016 Week 8: Testing trees, ootstraps, jackknifes, gene frequencies p.1/69 density e log (density) Normal distribution:
More informationMolecular Evolution & Phylogenetics Traits, phylogenies, evolutionary models and divergence time between sequences
Molecular Evolution & Phylogenetics Traits, phylogenies, evolutionary models and divergence time between sequences Basic Bioinformatics Workshop, ILRI Addis Ababa, 12 December 2017 1 Learning Objectives
More information