Inference of phylogenies, with some thoughts on statistics and geometry p.1/31
|
|
|
- Lucas King
- 5 years ago
- Views:
Transcription
1 Inference of phylogenies, with some thoughts on statistics and geometry Joe Felsenstein University of Washington Inference of phylogenies, with some thoughts on statistics and geometry p.1/31
2 Darwin s tree This is the only illustration in The Origin of Species Inference of phylogenies, with some thoughts on statistics and geometry p.2/31
3 One of Ernst Haeckel s trees (1866) (Bark! on some of his trees, even leaves!) He coined the word phylogeny". Inference of phylogenies, with some thoughts on statistics and geometry p.3/31
4 George Gaylord Simpson, 1940 s Structure starts to get imprecise. Far from algorithms. Inference of phylogenies, with some thoughts on statistics and geometry p.4/31
5 lgorithmic methods for inferring phylogenies Starting in the 1960 s, biologists invented a number of methods to infer phylogenies: Parsimony methods. Find that tree that minimizes the number of changes of state needed to evolve the observed data. (Related: compatibility methods) Distance matrix methods. Measure inferred distances between all pairs of species. Find the tree, with branch lengths, that predicts this table best. Maximum likelihood methods. Use a probability model of evolution (also used in distance matrix methods) and find that tree that makes the observed data most probable. (Related: Bayesian inference, which assumes also prior probabilities for the possible trees). Inference of phylogenies, with some thoughts on statistics and geometry p.5/31
6 Parsimony methods Two ways of looking at the parsimony method: Species are points in a sequence space. Find the Steiner tree for these points in this space. Trees live in a tree space; for each of them we can evaluate how many changes of state are needed to evolve the data set. Inference of phylogenies, with some thoughts on statistics and geometry p.6/31
7 Parsimony on a simple data set site species lpha T G G C Beta C T C T C Gamma G G T C Delta G G G T Epsilon C T C G C Here is the most parsimonious tree (drawn as if rooted, though root location doesn t matter for the parsimony score in this case) Gamma 5 4 Delta 6 lpha Beta 5 4 Epsilon Inference of phylogenies, with some thoughts on statistics and geometry p.7/31
8 Distance matrix methods E 0.05 C B D B C D E B C D E This table of expected distances is computed from the tree and compared to pairwise distances (branch lengths of a 2-species unrooted tree) computed from the data. Inference of phylogenies, with some thoughts on statistics and geometry p.8/31
9 Likelihood methods C C G t 1 t 2 y C t 3 t 4 t 5 w t t 6 z 7 x t 8 Likelihood is the probability of the data given the tree, where the tree has branch lengths, and a model of evolution (occurring independently in each site and independently in different lineages) Inference of phylogenies, with some thoughts on statistics and geometry p.9/31
10 Likelihood computed separately in each site: The likelihood is the product over sites (as they are independent given the tree): L = Prob (D T) = m Prob ( ) D (i) T i=1 For site i, summing over all possible states at interior nodes of the tree: Prob ( ) D (i) T = x y z w Prob (, C, C, C, G, x, y, z, w T) Inference of phylogenies, with some thoughts on statistics and geometry p.10/31
11 Using independence assumptions to simplify By independence of events in different branches (conditional only on their starting states): Prob (, C, C, C, G, x, y, z, w T) = Prob (x) Prob (y x, t 6 ) Prob ( y, t 1 ) Prob (C y, t 2 ) Prob (z x, t 8 ) Prob (C z, t 3 ) Prob (w z, t 7 ) Prob (C w, t 4 ) Prob (G w, t 5 ) There are, of course, numerical optimization issues in finding the best branch lengths for each topology examined. Inference of phylogenies, with some thoughts on statistics and geometry p.11/31
12 n example: Turbeville et al., 28s RN, chordates Xenopus?TCCTGGTTGTCCTGCCGTG-CTTG... Sebastol??????????????????????G-CTTG... Latimeri?TCCTGGTTGTCCTGCCGTG-CTTG... Squalus??????????????????????G-CTTG... Myxine Petromyz??CCCTGGTTGTCCTGCCGCCG-CTTG...???CCTGGTTGTCCTGCCGTG-CTTG... Branch Styela???CCTGGTTGTCCTGCCGTGTCTTG...??TCTGGTTGTCCTGCCGTGTGTTG... Herdman Saccogl?TTCTGGTTGTCCTGCCGTGTGTTG...??CCTGGTTGTCCTGCCGTGTCTTG... Ophiophol??CCTGGTTGTCCTGCCGTGTCTTG... Strongyl??CCTGGTTGTCCTGCCGTGTCTTG... Placopec CCCTGGTTGTCCTGCCGTGTCTTG... Limicol Eurypelm?TTCTGGTTGTCCTGCCGTGTCTTG...?TCCTGGTTGTCCTGCCGTGTCTTG... Tenebrio?TCCCTGGTTGTCCTGCCGTGTCTTG... (and so on for 11 more pages) Inference of phylogenies, with some thoughts on statistics and geometry p.12/31
13 The tree with parsimony (acorn worms) Saccogl Ophiophol (echinoderms) Strongyl Herdman (sea squirts) Styela Branchiostoma (amphioxus) Placopec (molluscs) Limicol Petromyz(lamprey) (insects) Eurypelm Tenebrio Latimeri (coelacanth) Squalus(dogfish) Sebastol(fish) Xenopus (frog) Myxine (hagfish) Inference of phylogenies, with some thoughts on statistics and geometry p.13/31
14 The tree with the Fitch-Margoliash distance matrix method Ophiophol (echinoderms) Strongyl (acorn worms) Saccogl Herdman (sea squirts) Styela Branch (amphioxus) Myxine (hagfish) Placopec (molluscs) Limicol Eurypelm (insects) Tenebrio Petromyz Xenopus (lamprey) Squalus Sebastol Latimeri (frog) (dogfish) (fish) (coelacanth) Inference of phylogenies, with some thoughts on statistics and geometry p.14/31
15 The tree with maximum likelihood Herdmanni (sea squirts) (acorn worm) Styela Saccoglossus Petromyzon (lamprey) Ophiopholes (echinoderms) Strongylocentrotus Placopecten Squalus (dogfish) Sebastoles (rockfish) Xenopus (frog) Latimeria (coelacanth) Myxine (hagfish) (molluscs) Branchiotoma Limicol Eurypelm Tenebrio (insects) (amphioxus) Inference of phylogenies, with some thoughts on statistics and geometry p.15/31
16 The bootstrap shows the uncertainty in our trees Original Data sites sequences Bootstrap sample #1 sequences sites sample same number of sites, with replacement Estimate of the tree Bootstrap sample #2 sites Bootstrap estimate of the tree, #1 sequences sample same number of sites, with replacement (and so on) Bootstrap estimate of the tree, #2 Inference of phylogenies, with some thoughts on statistics and geometry p.16/31
17 Phylogenies can be seen as graphical models We have observations at a number of characters (wuch as sites in DN molecules). Each site has a graphical model and is independently evolving on the same tree (by assumption). The graphical model looks like this: x x x x x x x x x x x x x x x v 1 x x x x x v 5 v 6 v 4 x x x x x x x x x x x x x x x v 3 v 8 v 7 x x x x x v 9 x x x x x Imagine a version of this 500 or so sites deep. Problem: given observations on the x s at the tips, to reconstruct the tree topology and the v s. Inference of phylogenies, with some thoughts on statistics and geometry p.17/31
18 Invariants (a method with a future?) With DN data and 4 species there are 256 data patterns possible: through TTTT. With the simplest symmetric model (the Jukes-Cantor model) of nucleotide change, the tree has 5 parameters (the branch lengths These 5 parameters define a 5-dimensional space that defines where in the space of expected pattern frequency the trees are. This leaves 250 degrees of freedom to account for: there are 250 equations the expected pattern frequencies must satisfy. Many are simple symmetry requirements such as f = f CCCC a few are phylogenetic invariants that depend on the tree topology. Inference of phylogenies, with some thoughts on statistics and geometry p.18/31
19 Mathematical approaches to trees that have been useful The analogy between trees and recursion. This was discovered early by many mathematically-oriented biologists. Invariants (J. Cavender and J. Lake). Holds promise for the future but so far not of great practical use. Lots of geometry here? The Hadamard conjugation (M. Hendy / D. Penny). Remarkable results, but so far confined to certain symmetric models of base change. But on the whole, this is a short list. We are waiting for a mathematical result that makes a real impact, rather than restating the obvious or just rigorizing something we already use. Inference of phylogenies, with some thoughts on statistics and geometry p.19/31
20 Tree space as explained by me a few years ago The space of trees with branch lengths an example: three species with a clock B C trifurcation t 1 t 2 t 1 not possible OK etc. t 2 when we consider all three possible topologies, the space looks like: t 1 2 t1 Inference of phylogenies, with some thoughts on statistics and geometry p.20/31
21 This graph, arranged by my stepson Ben Schoenberg ( appeared on the 1999 T-shirt of the Molecular Evolution Workshop, Marine Biological Laboratory, Woods Inference of phylogenies, with some thoughts on statistics and geometry p.21/31 Hole, Massachusetts. Tree space, for 5 species C D B E D B E C B C E D E B C D B C D E B D C E B C D E B C E D B D C E D B C E C B D E C B D E E B D C E C B D D B E C
22 dual Ben also noticed that if we take the cliques (3 trees each) of the NNI graph, and make a dual in which the points are cliques, and the lines connect them when they share a tree, we get (ta-da!) The infamous Petersen Graph. Inference of phylogenies, with some thoughts on statistics and geometry p.22/31
23 shamrock", part of the NNI graph for 6 species B B C F C F D E D E C D B E F B E F C D E F C D B D C F E B E F Each tree of shape ((x,x),(x,x),(x,x)) is surrounded by 6 trees of shape (((x,x),x),(x,(x,x))). There are 15 of these shamrocks, 105 trees in all. C D B Inference of phylogenies, with some thoughts on statistics and geometry p.23/31
24 More about the 6-species NNI graph Each shamrock connects from each leaf to two others. The 6 shamrocks that each shamrock connects to are the ones that share at least one of its two-species sets. Inference of phylogenies, with some thoughts on statistics and geometry p.24/31
25 test case: 3 species with clock, Jukes-Cantor model The data set (variant nucleotides lower-cased): 3 64 lpha gtcacgtcgtcgtcgtcgtcgtcgt Beta CGTgtcaCGTgtcagtcaCGTCGTCGT Gamma CGTCGTgtcaCGTCGTgtcagtcagtca B C CGTCGTCGTCGTCGTCGTCGTCGT CGTCGTCGTCGTCGTCGTCGTCGT CGTCGTCGTCGTCGTCGTCGTCGT y x y x+y Inference of phylogenies, with some thoughts on statistics and geometry p.25/31
26 ln likelihood This leads to these profile likelihoods C B x B C 196 x x B C x x (Profile likelihoods are max y ln L(x, y) ) Inference of phylogenies, with some thoughts on statistics and geometry p.26/31
27 The branch score (Kuhner and Felsenstein, 1994) F D B D B C F E 0.2 E 0.2 G G C partitions {D BCEFG} {DF BCEG} {BC DEFG} {DF BCEG} {EG BCDF} { BCDEFG} {B CDEFG} {C BDEFG} {D BCEFG} {E BCDFG} {F BCDEG} {G BCDEF} branch lengths none none 0.1 none Inference of phylogenies, with some thoughts on statistics and geometry p.27/31
28 The perfect tree distance Can we make a tree distance with perfect statistical meaning? What would that look like if we did? The (squared?) distance would reflect the difference of log likelihood between the two trees: D 2 = patterns i ( ) n i n ln pi q i where the p i and the q i are the expected frequencies of the possible data patterns in the two trees, and the n i are the numbers of each pattern seen. (This formula is related to the Kullback-Leibler separator but is slightly different because it has the data in it, and is not just a function of the two trees.) I don t see any way to make a distance that magically reflects the data, without using the data. Inference of phylogenies, with some thoughts on statistics and geometry p.28/31
29 n example of the problem: primates data Lemur Bovine Human Chimp Gorilla Orang Gibbon BarbMacaq Crab E.Mac Rhesus Mac Jpn Macaq Tarsier Squir Monk Mouse Tree from 232 nucleotides of mitochondrial DN data Inference of phylogenies, with some thoughts on statistics and geometry p.29/31
30 Bootstrap sampling finds these partition frequencies: Bovine Mouse Squir Monk Chimp Human Gorilla Orang Gibbon Rhesus Mac Jpn Macaq Crab E.Mac BarbMacaq Tarsier Lemur (drawing the tree as if rooted) Inference of phylogenies, with some thoughts on statistics and geometry p.30/31
31 How to get distance-ish methods to use data? modest proposal Use the cloud of bootstrap trees, jackknife trees, or posterior distribution of trees in a Bayesian analysis. The variation in these accurately reflects the uncertainty in the data. Can we somehow use this variation to evaluate which directions in tree space our data allows us to go? Inference of phylogenies, with some thoughts on statistics and geometry p.31/31
32 References urbeville. J. McC., Schulz, J.R. and R.. Raff Deuterostome phylogeny and the sister group of the chordates: evidence from molecules and morphology. Molecular Biology and Evolution Inference of phylogenies, with some thoughts on statistics and geometry p.32/31
33 How it was done This projection produced as a PDF, not a PowerPoint file, and viewed using the Full Screen mode (in the View menu of dobe crobat Reader): using the prosper style in LaTeX, using Latex to make a.dvi file, using dvips to turn this into a Postscript file, using ps2pdf to mill it into a PDF file, and displaying the slides in dobe crobat Reader. Result: nice slides using freeware. Inference of phylogenies, with some thoughts on statistics and geometry p.33/31
Molecular evolution. Joe Felsenstein. GENOME 453, Autumn Molecular evolution p.1/49
Molecular evolution Joe Felsenstein GENOME 453, utumn 2009 Molecular evolution p.1/49 data example for phylogeny inference Five DN sequences, for some gene in an imaginary group of species whose names
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
Week 7: Bayesian inference, Testing trees, Bootstraps
Week 7: ayesian inference, Testing trees, ootstraps Genome 570 May, 2008 Week 7: ayesian inference, Testing trees, ootstraps p.1/54 ayes Theorem onditional probability of hypothesis given data is: Prob
Bootstraps and testing trees. Alog-likelihoodcurveanditsconfidenceinterval
ootstraps and testing trees Joe elsenstein epts. of Genome Sciences and of iology, University of Washington ootstraps and testing trees p.1/20 log-likelihoodcurveanditsconfidenceinterval 2620 2625 ln L
Week 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:
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
Lecture 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
Lecture 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
History of Genetics in Evolution
History of Genetics in Evolution Joe Felsenstein GENOME 453, Winter 2005 History of Genetics in Evolution p.1/39 The Great Chain of Being (1600 s onward) Deity Angels Man Mammals Birds Reptiles Amphibians
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
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
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
Summary statistics, distributions of sums and means
Summary statistics, distributions of sums and means Joe Felsenstein Summary statistics, distributions of sums and means p.1/17 Quantiles In both empirical distributions and in the underlying distribution,
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. w(e), where P ij is the path in T from i to j. Then the matrix [D ij ] is said to be additive.](/thumbs/92/110439366.jpg "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
AN ALTERNATING LEAST SQUARES APPROACH TO INFERRING PHYLOGENIES FROM PAIRWISE DISTANCES
Syst. Biol. 46(l):11-lll / 1997 AN ALTERNATING LEAST SQUARES APPROACH TO INFERRING PHYLOGENIES FROM PAIRWISE DISTANCES JOSEPH FELSENSTEIN Department of Genetics, University of Washington, Box 35736, Seattle,
EVOLUTIONARY 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:
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
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
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
X X (2) X Pr(X = x θ) (3)
Notes for 848 lecture 6: A ML basis for compatibility and parsimony Notation θ Θ (1) Θ is the space of all possible trees (and model parameters) θ is a point in the parameter space = a particular tree
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
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
Statistical nonmolecular phylogenetics: can molecular phylogenies illuminate morphological evolution?
Statistical nonmolecular phylogenetics: can molecular phylogenies illuminate morphological evolution? 30 July 2011. Joe Felsenstein Workshop on Molecular Evolution, MBL, Woods Hole Statistical nonmolecular
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
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
Inferring 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
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
Reconstruire 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
"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
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
Estimating 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
1. 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
Lecture 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
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
Week 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
Evolutionary Models. Evolutionary Models
Edit Operators In standard pairwise alignment, what are the allowed edit operators that transform one sequence into the other? Describe how each of these edit operations are represented on a sequence alignment
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
Phylogenetic Analysis. Han Liang, Ph.D. Assistant Professor of Bioinformatics and Computational Biology UT MD Anderson Cancer Center
Phylogenetic Analysis Han Liang, Ph.D. Assistant Professor of Bioinformatics and Computational Biology UT MD Anderson Cancer Center Outline Basic Concepts Tree Construction Methods Distance-based methods
Phylogene)cs. IMBB 2016 BecA- ILRI Hub, Nairobi May 9 20, Joyce Nzioki
Phylogene)cs IMBB 2016 BecA- ILRI Hub, Nairobi May 9 20, 2016 Joyce Nzioki Phylogenetics The study of evolutionary relatedness of organisms. Derived from two Greek words:» Phle/Phylon: Tribe/Race» Genetikos:
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
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)
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
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
Phylogeny Tree Algorithms
Phylogeny Tree lgorithms Jianlin heng, PhD School of Electrical Engineering and omputer Science University of entral Florida 2006 Free for academic use. opyright @ Jianlin heng & original sources for some
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,
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;
Molecular 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
Phylogenetics: 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
Thanks to Paul Lewis and Joe Felsenstein for the use of slides
Thanks to Paul Lewis and Joe Felsenstein for the use of slides Review Hennigian logic reconstructs the tree if we know polarity of characters and there is no homoplasy UPGMA infers a tree from a distance
Week 6: Restriction sites, RAPDs, microsatellites, likelihood, hidden Markov models
Week 6: Restriction sites, RAPDs, microsatellites, likelihood, hidden Markov models Genome 570 February, 2012 Week 6: Restriction sites, RAPDs, microsatellites, likelihood, hidden Markov models p.1/63
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
Phylogeny. Properties of Trees. Properties of Trees. Trees represent the order of branching only. Phylogeny: Taxon: a unit of classification
Multiple sequence alignment global local Evolutionary tree reconstruction Pairwise sequence alignment (global and local) Substitution matrices Gene Finding Protein structure prediction N structure prediction
BMI/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
How to read and make phylogenetic trees Zuzana Starostová
How to read and make phylogenetic trees Zuzana Starostová How to make phylogenetic trees? Workflow: obtain DNA sequence quality check sequence alignment calculating genetic distances phylogeny estimation
Using algebraic geometry for phylogenetic reconstruction
Using algebraic geometry for phylogenetic reconstruction Marta Casanellas i Rius (joint work with Jesús Fernández-Sánchez) Departament de Matemàtica Aplicada I Universitat Politècnica de Catalunya IMA
CS5263 Bioinformatics. Guest Lecture Part II Phylogenetics
CS5263 Bioinformatics Guest Lecture Part II Phylogenetics Up to now we have focused on finding similarities, now we start focusing on differences (dissimilarities leading to distance measures). Identifying
Evolutionary trees. Describe the relationship between objects, e.g. species or genes
Evolutionary trees Bonobo Chimpanzee Human Neanderthal Gorilla Orangutan Describe the relationship between objects, e.g. species or genes Early evolutionary studies The evolutionary relationships between
Consistency Index (CI)
Consistency Index (CI) minimum number of changes divided by the number required on the tree. CI=1 if there is no homoplasy negatively correlated with the number of species sampled Retention Index (RI)
Substitution = 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
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
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
Phylogenetics: 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
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
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*
Phylogenetic Trees. Phylogenetic Trees Five. Phylogeny: Inference Tool. Phylogeny Terminology. Picture of Last Quagga. Importance of Phylogeny 5.
Five Sami Khuri Department of Computer Science San José State University San José, California, USA sami.khuri@sjsu.edu v Distance Methods v Character Methods v Molecular Clock v UPGMA v Maximum Parsimony
Michael Yaffe Lecture #5 (((A,B)C)D) Database Searching & Molecular Phylogenetics A B C D B C D
7.91 Lecture #5 Database Searching & Molecular Phylogenetics Michael Yaffe B C D B C D (((,B)C)D) Outline Distance Matrix Methods Neighbor-Joining Method and Related Neighbor Methods Maximum Likelihood
Counting phylogenetic invariants in some simple cases. Joseph Felsenstein. Department of Genetics SK-50. University of Washington
Counting phylogenetic invariants in some simple cases Joseph Felsenstein Department of Genetics SK-50 University of Washington Seattle, Washington 98195 Running Headline: Counting Phylogenetic Invariants
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
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
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. Parsimony & Likelihood [draft]](/thumbs/83/87443456.jpg "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
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
Lecture 11 Friday, October 21, 2011
Lecture 11 Friday, October 21, 2011 Phylogenetic tree (phylogeny) Darwin and classification: In the Origin, Darwin said that descent from a common ancestral species could explain why the Linnaean system
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:
I. Short Answer Questions DO ALL QUESTIONS
EVOLUTION 313 FINAL EXAM Part 1 Saturday, 7 May 2005 page 1 I. Short Answer Questions DO ALL QUESTIONS SAQ #1. Please state and BRIEFLY explain the major objectives of this course in evolution. Recall
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
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,
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
TheDisk-Covering MethodforTree Reconstruction
TheDisk-Covering MethodforTree Reconstruction Daniel Huson PACM, Princeton University Bonn, 1998 1 Copyright (c) 2008 Daniel Huson. Permission is granted to copy, distribute and/or modify this document
Phylogenetic 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
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
Biology Keystone (PA Core) Quiz Theory of Evolution - (BIO.B ) Theory Of Evolution, (BIO.B ) Scientific Terms
Biology Keystone (PA Core) Quiz Theory of Evolution - (BIO.B.3.2.1 ) Theory Of Evolution, (BIO.B.3.3.1 ) Scientific Terms Student Name: Teacher Name: Jared George Date: Score: 1) Evidence for evolution
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
Algebraic Statistics Tutorial I
Algebraic Statistics Tutorial I Seth Sullivant North Carolina State University June 9, 2012 Seth Sullivant (NCSU) Algebraic Statistics June 9, 2012 1 / 34 Introduction to Algebraic Geometry Let R[p] =
How Molecules Evolve. Advantages of Molecular Data for Tree Building. Advantages of Molecular Data for Tree Building
How Molecules Evolve Guest Lecture: Principles and Methods of Systematic Biology 11 November 2013 Chris Simon Approaching phylogenetics from the point of view of the data Understanding how sequences evolve
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
Phylogeny and Evolution. Gina Cannarozzi ETH Zurich Institute of Computational Science
Phylogeny and Evolution Gina Cannarozzi ETH Zurich Institute of Computational Science History Aristotle (384-322 BC) classified animals. He found that dolphins do not belong to the fish but to the mammals.
Molecular 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
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
Likelihoods and Phylogenies
Likelihoods and Phylogenies Joe Felsenstein Department of enome Sciences and Department of Biology University of Washington, Seattle Likelihoods and Phylogenies p.1/68 n ideal parsimony method? Ideally,
arxiv: v1 [q-bio.pe] 4 Sep 2013
![arxiv: v1 [q-bio.pe] 4 Sep 2013](/thumbs/88/117441027.jpg "arxiv: v1 [q-bio.pe] 4 Sep 2013")
Version dated: September 5, 2013 Predicting ancestral states in a tree arxiv:1309.0926v1 [q-bio.pe] 4 Sep 2013 Predicting the ancestral character changes in a tree is typically easier than predicting the
Module 13: Molecular Phylogenetics
Module 13: Molecular Phylogenetics Instructors: Joe Felsenstein (University of Washington) Mark Holder (University of Kansas) Jeff Thorne (North Carolina State University) Wednesday July 18: Day I 1:30
Phylogenetic inference: from sequences to trees
W ESTFÄLISCHE W ESTFÄLISCHE W ILHELMS -U NIVERSITÄT NIVERSITÄT WILHELMS-U ÜNSTER MM ÜNSTER VOLUTIONARY FUNCTIONAL UNCTIONAL GENOMICS ENOMICS EVOLUTIONARY Bioinformatics 1 Phylogenetic inference: from sequences
Markov chain Monte-Carlo to estimate speciation and extinction rates: making use of the forest hidden behind the (phylogenetic) tree
Markov chain Monte-Carlo to estimate speciation and extinction rates: making use of the forest hidden behind the (phylogenetic) tree Nicolas Salamin Department of Ecology and Evolution University of Lausanne
Page 1. Evolutionary Trees. Why build evolutionary tree? Outline
Page Evolutionary Trees Russ. ltman MI S 7 Outline. Why build evolutionary trees?. istance-based vs. character-based methods. istance-based: Ultrametric Trees dditive Trees. haracter-based: Perfect phylogeny
Module 13: Molecular Phylogenetics
Module 13: Molecular Phylogenetics http://evolution.gs.washington.edu/sisg/2013/ MTH Thanks to Paul Lewis, Joe Felsenstein, Peter Beerli, Derrick Zwickl, and Joe Bielawski for slides Wednesday July 17:
Phylogeny and Molecular Evolution. Introduction
Phylogeny and Molecular Evolution Introduction 1 2/62 3/62 Credit Serafim Batzoglou (UPGMA slides) http://www.stanford.edu/class/cs262/slides Notes by Nir Friedman, Dan Geiger, Shlomo Moran, Ron Shamir,
= 1 = 4 3. Odds ratio justification for maximum likelihood. Likelihoods, Bootstraps and Testing Trees. Prob (H 2 D) Prob (H 1 D) Prob (D H 2 )
4 1 1/3 1 = 4 3 1 4 1/3 1 = 1 12 Odds ratio justification for maximum likelihood Likelihoods, ootstraps and Testing Trees Joe elsenstein the data H 1 Hypothesis 1 H 2 Hypothesis 2 the symbol for given
CSCI1950 Z Computa4onal Methods for Biology Lecture 4. Ben Raphael February 2, hhp://cs.brown.edu/courses/csci1950 z/ Algorithm Summary
CSCI1950 Z Computa4onal Methods for Biology Lecture 4 Ben Raphael February 2, 2009 hhp://cs.brown.edu/courses/csci1950 z/ Algorithm Summary Parsimony Probabilis4c Method Input Output Sankoff s & Fitch
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
Consensus 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