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Phylogenetic Inference

Christian M Zmasek , PhD czmasek@burnham.org 15 June 2010. Phylogenetic Inference. Overview. Why perform phylogenetic inference? Theoretical background Methods Software & Examples. 1. Why perform phylogenetic inference?. ‘ Tree of life ’: The relationships amongst different species

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Phylogenetic Inference

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  1. Christian M Zmasek, PhD czmasek@burnham.org 15 June 2010 Phylogenetic Inference

  2. Overview • Why perform phylogenetic inference? • Theoretical background • Methods • Software & Examples (C) 2010 Christian M. Zmasek

  3. 1. Why perform phylogenetic inference? • ‘Tree of life’: The relationships amongst different species • Infer the functions of proteinsfrom family members in model organismsor to refine existing annotations through phylogeneticanalysis • A method to organize/cluster sequences with biological justification (C) 2010 Christian M. Zmasek

  4. Over-annotation due to database bias or gene loss RAT RAT MOUSE MOUSE Y Y HUMAN RICE RICE HUMAN X LIZARD LIZARD Z Z SHARK SHARK : query sequence : orthologous to query : most similar to query : gene duplication (C) 2010 Christian M. Zmasek

  5. Over-annotation due to unequal rates of evolution [phylogenetic tree ≠ clustering !!] HUMAN Y WHEAT RAT Z BARLEY : query sequence : orthologous to query : most similar to query : gene duplication (C) 2010 Christian M. Zmasek

  6. 2. Theoretical Background • A phylogeny is the evolutionary history of a species or a group of species. Lately, the term is also being applied to the evolutionary history of individualDNAorprotein sequences. • The evolutionary history of organisms or sequences can be illustrated using a tree-like diagram – a phylogenetic tree. (C) 2010 Christian M. Zmasek

  7. (C) 2010 Christian M. Zmasek

  8. Gene Trees/Species Trees • Initially, phylogenetic trees were built based on the morphology of organisms. • Around 1960 molecular sequences were recognized as containing phylogenetic information and hence as valuable for tree building • A tree built based on sequence data is called a gene tree since it is a representation of the evolutionary history of genes • A tree illustrating the evolutionary history of organisms is called a species tree (C) 2010 Christian M. Zmasek

  9. A gene tree which is also a species tree (C) 2010 Christian M. Zmasek

  10. A gene tree of orthologs and paralogs based on Bcl-2 family protein sequences (C) 2010 Christian M. Zmasek

  11. Homologs • Homologs are defined as sequences which share a common ancestor (Fitch, 1966) • This definition becomes unclear if mosaic proteins, which are composed of structural units originating from different genes are considered • Phylogenetic trees make sense only if constructed based on homologous sequences (whole genes/proteins, or domains) (C) 2010 Christian M. Zmasek

  12. Orthologs, Paralogs, Xenologs • Homologous sequences can be divided into orthologs, paralogs and xenologs: • Orthologs: diverged by a speciation event (their last common ancestor on a phylogenetic tree corresponds to a speciation event) • IMPORANT: Functional similarity does not imply orthology • Paralogs: diverged by a duplication event (their last common ancestor corresponds to a duplication) • Xenologs: are related to each other by horizontal gene transfer (via retroviruses, for example) (C) 2010 Christian M. Zmasek

  13. Orthologs, Paralogs example (C) 2010 Christian M. Zmasek

  14. Caveat emptor: Orthology vs. Function • Orthologous sequences tend to have more similar “functions” than paralogs • Yet: Orthologs are mathematically defined, whereas there is no definition of sequence “function” (i.e. it is a subjective term) (C) 2010 Christian M. Zmasek

  15. Gene Duplication • New genes evolve if mutations accumulate while selective constraints are relaxed by gene duplication • First recognized by Haldane (“… it [mutation pressure] will favour polyploids, and particularly allopolyploids, which possess several pairs of sets of genes, so that one gene may be altered without disadvantage…” (C) 2010 Christian M. Zmasek

  16. Gene Trees Vs. Species Trees – How Gene Duplications Can Be Detected G1 S G2 Rat Wheat Human Rat Rat Rat Wheat Wheat Wheat Human Human Human (C) 2010 Christian M. Zmasek

  17. 3. Methods Multiple sequence alignment of homologous sequences Pairwise distance calculation • Optimality Criteria Based on Character Data: • Maximum Parsimony • Maximum Likelihood • Algorithmic Methods Based on Pairwise Distances: • UPGMA • Neighbor Joining • Optimality Criteria Based on Pairwise Distances: • Fitch-Margoliash • Minimal Evolution Bayesian Methods (MCMC) “More accurate” (in general) Fast (C) 2010 Christian M. Zmasek

  18. Pairwise Distance Calculation The simplest method to measure the distance between two amino acid sequences is by their fractional dissimilarityp (nd is the number of aligned sequence positions containing non-identical amino acids and ns is the number of aligned sequence positions containing identical amino acids): (C) 2010 Christian M. Zmasek

  19. Pairwise Distance Calculation • Unfortunately, this is unrealistic -- does not take into account: • superimposed changes: multiple mutations at the same sequence location • different chemical properties of amino acids: for example, changing leucine into isoleucine is more likely and should be weighted less than changing leucine into proline (C) 2010 Christian M. Zmasek

  20. Pairwise Distance Calculation • A more realistic approach for estimating evolutionary distances is to apply maximum likelihood to empirical amino acid replacement models, such as PAM transition probability matrices. • The likelihood LHof a hypothesis H (an evolutionary distance, for example) given some data D (an alignment, for example) is the probability of D given H: LH=P(D|H) (C) 2010 Christian M. Zmasek

  21. UPGMA vs … • UPGMA stands for unweighted pair group method using arithmetic averages • Thisisclustering • This algorithm produces rooted trees based under the assumption of a molecular clock. (C) 2010 Christian M. Zmasek

  22. … Neighbor Joining • As opposed to UPGMA, neighbor joining (NJ) is not misled by the absence of a molecular clock • NJ produces phylogenetic trees (not cluster diagrams) (C) 2010 Christian M. Zmasek

  23. Optimality Criteria Based on Character Data • Fitch-Margoliash • Minimal evolution (ME) • Maximum Parsimony (MP) • Maximum Likelihood (ML) (C) 2010 Christian M. Zmasek

  24. Minimal Evolution • Branch lengths are fitted to a tree according to a unweighted least squares criterion, but the optimality criterion to evaluate and compare trees is to minimize the sum of all branch lengths. (C) 2010 Christian M. Zmasek

  25. Maximum Parsimony • Evaluate a given topology • Example: Sequence1: TGC Sequence2: TAC Sequence3: AGG Sequence4: AAG (C) 2010 Christian M. Zmasek

  26. Maximum Likelihood • Probabilistic methods can be used to assign a likelihood to a given tree and therefore allow the selection of the tree which is most likely given the observed sequences. • Probability for one residue a to change to b in time t along a branch of a tree: P(b|a,t) • Its actual calculation is dependent on what model for sequence evolution is used. • Poisson process: • P(b|a,t)=1/20 + 19/20e-ut for a=b • P(b|a,t)=1/20 + 1/20e-ut for a≠b (C) 2010 Christian M. Zmasek

  27. Bayesian Methods • Example: MrBayes • Use Markov Chain Monte Carlo (MCMC) approach to sample over tree space (C) 2010 Christian M. Zmasek

  28. Bootstrap resampling • To asses the reliability of trees • Resampling with replacement (see example on next slide) • What is “good enough”?? >60%?, >90%? (C) 2010 Christian M. Zmasek

  29. Bootstrap resampling: example Original sequence alignment: Sequence 1: ARNDCQ Sequence 2: VRNDCQ 123456 Bootstrap resample 1: Sequence 1: RRQCCA Sequence 2: RRQCCV 226551 Bootstrap resample 2: Sequence 1: AQCDCQ Sequence 2: VQCDCQ 165456 (C) 2010 Christian M. Zmasek

  30. Summary Multiple sequence alignment of homologous sequences Pairwise distance calculation • Optimality Criteria Based on Character Data: • Maximum Parsimony • Maximum Likelihood • Algorithmic Methods Based on Pairwise Distances: • UPGMA • Neighbor Joining • Optimality Criteria Based on Pairwise Distances: • Fitch-Margoliash • Minimal Evolution Bayesian Methods (MCMC) “More accurate” (in general) Fast (C) 2010 Christian M. Zmasek

  31. 4: Software for multiple sequence alignments • Mafft: • http://mafft.cbrc.jp/alignment/software/ • Server: http://mafft.cbrc.jp/alignment/server/ • T-Coffee: • http://www.tcoffee.org/Projects_home_page/t_coffee_home_page.html • Server: http://www.ch.embnet.org/software/TCoffee.html • Server: http://www.ebi.ac.uk/t-coffee/ • ClustalW: • ftp://ftp-igbmc.u-strasbg.fr/pub/ClustalW/ • Server: http://www.ebi.ac.uk/clustalw/ • Probcons: • http://probcons.stanford.edu/ • Server: http://probcons.stanford.edu • Muscle: • http://www.drive5.com/muscle/ • Server: http://phylogenomics.berkeley.edu/cgi-bin/muscle/input_muscle.py (C) 2010 Christian M. Zmasek

  32. Software for phylogeny reconstruction • List of programs: http://evolution.genetics.washington.edu/phylip/software.html • ML pairwise distance calculation (protein): • TREE-PUZZLE: http://www.tree-puzzle.de/ • Bootstrapping, pairwise distance calculation, UPGMA, NJ, Fitch-Margolish, ME: • PHYLIP: http://evolution.genetics.washington.edu/phylip.html • ME: • FastME (server): http://atgc.lirmm.fr/fastme/ • MEGA: http://www.megasoftware.net/ • ML: • PhyML (server):http://www.atgc-montpellier.fr/phyml/ • RAxML (server): http://phylobench.vital-it.ch/raxml-bb/ • Bayesian (MCMC): • MrBayes: http://mrbayes.csit.fsu.edu/ • Parsimony (esp. on Macintosh), display: • PAUP: http://paup.csit.fsu.edu/ • Tree display: • Archaeopteryx: http://www.phylosoft.org/archaeopteryx/ • Hypothesis testing: • HyPhy: http://www.hyphy.org/ (C) 2010 Christian M. Zmasek

  33. Books • Richard Durbin et al.: Biological Sequence Analysis: Probabilistic Models of Proteins and Nucleic Acids [http://www.amazon.com/Biological-Sequence-Analysis-Probabilistic-Proteins/dp/0521629713/sr=1-1/qid=1170198997/ref=sr_1_1/102-4955297-1236120?ie=UTF8&s=books] • Joe Felsenstein: Inferring Phylogenies [http://www.amazon.com/Inferring-Phylogenies-Joseph-Felsenstein/dp/0878931775/sr=8-1/qid=1170198215/ref=pd_bbs_sr_1/102-4955297-1236120?ie=UTF8&s=books] • Ziheng Yang: Computational Molecular Evolution [http://www.amazon.com/Computational-Molecular-Evolution-Oxford-Ecology/dp/0198567022/sr=1-1/qid=1170198731/ref=pd_bbs_sr_1/102-4955297-1236120?ie=UTF8&s=books] • Oliver Gascuel: Mathematics of Evolution & Phylogeny [http://www.amazon.com/Mathematics-Evolution-Phylogeny-Olivier-Gascuel/dp/0198566107/sr=1-1/qid=1170198842/ref=sr_1_1/102-4955297-1236120?ie=UTF8&s=books] (C) 2010 Christian M. Zmasek

  34. 5. “Homework” • Download and install MrBayes: http://mrbayes.csit.fsu.edu/ • Read the tutorial: http://mrbayes.csit.fsu.edu/wiki/index.php/Tutorial • Analyze the provided data set (“primates.nex”) • Download and install PHYLIP: http://evolution.genetics.washington.edu/phylip.html • Perform seqboot (100x) – dnadist – neighbor (NJ) – consense on “primates.nex” (you need to change the format accordingly) • Compare the results (MrBayes vs. Phylip NJ) (C) 2010 Christian M. Zmasek

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