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Phylogenetic Inference

Phylogenetic Inference. Data Optimality Criteria Algorithms Results Practicalities. Our Goals. Infer Phylogeny Optimality criteria Algorithm Phylogenetic inference (interesting ones). Watch Out.

Gabriel
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Phylogenetic Inference

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  1. Phylogenetic Inference Data Optimality Criteria Algorithms Results Practicalities Chuck Staben

  2. Our Goals • Infer Phylogeny • Optimality criteria • Algorithm • Phylogenetic inference • (interesting ones) Chuck Staben

  3. Watch Out “The danger of generating incorrect results is inherently greater in computational phylogenetics than in many other fields of science.” “…the limiting factor in phylogenetic analysis is not so much in the facility of software applicaition as in the conceptual understanding of what the software is doing with the data.” Chuck Staben

  4. Phylogenetic Models • No transfer of genetic information by hybridization • All sequences are homologous • Each position in alignment homologous • Observed variation is valid sample from included group • Positions evolve independently Chuck Staben

  5. Steps in Analysis • Data Model (Alignment) • alignment method • “trimming” to a phylogenetic set • DNA base substitution model • Build Trees • Algorithm based vs Criterion based • Distance based vs Character-based Chuck Staben

  6. Choice of Input Data • Data Type • Aligned sequences, RFLP, morphological data… • Molecule of interest • rRNA (general purpose) • interesting character • Number/type of taxa • ingroup and outgroup Informative Chuck Staben

  7. rRNA Genes • Conserved across kingdoms • Varies within species • Widely sequenced, easy • Long, lots of characters Duplication? Chuck Staben

  8. Multiple Alignment Method • Computer dependence • Phylogenetic Assumptions • Alignment parameters • (substitution matrix, gap cost) • Aligned features • primary sequence, structure • Optimization • statistical, non-statistical Chuck Staben

  9. Typical Alignment Method • CLUSTAL, then manual editing • Manual editing for phylogeny • phylogenetic assumption in guide tree • parameters a priori and dynamic • primary structure (with some “influence” • optimization non-statistical Chuck Staben

  10. Substitution Models • G to A, C to T versus N to N • amino acid substitution • forwards and backwards identical? • site-to-site variation Simpler model better Estimate from "quick" tree building, Observed Variation Chuck Staben

  11. Tree-Building Methods • Distance • UPGMA, NJ, FM, ME • Character • Maximum Parsimony (PAUP) • Maximum Likelihood (PHYLIP) Acrimonious Debates Chuck Staben

  12. Distance Methods • Measure distance (dissimilarity) • Accurate if distances are all summative (ultrametric) • NEVER true over large distance • Methods • UPGMA (Unweighted pair group method with Arithmetic Mean) • NJ (Neighbor joining) • FM (Fitch-Margoliash) • ME (Minimal Evolution) Most Often Wrong! CLUSTAL Chuck Staben

  13. Which Distance Method? • UPGMA • Least accurate, most used • NJ • EXTREMELY RAPID • GIVES ONLY 1 TREE • ME and FM seem best • Minimize tree path lengths Chuck Staben

  14. Character Methods • Maximum Parsimony • minimal changes to produce data • can use different substitution models • Maximum Likelihood • turns problem “inside out” • coin flip analogy • increasingly popular Chuck Staben

  15. Searching for Trees Chuck Staben

  16. Tree Search Algorithms • Exhaustive • VERY INTENSIVE • Branch and Bound • Compromise • Heuristic • FAST (usually start with NJ) Chuck Staben

  17. Evaluating Trees • Consenus Tree • Randomized Trees • Skewness tests • Randomized Character Data • Permutation tests • Bootstrap, Jackknife • resampling techniques • >70% probably correct; 50% overestimates accuracy Chuck Staben

  18. Rooting Trees • Molecular Clock • Root=midpoint, longest span • Almost ALWAYS WRONG • Extrinsic Evidence • select fungus as root for plants, eg • long branch attraction can be problem • Paralog rooting • long branch problems Chuck Staben

  19. Tree Congruence • Tree-to-Tree Comparison • 2 different characters/same groups • Important for evaluating biological hypotheses • lentiviruses diverged within their current hosts only • plant pathogenicity has arisen many times in fungi Chuck Staben

  20. Common Software • PAUP • GCG • Pileup, Lineup, Paupsearch, Paupdisplay • PAUPSTAR (MACs best!) • PHYLIP • UNIX (Seqanal) Chuck Staben

  21. Phylogenetic Stories • HIV • complete genome accessible • evolution rapid • selection, neutralism? • human interest (dentist and his patients, eg.) • Coevolution, host and pathogen • Big Tree Chuck Staben

  22. Phylogenetic Resources • NCBI Taxonomy Browser • http://www.ncbi.nlm.nih.gov/Taxonomy/ • RDP database • http://rdpwww.life.uiuc.edu/ • “Tree of Life” • http://phylogeny.arizona.edu/tree/phylogeny.html Chuck Staben

  23. Practicalities • Quality of input data critical • Examine data from all possible angles • distance, parsimony, likelihood • Outgroup taxon critical • problem if outgroup shares a selective property with a subset of ingroup • Order of input can be problematic • Jumble them! Chuck Staben

  24. Trees plagiarized by Chuck Staben, 1998 Seargent Joyce Kilmer, 1914 Chuck Staben

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