1. Phylogenetic Inference Data Optimality Criteria Algorithms Results Practicalities BIO520 BioinformaticsJim Lund Reading: Ch8

2. Our Goals Infer Phylogeny –Optimality criteria –Algorithm Determine the sequence of branching events that reflects the history of a group of organisms.

3. Phylogenetic Model Assumptions No transfer of genetic information by hybridization All sequences are homologous (orthologous, really) Each position in alignment homologous Observed variation is valid sample from included group Positions evolve independently

4. Steps in Analysis 1.Data Model (Alignment) –alignment method –“trimming” to a phylogenetic set 2.DNA base substitution model 3.Build Trees –Algorithm based vs Criterion based –Distance based vs Character-based 4.Assess tree quality.

5. Choice of Input Data Data Type –Aligned sequences, RFLP, morphological data… Molecule of interest –rRNA (general purpose) –Mitochondrial DNA –Selected genes Number/type of taxa –ingroup and outgroup

6. rRNA Genes Conserved across kingdoms Varies within species Widely sequenced, easy Long, lots of characters

7. Multiple Alignment Method Phylogenetic Assumptions Alignment parameters –(substitution matrix, gap cost) Aligned features –primary sequence, structure Optimization –statistical, non-statistical

8. Typical Alignment Method CLUSTAL, then manual editing –Manual editing for phylogeny –phylogenetic assumption in guide tree –parameters a priori and dynamic –Optimization Non-statistical Remove poorly aligned regions Test several gap penalties

9. Substitution Models G to A, C to T versus N to N Amino acid substitution Forwards and backwards weights identical? Site-to-site variation

10. Tree-Building Methods Distance-based methods –NJ, FM, ME, UPGMA Character-based methods –Maximum Parsimony (PAUP) –Maximum Likelihood (PHYLIP) Algorithm choice is a contested, active research field.

11. Molecular phylogenetic tree building methods: Are mathematical and/or statistical methods for inferring the divergence order of taxa, as well as the lengths of the branches that connect them. There are many phylogenetic methods available today, each having strengths and weaknesses. Most can be classified as follows: COMPUTATIONAL METHOD Clustering algorithmOptimality criterion DATA TYPE Characters (bp, aa) Distances PARSIMONY MAXIMUM LIKELIHOOD UPGMA NEIGHBOR-JOINING MINIMUM EVOLUTION LEAST SQUARES

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

13. Which Distance Method? UPGMA (Unweighted pair group method with Arithmetic Mean) –Least accurate, still commonly used NJ (Neighbor joining) –EXTREMELY RAPID –GIVES ONLY 1 TREE ME (Minimal Evolution) and FM (Fitch-Margoliash) seem best –Minimize tree path lengths

14. Inferring Trees and Ancestors CCCAGG CCCAAG-> CCCAAG CCCAAA-> CCCAAA CCCAAA-> CCCAAC

15. Different Criteria 1CCCAGG 2CCCAAG 3CCCAAA 4CCCAAC 1-21 1-32 1-42 2-31 2-41 3-41 1,2 can be sister taxa AND 3,4 can be sister taxa Infer ancestor of 1,2 and 3,4 Distance from 1/2, 3/4 equal

16. Character Methods Maximum Parsimony –minimal changes to produce data –can use different substitution models Maximum Likelihood –turns problem “inside out”, single most likely tree that explains data coin flip analogy –increasingly popular Bayesian –Searches for Best Set of trees that explains data AND fits evolutionary model

17. Parsimony CCCAGG CCCAAG-> CCCAAG CCCAAA-> CCCAAA CCCAAA-> CCCAAC 4 TAXA, 3 changes minimum Search for shortest tree, the one with the fewest changes.

18. Likelihood Models Hypothesis 1: All 3 teams are equally good. Hypothesis 2: The Yankees are the best team. Hypothesis 3: The Tigers are the worst team

19. Searching for Trees

20. Tree Search Algorithms Exhaustive –VERY INTENSIVE Branch and Bound –Compromise Heuristic –FAST (usually start with NJ) # of taxaNJParsimonyMLBayes 100.2s0.05s4.1s0.5 hr 50.2s.7s7hr4hr

21. Evaluating Trees Consensus Tree Randomized Trees –Skewness tests Randomized Character Data –Permutation tests (permuted by column) Bootstrap, Jackknife –resampling techniques –Counts how often each clade appears in test data. –>70% probably correct; 50% overestimates accuracy

22. Tree Congruence Tree-to-Tree Comparison –2 different characters/same groups –Important for evaluating biological hypotheses Example: Did lentiviruses diverge within their current hosts only? Or did plant pathogenicity has arisen many times in fungi?

23. Inferring evolutionary relationships between the taxa requires rooting the tree: To root a tree mentally, imagine that the tree is made of string. Grab the string at the root and tug on it until the ends of the string (the taxa) fall opposite the root: A B C Root D A B C D Note that in this rooted tree, taxon A is no more closely related to taxon B than it is to C or D. Rooted tree Unrooted tree

24. Now, try it again with the root at another position: A B C Root D Unrooted tree Note that in this rooted tree, taxon A is most closely related to taxon B, and together they are equally distantly related to taxa C and D. C D Root Rooted tree A B

25. Rooting Trees Molecular Clock –Root=midpoint of longest span –Unreliable, often wrong. Evidence –select fungus as root for plants, eg long branch attraction can be Extrinsic problem Paralog rooting –long branch problems

26. Phylogenetic Software PHYLIP –http://evolution.genetics.washington.edu/phylip.html –http://saf.bio.caltech.edu/www/saf_manuals/phylip/phylip.htmlhttp://saf.bio.caltech.edu/www/saf_manuals/phylip/phylip.html PAUP: Pileup, Lineup, Paupsearch, Paupdisplay –http://paup.csit.fsu.edu/versions.html MrBayes –Bayesian trees –http://mrbayes.csit.fsu.edu/ Treeview –Several programs going by this name have been written. –Draw/format phylogenic trees –Jave TreeView: http://jtreeview.sourceforge.net/

27. Phylogenetic Stories HIV –complete genome accessible –evolution rapid selection, neutralism? Primate evolution –Which primate is the closest relative to modern humans?

28. HIV Genome Diversity Error prone (RT) replication High rate of replication –10 10 virions/day In vivo selection pressure And In vivo recombination!

29. HIV tree Recombinants? ENV GAG AIDS 1996, 10:S13

30. Subtype E ENV=A “Bootscanning” AIDS 1996, 10:S13

31. Which species are the closest living relatives of modern humans? Mitochondrial DNA, most nuclear DNA- encoded genes, and DNA/DNA hybridization all show that bonobos and chimpanzees are related more closely to humans than either are to gorillas. The pre-molecular view was that the great apes (chimpanzees, gorillas and orangutans) formed a clade separate from humans, and that humans diverged from the apes at least 15-30 MYA. MYA Chimpanzees Orangutans Humans Bonobos Gorillas Humans Bonobos GorillasOrangutans Chimpanzees MYA 0 15-30 0 14

32. Phylogenetic Resources NCBI Taxonomy Browser –http://www.ncbi.nlm.nih.gov/Taxonomy/ RDP database (Ribosomal Database Project) –http://rdp.cme.msu.edu/index.jsp “Tree of Life” –http://tolweb.org/tree/phylogeny.html

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