Ph ylogenetic analysis

1. Phylogenetic analysis

2. Phylogenetics • Phylogenetics is the study of the evolutionary history of living organisms using treelike diagrams to represent pedigrees of these organisms. • The tree branching patterns representing the evolutionary divergence are referred to as phylogeny.

3. http://www.agiweb.org/news/evolution/fossilrecord.html Studying phylogenetics • Fossil records – morphological information, available only for certain species, data can be fragmentary, morphological traits are ambiguous, fossil record nonexistent for microorganisms • Molecular data (molecular fossils) – more numerous than fossils, easier to obtain, favorite for reconstruction of the evolutionary history

4. Tree of life http://tikalon.com/blog/blog.php?article=2011/domains

5. AAGACTT -3 mil yrs -2 mil yrs AAGGCCT AAGGCCT TGGACTT TGGACTT AGGGCAT TAGCCCT AGCACTT -1 mil yrs today AGGGCAT TAGCCCA TAGACTT AGCACAA AGCGCTT DNA sequence evolution www.cs.utexas.edu/users/tandy/CSBtutorial.ppt

6. Based on lectures by Tal Pupko Tree terminology Terminal nodes – taxa (taxon) Branches A B C D Ancestral node orrootof the tree E Internal nodes or Divergence points (represent hypothetical ancestors of the taxa)

7. Monophyletic(clade) – a taxon that is derived from a single ancestral species. • Polyphyletic – a taxon whose members were derived from two or more ancestors not common to all members. • Paraphyletic – a taxon that excludes some members that share a common ancestor with members included in the taxon. Based on lectures by Tal Pupko

8. dichotomy – all branches bifurcate, vs. polytomy – result of a taxon giving rise to more than two descendants or unresolved phylogeny (the exact order of bifurcations can not be determined exactly)

9. unrooted – no knowledge of a common ancestor, shows relative relationship of taxa, no direction of an evolutionary path • rooted – obviously, more informative

10. Finding a true tree is difficult • Correct reconstruction of the evolutionary history = find a correct tree topology with correct branch lengths. • Number of potential tree topologies can be enormously large even with a moderate number of taxa. 6 taxa … NR=945, NU=105 10 taxa … NR=34 459 425, NU = 2 027 025

12. B C Root D A A C B 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 Root Based on lectures by Tal Pupko 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: Unrooted tree

13. Based on lectures by Tal Pupko Now, try it again with the root at another position: B C Root Unrooted tree D A A B C D Rooted 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. Root

14. 2 4 1 5 3 Rooted tree 1a Rooted tree 1b Rooted tree 1c Rooted tree 1d Rooted tree 1e B A A C D A B D C B C C C A A D B B D D Based on lectures by Tal Pupko An unrooted, four-taxon tree theoretically can be rooted in five different places to produce five different rooted trees A C The unrooted tree 1: D B These trees show five different evolutionary relationships among the taxa!

15. Based on lectures by Tal Pupko Rooting the tree • outgroup– taxa (the “outgroup”) that are known to fall outside of the group of interest (the “ingroup”). Requires some prior knowledge about the relationships among the taxa. The outgroup can either be species (e.g., birds to root a mammalian tree) or previous gene duplicates (e.g., a-globins to root b-globins). outgroup

16. Based on lectures by Tal Pupko Rooting the tree • midpoint rooting approach - roots the tree at the midway point between the two most distant taxa in the tree, as determined by branch lengths. Assumes that the taxa are evolving in a clock-like manner. A d (A,D) = 10 + 3 + 5 = 18 Midpoint = 18 / 2 = 9 10 C 3 2 2 B D 5

17. Molecular clock • This concept was proposed by Emil Zuckerkandl and Linus Pauling (1962) as well as Emanuel Margoliash (1963). • This hypothesis states that for every given gene (or protein), the rate of molecular evolution is approximately constant. • Pioneering study by Zuckerkandl and Pauling • They observed the number of amino acid differences between human globins – β and δ (~ 6 differences), β and γ (~ 36 differences), α and β (~ 78 differences), and αand γ (~ 83 differences). • They could also compare human to gorilla (both β and αglobins), observing either 2 or 1 differences respectively. • They knew from fossil evidence that humans and gorillas diverged from a common ancestor about 11 MYA. • Using this divergence time as a calibration point, they estimated that gene duplications of the common ancestor to β and δoccurred 44 MYA; β and derived from a common ancestor 260MYA; α and β565 MYA; and α and γ 600MYA.

18. Gene phylogeny vs. species phylogeny • Main objective of building phylogenetic trees based on molecular sequences: reconstruct the evolutionary history of the species involved. • A gene phylogeny only describes the evolution of that particular gene or encoded protein. This sequence may evolve more or less rapidly than other genes in the genome. • The evolution of a particular sequence does not necessarily correlate with the evolutionary path of the species. • Branching point in a species tree – the speciation event • Branching point in a gene tree – which event? • The two events may or may not coincide. • To obtain a species phylogeny, phylogenetic trees from a variety of gene families need to be constructed to give an overall assessment of the species evolution.

19. Based on lectures by Tal Pupko Closest living relatives of humans?

20. Closest living relatives of humans? Humans Gorillas Chimpanzees Chimpanzees Bonobos Bonobos Gorillas Orangutans Orangutans Humans 14 0 0 15-30 MYA MYA 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. 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.

21. Orangutan Human Gorilla Chimpanzee From the Tree of the Life Website, University of Arizona

22. Forms of tree representation • phylogram – branch lengths represent the amount of evolutionary divergence • cladogram – external taxa line up neatly, only the topology matters

23. Taxon B Taxon C No meaning to the spacing between the taxa, or to the order in which they appear from top to bottom. Taxon A Taxon D Taxon E This dimension either can have no scale (for ‘cladograms’), can be proportional to genetic distance or amount of change (for ‘phylograms’), or can be proportional to time (for ‘ultrametric trees’ or true evolutionary trees). Based on lectures by Tal Pupko ((A,(B,C)),(D,E)) = The above phylogeny as nested parentheses These say that B and C are more closely related to each other than either is to A, and that A, B, and C form a clade that is a sister group to the clade composed of D and E. If the tree has a time scale, then D and E are the most closely related.

24. Newick format

25. A consensus tree • combining the nodes: • strict consensus - all conflicting nodes are collapsed into polytomies • majority rule – among the conflicting nodes, those that agree by more than 50% of the nodes are retained whereas the remaining nodes are collapsed into polytomies

26. Procedure • Choice of molecular markers • Multiple sequence alignment • Choice of a model of evolution • Determine a tree building method • Assess tree reliability

27. Choice of molecular markers • Nucleotide or protein sequence data? • NA sequences evolve more rapidly. • They can be used for studying very closely related organisms. • E. g., for evolutionary analysis of different individuals within a population, noncoding regions of mtDNA are often used. • Evolution of more divergent organisms – either slowly evolving NA (e.g., rRNA) or protein sequences. • Deepest level (e.g., relatioships between bacteria and eukaryotes) – conserved protein sequences • NA sequences: good if sequences are closely related, reveal synonymous/nonsynonymous substitutions

28. Positive and negative selection • synonymous substitution – nucleotide changes in a sequence not resulting in amino acid sequence changes (genetic code degeneracy, 3rd codon position) • nonsynonymous changes • nonsynonsymous substitution rate synonymous – positive selection • certain parts of the protein are undergoing active mutations that may contribute to the evolution of new function • negative selection – synonymous > nonsynonymous • neutral changes at the AA level, the protein sequence is critical enough that its changes are not tolerated

29. MSA • Critical step • Multiple state-of-the-art alignment programs (e.g., T-Coffee, Praline, Poa, …) should be used. • The alignment results from multiple sources should be inspected and compared carefully to identify the most reasonable one. • Automatic sequence alignments almost always contain errors and should be further edited or refined if necessary – manual editing! • Rascal and NorMD can help to improve alignment by correcting alignment errors and removing potentially unrelated or highly divergent sequences.

30. Model of evolution • A simple measure of the divergence of two sequences – number of substitutions in the alignment, a distance between two sequences – a proportion of substitutions • If A was replaced by C: A → C or A → T → G → C? • Back mutation: G → C → G. • Parallel mutations – both sequences mutate into e.g., T at the same time. • All of this obscures the estimation of the true evolutionary distances between sequences. • This effect is known as homoplasy and must be corrected. • Statistical models infer the true evolutionarydistances between sequences.

31. Model of evolution • Homplasy is corrected by substitution (evolutionary) models. • There exists a lot of such models. • Jukes-Cantor model • dAB … distance, pAB … proportion of substitutions • example: alignment of A and B is 20 nucleotides long, 6 pairs are different, pAB = 0.3, dAB = 0.38 • Kimura model • pti … frequency of transition, ptv … frequency of transversion

32. Models of amino acids substitutions • use the amino acid substitution matrix • PAM • JTT – 90s, the same methodology as PAM, but with larger protein database • protein equivalents of of Jukes–Cantor and Kimura models, e.g.,

33. Among site variations • Up to now we have assumed that different positions in a sequence are assumed to be evolving at the same rate. • However, in reality is may not be true. • In DNA, the rates of substitution differ for different codon positions. 3rd codon mutates much faster. • In proteins, some AA change much more rarely than others owing to functional constraints. • It has been shown that there are always a proportion of positions in a sequence dataset that have invariant rates and a proportion that have more variable rates. • The distribution of variant sites follows a Gammadistribution pattern.

34. Gamma distribution

35. To account for site-dependent rate variation, a Gammacorrection factor can be used. • For the Jukes–Cantor model, the evolution distance can be adjusted with the following formula: • For the Kimura model, the evolutionary distance becomes

36. Tree building methods • Two major categories. • Distance based methods. • Based on the amount of dissimilarity between pairs of sequences, computed on the basis of sequence alignment. • Characters based methods. • Based on discrete characters, which are molecular sequences from individual taxa.

37. COMPUTATIONAL METHOD Optimality criterion Clustering algorithm Maximum parsimony (MP) Maximum likelihood (ML) Characters DATA TYPE Fitch-Margoliash (FM) UPGMA Neighbor-joining (NJ) Distances Tree building methods

38. Distance based methods • Calculate evolutionary distances dAB between sequences using some of the evolutionary model. • Construct a distance matrix – distances between all pairs of taxa. • Based on the distance scores, construct a phylogenetic tree. • clustering algorithms – UPGMA, neighbor joining (NJ) • optimality based – Fitch-Margoliash (FM), minimum evolution (ME)

39. Clustering methods • UPGMA  (Unweighted Pair Group Method with Arithmetic Mean) • Hierachical clustering, agglomerative, you know it as an average linkage • Produces rooted tree (most phylogenetic methods produce unrooted tree). • Basic assumption of the UPGMA method: all taxa evolve at a constant rate, they are equally distant from the root, implying that a molecular clock is in effect. • However, real data rarely meet this assumption. Thus, UPGMA often produces erroneous tree topologies.

40. C C A A D D A,B E B C B E A D E B Neighbor joining The Minimum Evolution (ME) criterion: in each iteration we separate the two sequences which result with the minimal sum of branch lengths

41. Optimality based methods • Clustering methods produce a single tree. • There is no criterion in judging how this tree is compared to other alternative trees. • Optimalitybased methods have awell-defined algorithm to compare all possible treetopologies and select a tree that best fits the actual evolutionary distance matrix.

42. Distance based – pros and cons • clustering • Fast, can handle large datasets • Not guaranteed to find the best tree • The actual sequence information is lost when all the sequence variation is reduced to a single value. Hence, ancestral sequences at internal nodes cannot be inferred. • UPGMA – assumes a constant rate of evolution of the sequences in all branches of the tree (molecular clock assumption) • NJ – does not assume that the rate of evolution is the same in all branches of the tree • NJ is slower but better than UPGMA • exhaustive tree searching (FM) • better accuracy, prohibitive for more than 12 taxa

43. Character based methods • Also called discreet methods • Based directly on the sequence characters • They count mutational events accumulated on the sequences and may therefore avoid the loss of information when characters are converted to distances. • Evolutionary dynamics of each character can be studied • Ancestral sequences can also be inferred. • The two most popular character-based approaches: maximum parsimony (MP) and maximum likelihood (ML) methods.

44. Maximum parsimony • Based on Occam’s razor. • William of Occam, 13thcentury. • The simplest explanation is probably the correct one. • This is because the simplest explanation requires the fewest assumptions and the fewest leaps of logic. • A tree with the least number of substitutions is probably the best to explain the differences among the taxa under study.

45. A worked example To save computing time, only a small number of sites that have the richest phylogenetic information are used in tree determination. informative site – sites that have at least two different kinds of characters, each occurring at least twice

46. A worked example To save computing time, only a small number of sites that have the richest phylogenetic information are used in tree determination. informative site – sites that have at least two different kinds of characters, each occurring at least twice

47. 1 1 1 3 2 3 2 3 4 4 4 2 How many possible unrooted trees? Tree I Tree II Tree III

48. G G G G A A 3 3 2 1 1 1 A G A A G A 4 4 2 4 3 2 GGAA A G G G Tree I Tree II G G Tree III

49. G G G C G C C C G C G C GGCC C G G G Tree I Tree II G G Tree III

50. A A A A G A G G G A G G AGAG A A G A Tree I Tree II G G Tree III