Bioinformatica t6-phylogenetics


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Bioinformatica t6-phylogenetics

  1. 1. FBW 6-11-2012Wim Van Criekinge
  2. 2. Inhoud Lessen: Bioinformatica GEEN LES
  3. 3. Phylogenetics Introduction Definitions Species concept Examples The Tree-of-life Phylogenetics Methodologies Algorithms Distance Methods Maximum Likelihood Maximum Parsimony Rooting Statistical Validation Conclusions Orthologous genes Horizontal Gene Transfer Phylogenomics Practical Approach: PHYLIP Weblems
  4. 4. What is phylogenetics ? Phylogeny (phylo =tribe + genesis) Phylogenetic trees are about visualising evolutionary relationships. They reconstruct the pattern of events that have led to the distribution and diversity of life. The purpose of a phylogenetic tree is to illustrate how a group of objects (usually genes or organisms) are related to one another Nothing in Biology Makes Sense Except in the Light of Evolution. Theodosius Dobzhansky (1900-1975)
  5. 5. Trees • Diagram consisting of branches and nodes • Species tree (how are my species related?) – contains only one representative from each species. – all nodes indicate speciation events • Gene tree (how are my genes related?) – normally contains a number of genes from a single species – nodes relate either to speciation or gene duplication events
  6. 6. Clade: A set of species which includes all of the speciesderived from a single common ancestor
  7. 7. S p e c ie s C o n c e p ts from V a rio u s A u th o rsD .A . B a um a nd K .L . S ha w - E x c lu s iv e g rou p s o f org a n ism s, w h ere a n ex c lu s iv e g rou p is o ne w h ose m e m b ers are a ll m ore c lose ly re la ted to ea c h oth er th a n to a n y org a n is m s ou ts id e the g rou p .J . C ra cra ft - A n irred u c ib le c lu ster o f org a n ism s, d iag n osab ly d is tin ct fro m oth er su c h c lu sters, a nd w ith in w h ic h there is a p are n ta l p a ttern o f a nc estry a nd d esce n t.C ha rles D a rw in - "F rom these rem arks it w ill b e se e n th at I lo o k a t th e term sp e c ies, as o n e arb itrarily g iv e n for the sa k e o f c o n v e n ie nc e to a set o f in d iv id u a ls c lose ly rese m b lin g e ac h o ther, a nd th a t it d oes n ot essen tia lly d iffer from the term varie ty, w h ic h is g iv e n to l ess d istin ct a nd m ore flu c tu a ting form s. T he term varie ty, ag a in, in c o m p aris o n w ith m ere in d iv id u a l d iffere n ces, is a ls o a p p lied arb itrarily, a n d for m ere c o n ve n ie n ce sa k e " (O rig in o f S p ec ies, 1 st ed., p . 1 0 8 ).T . D o b zha nsk y - T h e larg est a nd m ost in c lu s ive rep rod u ctiv e c om m u n ity o f sex u a l a nd cross-fertiliz ing in d iv id u a ls w h ic h sh are a c o m m o n g e ne p o o l. A nd la ter...S ys te m s o f p op u la tio ns, th e g e n e ex c ha ng e b e tw ee n w h ic h is lim ited or p re v e nted b y rep rod u ctiv e is o la ting m e c h a n is m s.M . G hise lin - T h e m ost ex te ns ive u n its in the n atu ra l e c o n om y, su c h tha t rep r od u ctiv e c om p etitio n oc cu rs am o ng th e ir p arts.D .M . L a m b ert - G rou p s o f ind iv id u a ls th at d e fin e th em se lv es b y a sp e c ific m a te rec og n itio n s ystem .J . M a llet - Id e ntifia b le g e n o typ ic c lu sters re c og n iz e d b y a d e fic it o f in term ed iates, b o th a t s ing le lo c i a n d at m u ltip le lo c i.E . M a y r - G rou p s o f ac tu a lly or p o te n tia lly in terb ree d ing na tu ra l p op u lat io ns w h ic h are rep rod u ctiv e ly is o la ted fro m oth er su c h g rou p s.C .D . M ich en er - A g rou p o f org a n is m s n o t itse lf d iv is ib le b y p he n etic g ap s resu ltin g from c o nc ord a nt d iffere n ces in c harac ter states (ex c ep t for m orp hs - su ch as sex , ag e, or caste), b u t sep ara ted b y su ch p h e ne tic g ap s from o ther su c h u n its.H .E .H . P a tte rso n - T h at m ost in c lu s iv e p op u latio n o f in d iv id u a l b ip are n ta l org a n is m s w h ic h sh are a c o m m o n fertiliz atio n s ystem .G .G . S im p so n - A lin eag e o f p op u latio ns e v o lv in g w ith tim e, sep arate ly fro m ot h ers, w ith its ow n u n iq u e e v o lu tio n ary ro le a nd te nd e n c ies.P .H .A . S nea th a nd R .R . S o k a l - T he sm a llest (m ost h o m og e n e ou s) c lu ster th at ca n b e re c og n iz ed u p o n s o m e g iv e n criterio n as b e in g d istin c t fro m oth er c lu sters.A .R . T em p leto n - T h e m ost in c lu s ive p op u la tio n o f in d iv id u a ls h a v ing the p o te n ti a l for p h e n otyp ic c o he sio n throu g h in trins ic c o h es io n m e c ha n is m s (g e ne tic a nd /or d e m og rap h ic - i.e. ec o lo g ic a l -ex c h a ng e ab ility).E .O . W iley - A s ing le lin e ag e o f a nc estor -d esc e nd a n t p op u latio ns w h ic h m a in ta ins its id e ntity fro m oth er s u ch lin e ag es a nd w h ic h h as its ow n e v o lu tio n ary te nd e n c ies a nd h istoric a l fate.S . W rig ht - A sp ec ies in tim e a nd sp ac e is c o m p ose d o f nu m erou s lo ca l p op u latio ns, ea c h o ne in terc o m m u n ica ting a nd in terg rad ing w ith oth ers.
  8. 8. SpeciesI. Definitions:Species = the basic unit of classification> Three different ways to recognize species:
  9. 9. Plant SpeciesDefinitions:> Three different ways to recognize species:1) Morphological species = the smallest group that is consistently and persistently distinct (Clusters in morphospace) species are recognized initially on the basis of appearance; the individuals of one species look different from the individuals of another
  10. 10. SpeciesDefinitions:> Three different ways to recognize species:2) Biological species = a set of interbreeding or potentially interbreeding individuals that are separated from other species by reproductive barriers species are unable to interbreed
  11. 11. SpeciesDefinitions:> Three different ways to recognize species:3) Phylogenetic species = the boundary between reticulate (among interbreeding individuals) and divergent relationships (between lineages with no gene exchange)
  12. 12. Phylogenetic species divergentboundary reticulate recognized by the pattern of ancestor - descendent relationships
  13. 13. SpeciesDefinitions:> Three different ways to recognize species:4) Phylogenomics species = ability to transmit (and maintain) a (stable) gene pool Adresses the Anopheles genome topology variations
  14. 14. Branching Order in a Phylogenetic Tree • In the tree to the left, A and B share the most recent common ancestry. Thus, of the species in the tree, A and B are the most closely related. • The next most recent common ancestry is C with the group composed of A and B. Notice that the relationship of C is with the group containing A and B. In particular, C is not more closely related to B than to A. This can be emphasized by the following two trees, which are equivalent to each other:
  15. 15. More definitions … Edge, Branch Leafs Tips external node Branch node, internal node• A common simplifying assumption is that the three is bifurcating,meaning that each brach node has exactly two descendents.• The edges, taken together, are sometimes said to define the topologyof the tree
  16. 16. Outgroups, rooted versus unrooted An unrooted reptilian phylogeny with an avian outgroup and the corresponding rooted phylogeny. The Ri represent modern reptiles; the Ai, inferred ancestors and the B a bird.
  17. 17. Some definitions …
  18. 18. Examples Phylogenetic methods may be used to solve crimes, test purity of products, and determine whether endangered species have been smuggled or mislabeled: – Vogel, G. 1998. HIV strain analysis debuts in murder trial. Science 282(5390): 851-853. – Lau, D. T.-W., et al. 2001. Authentication of medicinal Dendrobium species by the internal transcribed spacer of ribosomal DNA. Planta Med 67:456-460.
  19. 19. Examples – Epidemiologists use phylogenetic methods to understand the development of pandemics, patterns of disease transmission, and development of antimicrobial resistance or pathogenicity: • Basler, C.F., et al. 2001. Sequence of the 1918 pandemic influenza virus nonstructural gene (NS) segment and characterization of recombinant viruses bearing the 1918 NS genes. PNAS, 98(5):2746-2751. • Ou, C.-Y., et al. 1992. Molecular epidemiology of HIV transmission in a dental practice. Science 256(5060):1165-1171. • Bacillus Antracis:
  20. 20. Examples• Conservation biologists may use these techniques to determine which populations are in greatest need of protection, and other questions of population structure: – Trepanier, T.L., and R.W. Murphy. 2001. The Coachella Valley fringe-toed lizard (Uma inornata): genetic diversity and phylogenetic relationships of an endangered species. Mol Phylogenet Evol 18(3):327-334. – Alves, M.J., et al. 2001. Mitochondrial DNA variation in the highly endangered cyprinid fish Anaecypris hispanica: importance for conservation. Heredity 87(Pt 4):463-473.• Pharmaceutical researchers may use phylogenetic methods to determine which species are most closely related to other medicinal species, thus perhaps sharing their medicinal qualities: – Komatsu, K., et al. 2001. Phylogenetic analysis based on 18S rRNA gene and matK gene sequences of Panax vietnamensis and five related species. Planta Med 67:461-465.
  21. 21. Tree-of-life
  22. 22. Some Important Dates in History Origin of the Universe 15 billion yrs Formation of the Solar System 4.6 " First Self-replicating System 3.5 " Prokaryotic-Eukaryotic Divergence 2.0 " Plant-Animal Divergence 1.0 " Invertebrate-Vertebrate Divergence 0.5 " Mammalian Radiation Beginning 0.1 "
  23. 23. Tree Of Life
  24. 24. Tree Of Life
  25. 25. Tree Of Life
  26. 26. Tree Of Life
  27. 27. What Sequence to Use ? • To infer relationships that span the diversity of known life, it is necessary to look at genes conserved through the billions of years of evolutionary divergence. • The gene must display an appropriate level of sequence conservation for the divergences of interest. .
  28. 28. What Sequence to Use ? • If there is too much change, then the sequences become randomized, and there is a limit to the depth of the divergences that can be accurately inferred. • If there is too little change (if the gene is too conserved), then there may be little or no change between the evolutionary branchings of interest, and it will not be possible to infer close (genus or species level) relationships.
  29. 29. Ribosomal RNA Genes and Their Sequences recognized the full potential of rRNA sequences as a measure of phylogenetic relatedness. He initially used an RNA sequencing method that determined about 1/4 of the nucleotides in the 16S rRNA (the best technology available at the time). This amount of data greatly exceeded anything else then available. Using newer methods, it is now routine to determine the Carl Woese sequence of the entire 16S rRNA molecule. Today, the accumulated 16S rRNA sequences (about 10,000) constitute the largest body of data available for inferring relationships among organisms.
  30. 30. What Sequence to Use ? An example of genes in this category are those that define the ribosomal RNAs (rRNAs). Most prokaryotes have three rRNAs, called the 5S, 16S and 23S rRNA. Namea Size (nucleotides) Location 5S 120 Large subunit of ribosome 16S 1500 Small subunit of ribosome 23S 2900 Large subunit of ribosome a The name is based on the rate that the molecule sediments (sinks) in water. Bigger molecules sediment faster than small ones.
  32. 32. Other genes …
  33. 33. Molecular Clock (MC) • Rate of evolution = rate of mutation • rate of evolution for any macromolecule is approximately constant over time (Neutral Theory of evolution) • For a given protein the rate of sequence evolution is approximately constant across lineages. Zuckerkandl and Pauling (1965) • This would allow speciation and duplication events to be dated accurately based on molecular data
  34. 34. Noval trees using Hox genes
  35. 35. • (a) A traditional phylogenetic tree and
  36. 36. • (a) A traditional phylogenetic tree and• (b) the new phylogenetic tree, each showing the positions of selected phyla. B, bilateria; AC, acoelomates; PC, pseudocoelomates; C, coelomates; P, protostomes; L, lophotrochozoa; E, ecdysozoa; D, deuterostomes.
  37. 37. Molecular Clock (MC) • Local and approximate molecular clocks more reasonable – one amino acid subst. 14.5 My – 1.3 10-9 substitutions/nucleotide site/year – Relative rate test (see further) • ((A,B),C) then measure distance between (A,C) & (B,C)
  38. 38. Proteins evolve at highly different rates Rate of Change Theoretical Lookback Time (PAMs / 100 myrs) (myrs) Pseudogenes 400 45 Fibrinopeptides 90 200 Lactalbumins 27 670 Lysozymes 24 850 Ribonucleases 21 850 Haemoglobins 12 1500 Acid proteases 8 2300 Cytochrome c 4 5000 Glyceraldehyde-P dehydrogenase2 9000 Glutamate dehydrogenase 1 18000 PAM = number of Accepted Point Mutations per 100 amino acids.
  39. 39. Phylogenetics Introduction Definitions Species concept Examples The Tree-of-life Phylogenetics Methodologies Algorithms Distance Methods Maximum Likelihood Maximum Parsimony Rooting Statistical Validation Conclusions Orthologous genes Horizontal Gene Transfer Phylogenomics Practical Approach: PHYLIP Weblems
  40. 40. Multiple Alignment Method
  41. 41. 4-steps • align • select method (evolutionary model) – Distance – ML – MP • generate tree • validate tree
  42. 42. Some definitions …
  43. 43. Distance matrix methods (upgma, nj, Fitch,...) • Convert sequence data into a set of discrete pairwise distance values (n*(n-1)/2), arranged into a matrix. Distance methods fit a tree to this matrix. • The phylogenetic topology tree is constructed by using a cluster analysis method (like upgma or nj methods).
  44. 44. Distance matrix methods (upgma, nj, Fitch,...)
  45. 45. Distance matrix methods (upgma, nj, Fitch,...) CGT
  46. 46. Distance matrix methods (upgma, nj, Fitch,...) Since we start with A,p(A)=1
  47. 47. Distance matrix methods (upgma, nj, Fitch,...) D=evolutionary distance ~ tijd F = dissimilarity ~ (1 – PX(t)) F~1– d
  48. 48. Distance matrix methods (upgma, nj, Fitch,...)
  49. 49. Unweighted Pair Group Method with Arithmatic Mean (UPGMA)
  50. 50. Unweighted Pair Group Method with Arithmatic Mean (UPGMA)
  51. 51. Unweighted Pair Group Method with Arithmatic Mean (UPGMA)
  52. 52. Unweighted Pair Group Method with Arithmatic Mean (UPGMA)
  53. 53. Distance matrix methods: Summary
  54. 54. Distance matrix methods (upgma, nj, Fitch,...) • The phylogeny makes an estimation of the distance for each pair as the sum of branch lengths in the path from one sequence to another through the tree. easy to perform ; quick calculation ; fit for sequences having high similarity scores ; • drawbacks : the sequences are not considered as such (loss of information) ; all sites are generally equally treated (do not take into account differences of substitution rates ) ; not applicable to distantly divergent sequences.
  55. 55. Maximum likelihood • In this method, the bases (nucleotides or amino acids) of all sequences at each site are considered separately (as independent), and the log-likelihood of having these bases are computed for a given topology by using a particular probability model. • This log-likelihood is added for all sites, and the sum of the log- likelihood is maximized to estimate the branch length of the tree.
  56. 56. Maximum likelihood
  57. 57. Maximum likelihood • This procedure is repeated for all possible topologies, and the topology that shows the highest likelihood is chosen as the final tree. • Notes : ML estimates the branch lengths of the final tree ; ML methods are usually consistent ; ML is extented to allow differences between the rate of transition and transversion. • Drawbacks need long computation time to construct a tree.
  58. 58. Maximum likelihood
  59. 59. Maximum Parsimony Parsimony criterion • It consists of determining the minimum number of changes (substitutions) required to transform a sequence to its nearest neighbor. Maximum Parsimony • The maximum parsimony algorithm searches for the minimum number of genetic events (nucleotide substitutions or amino-acid changes) to infer the most parsimonious tree from a set of sequences.
  60. 60. Maximum Parsimony Occam’s RazorEntia non sunt multiplicanda praeter necessitatem. William of Occam (1300-1349) The best tree is the one which requires the least number of substitutions
  61. 61. Maximum Parsimony • The best tree is the one which needs the fewest changes. – If the evolutionary clock is not constant, the procedure generates results which can be misleading ; – within practical computational limits, this often leads in the generation of tens or more "equally most parsimonious trees" which make it difficult to justify the choice of a particular tree ; – long computation time to construct a tree.
  62. 62. Maximum Parsimony: Branch Node A or B ?
  63. 63. Maximum Parsimony: A requires 5 mutaties
  64. 64. Maximum Parsimony: B (and propagating A->B) requires only 4 mutations
  65. 65. Maximum Parsimony • The best tree is the one which needs the fewest changes. • Problems : – If the evolutionary clock is not constant, the procedure generates results which can be misleading ; – within practical computational limits, this often leads in the generation of tens or more "equally most parsimonious trees" which make it difficult to justify the choice of a particular tree ; – long computation time to construct a tree.
  66. 66. Phylogenetics Introduction Definitions Species concept Examples The Tree-of-life Phylogenetics Methodologies Algorithms Distance Methods Maximum Likelihood Maximum Parsimony Rooting Statistical Validation Conclusions Orthologous genes Horizontal Gene Transfer Phylogenomics Practical Approach: PHYLIP Weblems
  67. 67. Comparative evaluation of different methods There is at present no statistical methods which allow comparisons of trees obtained from different phylogenetic methods, nevertheless many studies have been made to compare the relative consistency of the existing methods.
  68. 68. Comparative evaluation of different methods The consistency depends on many factors, among these the topology and branch lengths of the real tree, the transition/transversion rate and the variability of the substitution rates. One expects that if sequences have strong phylogenetic relationship, different methods will show the same phylogenetic tree
  69. 69. Comparison of methods • Inconsistency • Neighbour Joining (NJ) is very fast but depends on accurate estimates of distance. This is more difficult with very divergent data • Parsimony suffers from Long Branch Attraction. This may be a particular problem for very divergent data • NJ can suffer from Long Branch Attraction • Parsimony is also computationally intensive • Codon usage bias can be a problem for MP and NJ • Maximum Likelihood is the most reliable but depends on the choice of model and is very slow • Methods may be combined
  70. 70. Rooting the Tree • In an unrooted tree the direction of evolution is unknown • The root is the hypothesized ancestor of the sequences in the tree • The root can either be placed on a branch or at a node • You should start by viewing an unrooted tree
  71. 71. Automatic rooting • Many software packages will root trees automaticall (e.g. mid-point rooting in NJPlot) • Sometimes two trees may look very different but, in fact, differ only in the position of the root • This normally involves assumptions… BEWARE!
  72. 72. Rooting Using an Outgroup 1. The outgroup should be a sequence (or set of sequences) known to be less closely related to the rest of the sequences than they are to each other 2. It should ideally be as closely related as possible to the rest of the sequences while still satisfying condition 1 The root must be somewhere between the outgroup and the rest (either on the node or in a branch)
  73. 73. How confident am I that my tree is correct? Bootstrap values Bootstrapping is a statistical technique that can use random resampling of data to determine sampling error for tree topologies
  74. 74. Bootstrapping phylogenies• Characters are resampled with replacement to create many bootstrap replicate data sets• Each bootstrap replicate data set is analysed (e.g. with parsimony, distance, ML etc.)• Agreement among the resulting trees is summarized with a majority-rule consensus tree• Frequencies of occurrence of groups, bootstrap proportions (BPs), are a measure of support for those groups
  75. 75. Bootstrapping - an example Ciliate SSUrDNA - parsimony bootstrap Ochromonas (1) Symbiodinium (2) 100 Prorocentrum (3) Euplotes (8) 84 Tetrahymena (9) 96 Loxodes (4) 100 Tracheloraphis (5) 100 Spirostomum (6) 100 Gruberia (7) Majority-rule consensus
  76. 76. Bootstrap - interpretation • Bootstrapping is a very valuable and widely used technique (it is demanded by some journals) • BPs give an idea of how likely a given branch would be to be unaffected if additional data, with the same distribution, became available • BPs are not the same as confidence intervals. There is no simple mapping between bootstrap values and confidence intervals. There is no agreement about what constitutes a ‘good’ bootstrap value (> 70%, > 80%, > 85% ????) • Some theoretical work indicates that BPs can be a conservative estimate of confidence intervals • If the estimated tree is inconsistent all the bootstraps in the world won’t help you…..
  77. 77. Jack-knifing • Jack-knifing is very similar to bootstrapping and differs only in the character resampling strategy • Jack-knifing is not as widely available or widely used as bootstrapping • Tends to produce broadly similar results
  78. 78. Statistical evaluation of the obtained phylogenetic trees At present only sampling techniques allow testing the topology of a phylogenetic tree Bootstrapping » It consists of drawing columns from a sample of aligned sequences, with replacement, until one gets a data set of the same size as the original one. (usually some columns are sampled several times others left out) Half-Jacknife » This technique resamples half of the sequence sites considered and eliminates the rest. The final sample has half the number of initial number of sites without duplication.
  79. 79. Weblems W6.1: The growth hormones in most mammals have very similar ammo acid sequences. (The growth hormones of the Alpaca, Dog Cat Horse, Rabbit, and Elephant each differ from that of the Pig at no more than 3 positions out of 191.) Human growth hormone is very different, differing at 62 positions. The evolution of growth hormone accelerated sharply in the line leading to humans. By retrieving and aligning growth hormone sequences from species closely related to humans and our ancestors, determine where in the evolutionary tree leading to humans the accelerated evolution of growth hormone took place. W6.2: Humans are primates, an order that we, apes and monkeys share with lemurs and tarsiers. On the basis of the Beta-globin gene cluster of human, a chimpanzee, an old-world monkey, a new-world monkey, a lemur, and a tarsier, derive a phylogenetic tree of these groups. W6.3: Primates are mammals, a class we share with marsupials and monotremes; Extant marsupials live primarily in Australia, except for the opossum, found also in North and South America. Extant monotremes are limited to two animals from Australia: the platypus and echidna. Using the complete mitochondnal genome from human, horse (Equus caballus), wallaroo (Macropus robustus), American opossum (Didelphis mrgimana), and platypus (Ormthorhynchus anatmus), draw an evolutionary tree, indicating branch lengths. Are monotremes more closely related to placental mammals or to marsupials? W6.4: Mammals are vertebrates, a subphylum that we share with fishes, sharks, birds and reptiles, amphibia, and primitive jawless fishes (example: lampreys). For the coelacanth (Latimeria chalumnae), the great white shark (Carcharodon carcharias), skipjack tuna (Katsuwonus pelamis), sea lamprey (Petromyzon marinus), frog (Rana Ripens), and Nile crocodile (Crocodylus niloticus), using sequences of cytochromes c and pancreatic ribonucleases, derive evolutionary trees of these species.