Phylogenetic Inference - Lecture Slides | BSC 5936, Exams of Biology

Download Phylogenetic Inference - Lecture Slides | BSC 5936 and more Exams Biology in PDF only on Docsity! Phylogenetic Inference • Involves an attempt to estimate the evolutionary history of a collection of organisms (taxa) or a family of genes • Two major components – Estimation of the evolutionary tree (branching order) – Using estimated trees (phylogenies) as analytical framework for further evolutionary study • Traditional role: systematics and classification Example 1: Closest living relatives of humans Orangutans 15-30 Humans Bonobos Gorillas Chimpanzees MYA 0 Pre-molecular view (morphology) MYA Chimpanzees Orangutans Humans Bonobos Gorillas 014 Emerging picture from mtDNA, most nuclear genes, DNA/DNA hybridization Other interesting applications Studying dynamics of microbial communities: Sequence 16s rDNA to identify and quantify microbes in soil before and after pesticide exposure (many microbes are previously unknown, so study gene sequences phylogenetically to follow changes in community composition) Known sequences from database Novel microbial sequences Other interesting applications Predicting evolution of influenza viruses Lineages with many mutations in one set of positively selected codons were usually the ones which led to successful strains in subsequent seasons Other interesting applications Predicting functions of uncharacterized genes Use “character-mapping” to infer functions based on parsimonious reconstructions Many situations where similarity-based methods are inadequate, e.g.: “Typical” procedure used to infer phylogeny GorillaPan Homo sapiens Hylobates Pongo 0.1 substitutions/site A G AT T C T T C T Methods of Phylogenetic Inference • Basic components of a phylogenetic method – Evolutionary model: Mathematical construct, including assumptions, used to model the evolutionary process – Optimality criterion: numerical quantity that is maximized or minimized; provides basis for comparison among trees – Search strategy: an algorithm used to search (huge) tree space for trees that optimize the chosen criterion Common Tree Terms A E B C D Branches Lineages Edges Internal nodes Divergence points Hypothetical ancestors Root Terminal Nodes Leaf -- A, B, C, D, and E represent contemporary sequences All of these rearrangements show the same evolutionary relationships between the taxa B A C D A B D C B C A D B D A C B A C D Rooted tree 1a B A C D A B C D Slide by Caro-Beth Stewart 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 Root 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 Slide by Caro-Beth Stewart 1 3 42 1 3 4 2 1 3 4 2 Evaluating Trees Rooted binary (2N-3)!! Sequences Number of Trees 3 3 4 15 5 105 6 945 7 10395 8 135,135 9 2,027,025 10 34,459,425 11 654,729,075 12 13,749,310,575 13 316,234,143,225 14 7,905,853,580,625 15 2,134,580,4667,6875 1324 3124 1324 1324 1324 Similarity vs. Evolutionary Relationship: Similarity and relationship are not the same thing, even though evolutionary relationship is inferred from certain types of similarity. Similar: having likeness or resemblance (an observation) Related: genetically connected (an historical fact) Two taxa can be most similar without being most closely-related: Taxon A Taxon B Taxon C Taxon D 1 1 1 6 3 5 C is more similar in sequence to A (d = 3) than to B (d = 7), but C and B are most closely related (that is, C and B shared a common ancestor more recently than either did with A). Character-based methods can tease apart types of similarity and theoretically find the true evolutionary tree. Similarity = relationship only if certain conditions are met (if the distances are ‘ultrametric’). Types of Similarity Observed similarity between two entities can be due to: Evolutionary relationship: Shared ancestral characters (‘symplesiomorphies’) Shared derived characters (‘’synapomorphy’) Homoplasy (independent evolution of the same character): Convergent events (in either related on unrelated entities), Parallel events (in related entities), Reversals (in related entities) C C G G C C G G C G G C C G GT General strategy for estimating a phylogeny (reminder) 1. Get data 2. Select an optimality criterion (e.g., parsimony, least-squares distance, maximum likelihood) 3. Choose a search strategy (e.g., stepwise addition with branch swapping, branch-and-bound) 4. Evaluate optimality criterion for each tree visited during search, always keeping track of best tree(s) found Fitch parsimony (unordered/nonadditive) a c a g taa a 2 2 3 2 c a c c c g tc 3 5 4 g a g c tg 4 g g 4 4 t a t c t g tt 4 5 3 5 5 5 Parsimony variants used for molecular data Generalized parsimony: Each change can be assigned a specific cost. For example, transversions=5 and transitions=1. Transversions are changes between a purine (A/G) and a pyrimidine (C/T). Transitions are changes between two purines or between two pyrimidines. A G C T A A a C T {ct} Six Steps = 1step = 5 steps Parsimony variants used for molecular data Generalized parsimony (Tv=5 and Ti=1) a aa c a g ta 10 6 11 6 c ca c c g tc 25 11 25 12 g ga c g g tg 13 8 12 8 t ta c t g tt 25 12 25 11 Parsimony variants used for molecular data A C!!! G T A C!!! G T A C!!! G T A C!!! G T A C!!! G T A G C T C A C!!! G T A C!!! G T A C!!! G T A C!!! G T ∞∞∞ ∞ ∞ ∞ ∞ ∞ ∞ ∞ ∞ ∞ ∞ ∞ ∞0 0 0 0 0 (0+1) (1+1)(1+0) (1+1) 1 2 1 2 (0+1)(1+1) (1+0)(1+1) 12 12 Min [(0+1),(1+2),(1+1),(1+2)]+ 3 3 3 3 4 3 4 4 Min [(0+2),(1+1),(1+2),(1+1)][A ] Min [(1+1),(0+2),(1+1),(1+2)]+ Min [(1+2),(0+1),(1+2),(1+1)][C ] Min [(1+1),(1+2),(0+1),(1+2)]+ Min [(1+2),(1+1),(0+2),(1+1)][G ] Min [(1+1),(1+2),(1+1),(0+2)]+ Min [(1+2),(1+1),(1+2),(0+1)][T ] Min [1]+ Min [(0+3),(1+3),(1+3),(1+3)][A ] Min [0]+ Min [(1+3),(0+3),(1+3),(1+3)][C ] Min [1]+ Min [(1+3),(1+3),(0+3),(1+3)][G ] Min [1]+ Min [(1+3),(1+3),(1+3),(0+3)][T ] A C G T A - 1 1 1 C 1 - 1 1 G 1 1 - 1 T 1 1 1 - Costs: A C!!! G T A C!!! G T A C!!! G T A C!!! G T A C!!! G T A G C T C A C!!! G T A C!!! G T A C!!! G T A C!!! G T ∞∞∞ ∞ ∞ ∞ ∞ ∞ ∞ ∞ ∞ ∞ ∞ ∞ ∞0 0 0 0 0 (0+1) (2+2)(1+0) (2+2) 1 4 1 4 (0+1)(2+2) (1+0)(2+2) 14 14 Min [(0+1),(2+4),(1+1),(2+4)]+ 4 4 4 4 6 4 6 5 Min [(0+4),(2+1),(1+4),(2+1)][A ] Min [(2+1),(0+4),(2+1),(1+4)]+ Min [(2+4),(0+1),(2+4),(1+1)][C ] Min [(1+1),(2+4),(0+1),(2+4)]+ Min [(1+4),(2+1),(0+4),(2+1)][G ] Min [(2+1),(1+4),(2+1),(0+4)]+ Min [(2+4),(1+1),(2+4),(0+1)][T ] Min [2]+ Min [(0+4),(2+4),(1+4),(2+4)][A ] Min [0]+ Min [(2+4),(0+4),(2+4),(1+4)][C ] Min [2]+ Min [(1+4),(2+4),(0+4),(2+4)][G ] Min [1]+ Min [(2+4),(1+4),(2+4),(0+4)][T ] A C G T A - 2 1 2 C 2 - 2 1 G 1 2 - 2 T 2 1 2 - Costs: Faster algorithms for special cases • Farris (1970) algorithm for ordered characters • Fitch (1971) algorithm for unordered characters • Assign “state sets” to terminal taxa based on observed data, and initialize tree length to 0 • Traverse tree from tips to root; for each node consider state sets of two immediate descendants (children) – If child state sets have a nonempty intersection, new state set equals this intersection – Otherwise, make new state set equal to the union of the two child state sets, and add 1 to the tree length