14 Phylogenetics(II)

14 Phylogenetics(II) - Introduction to Bioinformatics/...

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Introduction to Bioinformatics/ Elements of Bioinformatics Phylogenetics (II)
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Multiple alignment rooted tree A B C D E F unrooted tree Phylogenetic trees
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Number of possible tree topologies 1 unrooted tree with 3 taxa 3 possible unrooted trees with 4 taxa 15 possible unrooted trees with 5 taxa A 5 trees 5 trees A A A A A A A A A A B B B B B B B B B B E E E E E E E E D D D D D D D D D D D C C C C C C C C C C D
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7 possible rooted trees from one unrooted tree with 5 taxa. There are 7*15=105 rooted trees with 5 taxa. A B D C E A A A A A A A B B B B B B B C C C C C C C D D D D D D D E E E E E E E Number of possible tree topologies Unrooted tree
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Number of unrooted trees = 3)! (n 2 5)! (2n 3 n Number of rooted trees = 2)! (n 2 3)! n (2 trees unrooted of . no * 3) n- (2 2 n = where n = no. of taxa Only one of the possible trees topologies is the true tree. 3.4 x 10 139 2.2 x 10 137 80 2.8 x 10 76 2.8 x 10 74 50 8.2 x 10 21 2.2 x 10 20 20 3.4 x 10 7 2.0 x 10 6 10 105 15 5 15 3 4 No. of rooted trees No. of unrooted trees No. of Taxa Number of possible tree topologies
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Maximum Parsimony Maximum Likelihood Character State Minimum Evolution Fitch-Margoliash UPGMA Neighbour joining Distance Matrix Exhaustive Search Stepwise Clustering Making a phylogeny • For molecular sequences, treat each column of the multiple alignment as a character
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Clustering methods to build phylogenetic trees. Exhaustive search methods to build phylogenetic trees. The tree(s) with the best score is chosen to represent the phylogeny. From Page and Holmes (1998) Clustering and tree searching methods
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Tree-building methods • Distance methods – Clustering methods Unweighted pair group method with arithmetic mean (UPGMA): group pair that shares the smallest distance Neighbour joining (NJ): find neighbours that result in the smallest total branch length – Tree searching methods Minimum evolution method: seek a tree with minimum sum of branch lengths Fitch-Margoliash method: seek a tree whose branch lengths best fit the observed distances (“least squares”)
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Tree-building methods • Character-state methods: – Maximum parsimony (MP) find the tree that requires the fewest number of changes to account for the alignment data – Maximum likelihood (ML) infer evolutionary tree by finding the tree that maximize the probability of observing the data according to a evolutionary model
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Estimating distances • Observed distance – Proportion of sites that differ between two sequences – Gaps are usually discarded – Usually underestimate the true distance
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Observed difference underestimates actual difference Single substitution 1 change, 1 difference A G A AG Multiple substitution 2 changes, 1 difference A G A CG AC Coincidental substitution 2 changes, 1 difference C G A Parallel substitution 2 changes, no difference G G A Back substitution 2 changes, no difference A A A CA Convergent substitution 3 changes, no difference G G A
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Distance correction for nucleotide sequences • Jukes-Cantor (JC) model – One rate of change ( α ) for all nucleotide substitutions.
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14 Phylogenetics(II) - Introduction to Bioinformatics/...

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