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Distance Matrix Methods: Models of Evolution

Distance Matrix Methods: Models of Evolution. Anders Gorm Pedersen Molecular Evolution Group Center for Biological Sequence Analysis Technical University of Denmark gorm@cbs.dtu.dk. Distance Matrix Methods. Construct multiple alignment of sequences

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Distance Matrix Methods: Models of Evolution

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  1. Distance Matrix Methods:Models of Evolution Anders Gorm Pedersen Molecular Evolution Group Center for Biological Sequence Analysis Technical University of Denmark gorm@cbs.dtu.dk

  2. Distance Matrix Methods • Construct multiple alignment of sequences • Construct table listing all pairwise differences (distance matrix) • Construct tree from pairwise distances Gorilla : ACGTCGTA Human : ACGTTCCT Chimpanzee: ACGTTTCG Ch 1 1 1 Hu 2 Go

  3. Optimal Branch Lengths: Least Squares • Fit between given tree and observed distances can be expressed as “sum of squared differences”: Q = (Dij - dij)2 • Find branch lengths that minimize Q - this is the optimal set of branch lengths for this tree. S2 S1 a c b e d S3 S4 Distance along tree j>i D12 d12 = a + b + c D13 d13 = a + d D14 d14 = a + b + e D23 d23 = d + b + c D24 d24 = c + e D34 d34 = d + b + e Goal:

  4. Superimposed Substitutions • Actual number of evolutionary events: 5 • Observed number of differences: 2 • Distance is (almost) always underestimated ACGGTGC C T GCGGTGA

  5. Model-based correction for superimposed substitutions • Goal: try to infer the real number of evolutionary events (the real distance) based on • Observed data (sequence alignment) • A model of how evolution occurs

  6. Jukes and Cantor Model • Four nucleotides assumed to be equally frequent (f=0.25) • All 12 substitution rates assumed to be equal • Under this model the corrected distance is: DJC = -0.75 x ln(1-1.33 x DOBS) • For instance: DOBS=0.43 => DJC=0.64

  7. Other models of evolution

  8. General Time Reversible Model Time-reversibility: The amount of change from state x to y is equal to the amount of change from y to x πA x PAG = πG x PGA => πA x πG x  = πG x πA x 

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