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Building Phylogenies. Phylogenetic (evolutionary) trees Human Gorilla Chimp Gibbon Orangutan Describe evolutionary relationships between species Cannot.

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Presentation on theme: "Building Phylogenies. Phylogenetic (evolutionary) trees Human Gorilla Chimp Gibbon Orangutan Describe evolutionary relationships between species Cannot."— Presentation transcript:

1 Building Phylogenies

2 Phylogenetic (evolutionary) trees Human Gorilla Chimp Gibbon Orangutan Describe evolutionary relationships between species Cannot be known with certainty! Nevertheless, phylogenies can be useful Human Gorilla Chimp Gibbon Orangutan ?? or

3 Applications of Phylogenetic Analysis Inferring function –Closely related sequences occupy neighboring branches of tree Tracking changes in rapidly evolving populations (e.g., viruses) –Which genes are under selection?

4 Phyloinformatics Comparative analysis through phylogenies helps to understand biological function Exploit phylogenies for data mining

5 Disease Transmission and Medical Forensics

6 Discovering Snake Antivenin

7 Methods Distance-based Parsimony Maximum likelihood

8 Distance Matrices a0 b60 c730 d141090 abcd a b c d 123450678

9 Ultrametric Matrices a0 b20 c660 d1010100 abcd a b c d 123450

10 Least Squares D ij = distance between i and j in matrix d ij = distance between i and j in tree Objetive: Find tree that minimizes

11 Characters a0111a0111 ABCDABCD c0011c0011 d0110d0110 e0001e0001 f1000f1000 b0111b0111 Characters are represented using matrices A character can be a morphological trait or a letter in a column of an alignment.

12 Parsimony a0111a0111 ABCDABCD c0011c0011 d0110d0110 e0001e0001 f1000f1000 b0111b0111 ABC D f a, b d c ed Goal: Find the tree with least number of evolutionary changes

13 Markov models on trees Observed: The species labeling the leaves Hidden: The ancestral states Transition probabilities: The mutation probabilities Assumptions: –Only mutations are allowed –Sites are independent –Evolution at each site occurs according to a Markov process

14 Models of evolution at a site Transition probability matrix: M = [m ij ], i, j  {A, C, T, G} where m ij = Prob(i  j mutation in 1 time unit) Different branches of tree may have different lengths

15 The probability of an assignment AGCT Probability = m TG · m GA · m GG · m TT · m TC · m TT G T T

16 Ancestral reconstruction: most likely assignment AGCT L* = max X,Y,Z {m XY · m YA · m YG · m XZ · m ZC · m ZT } Y X Z


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