1. Construcción de cladogramas y Reconstrucción Filogenética

2. DATOS: Alineamiento de secuencias de genes
Cómo podemos transformar esta información a un contexto histórico?

3. Patrón de Electroforesis en Campo Pulsado

4. Spoligotyping de aislados clínicos de M. tuberculosis
Cepas 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43

5. Dendograma y patrones RFLP de aislados clínicos de M. tuberculosis

6. Las bandas polimórficas son convertidas en arreglos de 0 y 1 (0=ausencia de banda, 1=presencia de banda)
H37Rv
CDC
H37Ra
H37Pe

7. Phylogeny inference Distance based methods -Pair wise distance matrix
-Adjust tree branch lengths to fit the distance matrix (ex. Minimum squares, Neighbor joining)
2. Character based methods
-Parsimony
-Maximum likelihood or model based evolution

8. In 1866, Ernst Haeckel coined the word “phylogeny” and presented phylogenetic trees for most known groups of living organisms.

9. Surf the tree of life at:
The Tree of Life project
Surf the tree of life at:

10. What is a tree?
A tree is a mathematical structure which is used to model
the actual evolutionary history of a group of sequences or organisms, i.e. an evolutionary hypothesis.
A tree consists of nodes connected by branches.
The ancestor of all the sequences is the root of the tree
Internal nodes represent
hypothetical ancestors
Terminal nodes represent sequences or
organisms for which we have data.
Each is typically called a “Operational Taxonomical Unit” or OTU.

11. Types of Trees Bifurcating Multifurcating Polytomy
Polytomies: Soft vs. Hard
Soft: designate a lack of information about the order of divergence.
Hard: the hypothesis that multiple divergences occurred simultaneously

12. Types of Trees Trees Networks Only one path between any pair of nodes
More than one path between any pair of nodes

13. Comments on Trees Trees give insights into underlying data
Identical trees can appear differently depending upon the method of display
Information maybe lost when creating the tree. The tree is not the underlying data.

14. Given a multiple alignment, how do we construct the tree?
A - GCTTGTCCGTTACGAT
B – ACTTGTCTGTTACGAT
C – ACTTGTCCGAAACGAT
D - ACTTGACCGTTTCCTT
E – AGATGACCGTTTCGAT
F - ACTACACCCTTATGAG ?

15. Construction of a distance tree using clustering with the Unweighted Pair Group Method with Arithmatic Mean (UPGMA)
First, construct a distance matrix:
A - GCTTGTCCGTTACGAT
B – ACTTGTCTGTTACGAT
C – ACTTGTCCGAAACGAT
D - ACTTGACCGTTTCCTT
E – AGATGACCGTTTCGAT
F - ACTACACCCTTATGAG  A  B  C  D  E  2  4  6  F  8
From

16. UPGMA
First round  A  B  C  D  E  2  4  6  F  8
dist(A,B),C = (distAC + distBC) / 2 = 4
dist(A,B),D = (distAD + distBD) / 2 = 6
dist(A,B),E = (distAE + distBE) / 2 = 6
dist(A,B),F = (distAF + distBF) / 2 = 8
 A,B  C  D  E  4  6  F  8
Choose the most similar pair,
cluster them together and calculate the new distance matrix.

17. UPGMA
Second round
 A,B  C  D  E  4  6  F  8
Third round
 A,B  C
 D,E  4  6  F  8

18. UPGMA AB,C D,E 6 F 8 ABC,DE F 8 Fourth round Fifth round 6  F  8
Fifth round
 ABC,DE  F  8
Note the this method identifies the root of the tree.

19. UPGMA assumes a molecular clock
The UPGMA clustering method is very sensitive to unequal evolutionary rates (assumes that the evolutionary rate is the same for all branches).
Clustering works only if the data are ultrametric
Ultrametric distances are defined by the satisfaction of the 'three-point condition'.
The three-point condition: A B C
For any three taxa, the two greatest distances are equal.

20. (Neighbor joining will get the right tree in this case.)
UPGMA fails when rates of evolution are not constant
A tree in which the evolutionary rates are not equal  A  B  C  D  E  5  4  7
 10  6  9  F  8
 11
(Neighbor joining will get the right tree in this case.)
From

21. Character state methods
MAXIMUM PARSIMONY
Logic:
Examine each column in the multiple alignment of the sequences.
Examine all possible trees and choose among them according to some optimality criteria
Method we’ll talk about
Maximum parsimony

22. Maximum Parsimony
Simpler hypotheses are preferable to more complicated ones and that as hoc hypotheses should be avoided whenever possible (Occam’s Razor).
Thus, find the tree that requires the smallest number of evolutionary changes.
W - ACTTGACCCTTACGAT
X – AGCTGGCCCTGATTAC
Y – AGTTGACCATTACGAT
Z - AGCTGGTCCTGATGAC W X Y Z

23. Maximum Parsimony Start by classifying the sites:
Mouse CTTCGTTGGATCAGTTTGATA
Rat CCTCGTTGGATCATTTTGATA
Dog CTGCTTTGGATCAGTTTGAAC
Human CCGCCTTGGATCAGTTTGAAC
Invariant * * ******** *****
Variant ** * * **
Informative ** **
Non-inform. * *

24. Mouse CTTCGTTGGATCAGTTTGATA Rat CCTCGTTGGATCATTTTGATA
Mouse CTTCGTTGGATCAGTTTGATA
Rat CCTCGTTGGATCATTTTGATA
Dog CTGCTTTGGATCAGTTTGAAC
Human CCGCCTTGGATCAGTTTGAAC
** *
Mouse
Rat
Dog
Human G G T T T G G G G G G G
Site 5: G C G C T C
Mouse
Rat
Dog
Human T T T C C T C C T C T C
Site 2: C C C C T C
Mouse
Rat
Dog
Human T T G G G G G T T G G T
Site 3: T G T G G G

25. Maximum Parsimony
Mouse CTTCGTTGGATCAGTTTGATA
Rat CCTCGTTGGATCATTTTGATA
Dog CTGCTTTGGATCAGTTTGAAC
Human CCGCCTTGGATCAGTTTGAAC
Informative ** **
Mouse
Rat
Dog
Human 3 1

30. EVOLUCIÓN IN VITRO POR INTERMEDIO DE PCR