construcción de cladogramas y reconstrucción filogenética
DESCRIPTION
DATOS: Alineamiento de secuencias de genes Cómo podemos transformar esta información a un contexto histórico?TRANSCRIPT
Construcción de cladogramas y Reconstrucción Filogenética
DATOS: Alineamiento de secuencias de genes
Cómo podemos transformar esta información a un contexto histórico?
Patrón de Electroforesis en Campo Pulsado
Spoligotyping de aislados clínicos de M. tuberculosis
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Cepas
Dendograma y patrones RFLP de aislados clínicos de M. tuberculosis
Las bandas polimórficas son convertidas en arreglos de 0 y 1 (0=ausencia de banda, 1=presencia de banda)
• H37Rv 1100111111111111111111111111111111111111111• CDC1551 1111111111111111111111101111011110101111111• H37Ra 1100111111111111111111111111111111111111111• 430 1111111111111111111111111111111110111111111• 280 1111111111111111111111111111011110111111111• 312 1111111111101001111110111111111110111111111• 413 1110111111111111111111111100111110111111111• 467 1110111111111111111111110111111111111111111• 270 1110111111111011111111111111111110111111111• 2604 1110111111111001111111111111111111111111101• 300 1110111111111001111111111111111111111111101• 2651 1110111111101111111111110111111110111111111• 593 1110111111101011111111111111111110111111111• 372 1110111111101011111111111111111110111111111• 545 1110111111101011111111111111111110111111111• 271 1110111111101011111111111111111110111111111• 558 1110111111101011111111111111111110111111111• 397 1110111111101011111111111111111110111111111• 552 1110111111101001111111111111111110111111111• 466 1110111110111111111111110111111111111111111• 465 1110111110111111111111110111111111111111111• 340 1110111110111111111111110111111111111111111• 339 1110111110111111111111110111111111111111111• 345 1110111110111111111111110111111111111111111• 346 1110111110111111111111110111111111111111111• 452 1100111111111101111111111111110110111111111• H37Pe 1100111111111011111111111111111111111111111
Phylogeny inference
1. 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
In 1866, Ernst Haeckel coined the word “phylogeny” and presented phylogenetic trees for most known groups of living organisms.
Surf the tree of life at:http://tolweb.org/tree/phylogeny.html
The Tree of Life project
What is a tree?
A tree consists of nodes connected by branches.
Terminal nodes represent sequences or organisms for which we have data.Each is typically called a “Operational Taxonomical Unit” or OTU.
Internal nodes representhypothetical ancestors
The ancestor of all the sequences is the root of
the tree
A tree is a mathematical structure which is used to modelthe actual evolutionary history of a group of sequences or organisms, i.e. an evolutionary hypothesis.
Bifurcating
Polytomies: Soft vs. Hard• Soft: designate a lack of information about the
order of divergence.• Hard: the hypothesis that multiple divergences
occurred simultaneously
Types of Trees
Multifurcating
Polytomy
Trees
Types of Trees
Networks
Only one path between any pair of nodes
More than one path between any pair of nodes
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.
A B C B A C
B ACA BC
A - GCTTGTCCGTTACGATB – ACTTGTCTGTTACGATC – ACTTGTCCGAAACGATD - ACTTGACCGTTTCCTTE – AGATGACCGTTTCGATF - ACTACACCCTTATGAG
Given a multiple alignment, how do we construct the tree?
?
Construction of a distance tree using clustering with the Unweighted Pair Group Method with Arithmatic Mean (UPGMA)
A B C D E B 2 C 4 4 D 6 6 6 E 6 6 6 4 F 8 8 8 8 8
From http://www.icp.ucl.ac.be/~opperd/private/upgma.html
A - GCTTGTCCGTTACGATB – ACTTGTCTGTTACGATC – ACTTGTCCGAAACGATD - ACTTGACCGTTTCCTTE – AGATGACCGTTTCGATF - ACTACACCCTTATGAG
First, construct a distance matrix:
First round
dist(A,B),C = (distAC + distBC) / 2 = 4 dist(A,B),D = (distAD + distBD) / 2 = 6 dist(A,B),E = (distAE + distBE) / 2 = 6dist(A,B),F = (distAF + distBF) / 2 = 8
A B C D E B 2 C 4 4 D 6 6 6 E 6 6 6 4 F 8 8 8 8 8
A,B C D E
C 4 D 6 6 E 6 6 4 F 8 8 8 8
UPGMA
Choose the most similar pair, cluster them together and calculate the new distance matrix.
A,B C D E
C 4 D 6 6 E 6 6 4 F 8 8 8 8
A,B C D,E
C 4
D,E 6 6
F 8 8 8
Second round
Third round
UPGMA
AB,C D,E
D,E 6
F 8 8
ABC,DE
F 8
Fourth round
Fifth round
UPGMA
Note the this method identifies the root of the tree.
• 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'.
UPGMA assumes a molecular clock
A B C
For any three taxa, the two greatest distances are equal.
The three-point condition:
A B C D E B 5 C 4 7
D 7 10 7
E 6 9 6 5
F 8 11 8 9 8
UPGMA fails when rates of evolution are not constantA tree in which the evolutionary rates are not equal
From http://www.icp.ucl.ac.be/~opperd/private/upgma.html
(Neighbor joining will get the right tree in this case).
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
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.
0123456789012345W - ACTTGACCCTTACGATX – AGCTGGCCCTGATTACY – AGTTGACCATTACGATZ - AGCTGGTCCTGATGAC
W
Y
X
Z
123456789012345678901 Mouse CTTCGTTGGATCAGTTTGATA Rat CCTCGTTGGATCATTTTGATADog CTGCTTTGGATCAGTTTGAAC Human CCGCCTTGGATCAGTTTGAAC------------------------------------Invariant * * ******** *****Variant ** * * **------------------------------------Informative ** ** Non-inform. * *
Start by classifying the sites:
Maximum Parsimony
123456789012345678901 Mouse CTTCGTTGGATCAGTTTGATA Rat CCTCGTTGGATCATTTTGATADog CTGCTTTGGATCAGTTTGAAC Human CCGCCTTGGATCAGTTTGAAC
** * Mouse
Rat
Dog
Human
Mouse
Rat
Dog
Human
Mouse Rat
Dog Human
Mouse
Rat
Dog
Human
Mouse
Rat
Dog
Human
Mouse Rat
Dog HumanMouse
Rat
Dog
Human
Mouse
Rat
Dog
Human
Mouse Rat
Dog Human
Site 5:G
G
T
C
T
T
T
C
T
C
G
G
T
G
T
C
G
T
G
C
T
C
T
G
G
T
T
T
T
G
G
C
C
C
T
G
GG
CC
GG
GG
CT
GG
TG
CC
GT
Site 2:
Site 3:
123456789012345678901 Mouse CTTCGTTGGATCAGTTTGATA Rat CCTCGTTGGATCATTTTGATADog CTGCTTTGGATCAGTTTGAAC Human CCGCCTTGGATCAGTTTGAACInformative ** **
Mouse
Rat
Dog
Human
Mouse
Rat
Dog
Human
Mouse Rat
Dog Human
3 0 1
Maximum Parsimony
EVOLUCIÓN IN VITRO POR INTERMEDIO DE PCR