construcción de cladogramas y reconstrucción filogenética

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

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Human

Mouse Rat

Dog HumanMouse

Rat

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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

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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