Phylogeny 2009

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Dr.G.P.L.Jayasree

PHYLOGENETIC ANALYSIS AND PHYLOGENETIC TREES

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TREE OF LIFE

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Evolution What we can see

are the present-

day species

Offspring lookslike its parents

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Biological FoundationsEvolution is driven by Inheritance Variation Mutations

Phenotype Genotype

Recombination Nature selects: survival of the fittest

All organisms share a common ancestry

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Terminology Phylogeny

The evolutionary relationships among

organisms, based on a common ancestor Phylogenetics

Area of research concerned with finding the

genetic relationships between species (Greek: phylon = race and genetic = birth

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Phylogeny

Orangutan Gorilla Chimpanzee Human

From the Tree of the Life Website,

University of Arizona

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Applications of phylogenetic trees

Evolution studies

Systematic biology

Medical research and epidemiology

Ecology

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Construction of phylogenetic

trees Classic phylogenetic analysis uses

morphological features

Anatomy, size, number of legs, beak

shape

Modern phylogenetic analysis uses

molecular informationGenetic material (DNA and protein sequences)

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Advantages of molecularphylogenetic analysis

Analogous features (share common

function, but NOT common ancestry)

can be misleading

DNA sequences more simple to model,

we only have the four states A, C, G, T DNA samples for sequence analysis

easy to prepare

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Phylogenetic Trees A graph representing the

evolutionary history of a sequence

Relationship of one sequence toother sequences

Dissect the order of appearance of

insertions, deletions, and mutations Predict function, observe changes

A

B

C

D

Simple

Tree

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Parts of a phylogenetic treeNode

Root

Outgroup

Ingroup

Branch

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Tree ShapesRooted Un-rooted

Branches intersect atNodes

Leaves are the topmost branches

A

B

C

D

A

B

C

D

A

B

C

D

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Tree Characteristics Tree Properties

Clade: all the descendants of a commonancestor represented by a node

Distance: number of changes that havetaken place along a branch

Tree Types Cladogram: shows the branching order of

nodes

Phylogram: shows branching order anddistances

A

B

C

D

.035

.009

.057

.044

.012

.016

Phylogram

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Taxon A

Taxon B

Taxon C

Taxon D

1

1

1

6

3

5

genetic change

Taxon A

Taxon B

Taxon C

Taxon D

time

Taxon A

Taxon B

Taxon C

Taxon D

no meaning

Three types of trees

Cladogram Phylogram Ultrametric tree

All show the same evolutionary relationships, or branching orders, between the taxa.

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Phylogenetic trees diagram the evolutionaryrelationshipsbetween the taxa

((A,(B,C)),(D,E)) = The above phylogeny as nested parentheses

Taxon A

Taxon B

Taxon C

Taxon E

Taxon D

No meaning to thespacing between thetaxa, or to the order inwhich they appear fromtop to bottom.

This dimension either can have no scale (for cladograms),can be proportional to genetic distance or amount of change

(for phylograms or additive trees), or can be proportionalto time (for ultrametric trees or true evolutionary trees).

These say that B and C are more closely related to each other than either is to A,and that A, B, and C form a clade that is a sister group to the clade composed of

D and E. If the tree has a time scale, then D and E are the most closely related.

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Completely unresolvedor "star" phylogeny

Partially resolvedphylogeny

Fully resolved,bifurcating phylogeny

A A A

B

B B

C

C

C

E

E

E

D

D D

Polytomy or multifurcation A bifurcation

The goal of phylogeny inference is to resolve thebranching orders of lineages in evolutionary trees:

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C-B Stewart, NHGRI lecture,12/5/00

There are three possible unrooted treesfor four taxa (A, B, C, D)

A C

B D

Tree 1

A B

C D

Tree 2

A B

D C

Tree 3

Phylogenetic tree building (or inference) methods are aimed atdiscovering which of the possible unrooted trees is "correct".

We would like this to be the true biological tree that is, onethat accurately represents the evolutionary history of the taxa.However, we must settle for discovering the computationallycorrector optimaltree for the phylogenetic method of choice.

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The number of unrooted trees increases in a greaterthan exponential manner with number of taxa

# Taxa ( N)

3

4

5

6

78

9

10

.

.

.

.

30

# Unrooted trees

1

3

15

105

94510,935

135,135

2,027,025

.

.

.

.

3.58 x 1036

CA

B D

A B

C

A D

B E

C

A D

B E

C

F (2n 5)! / ((n-3)!2n-3)

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A

BC

Root

D

Unrooted tree

Note that in this rooted tree, taxon A is mostclosely related to taxon B, and together theyare equally distantly related to taxa C and D.

C D

Root

Rooted tree

A

B

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An unrooted, four-taxon tree theoretically can be rooted in fivedifferent places to produce five different rooted trees

The unrooted tree 1:A C

B D

Rooted tree 1d

C

D

A

B

4

Rooted tree 1c

A

B

C

D

3

Rooted tree 1e

D

C

A

B

5

Rooted tree 1b

A

B

C

D

2

Rooted tree 1a

B

A

C

D

1

These trees showfive different evolutionary relationships among the taxa!

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By outgroup:

Uses taxa (the outgroup) that are known

to fall outside of the group of interest (theingroup). Requires some prior

knowledge about the relationships amongthe taxa. The outgroup can either bespecies (e.g., birds to root a mammaliantree) or previous gene duplicates (e.g.,a-globins to root b-globins).

There are two major ways to root trees:

A

B

C

D

10

2

3

5

2

By midpoint or distance:

Roots the tree at the midway pointbetween the two most distant taxa inthe tree, as determined by branchlengths. Assumes that the taxa areevolving in a clock-like manner. This

assumption is built into some of thedistance-based tree building methods.

outgroup

d(A,D) = 10 + 3 + 5 = 18Midpoint = 18 / 2 = 9

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Similarity vs. Evolutionary Relationship:

Similarity and relationship are notthe same thing, even though

evolutionary relationship is inferred from certain types of similarity.

Similar: having likeness or resemblance (an observation)

Related: genetically connected (an historical fact)

Two taxa can be most similar without being most closely-related:

Taxon A

Taxon B

Taxon C

Taxon D

1

1

1

6

3

5

C is more similar in sequenceto A (d= 3) than to B (d= 7),but C and B are most closelyrelated (that is, C and B shareda common ancestor more recentlythan either did with A).

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Procedure: Steps of a molecularphylogenetic analysis

1. Decide what sequences to examine

2. Determine the evolutionary distancesbetween the sequences and build

distance matrix

3. Phylogenetic tree construction

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Decide what to examine

Choose homologous sequences in

different species

Homologous sequences must, by

definition, be derived from a common

ancestral sequence Homology is not similarity

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Methods for DeterminingTrees

Sequence Based methods:

Maximum Parsimony

Maximum Likelihood

Distance Based methods:

UPGMA

Neighbor Joining

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Distance Methods

Distanceis expressed as the fractionof sites that differ between two

sequences in an alignment Sequences with the smallest number of

changes (shortest distance) are related

taxa

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Determine the evolutionarydistances and build distance matrix

For molecular data, evolutionary distances

can be the observed number of nucleotide

differences between the pairs of species. Distance matrix: simply a table showing

the evolutionary distances between all

pairs of sequences in the dataset

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Determine the evolutionary distances andbuild distance matrix - A simple example

1. AGGCCATGAAT TAAGAATAA2. AGCCCATGGATAAAGAGTAA

3. AGGACATGAATTAAGAATAA

4. AAGCCAAGAATTACGAATAA

Distance Matrix

In this example the evolutionary distance isexpressed as the number of nucleotide differences

for each sequence pair. For example, sequences 1and 2 are 20 nucleotides in length and have fourdifferences, corresponding to an evolutionarydifference of 4/20= 0.2.

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1. AGGCCATGAATTAAGAATAA2. AGCCCATGGATAAAGAGTAA

3. AGGACATGAATTAAGAATAA4. AAGCCAAGAATTACGAATAA

1 2 3 41 --- 0.2 0.05 0.15

2 --- --- 0.25 0.4

3 --- --- --- 0.2

4 --- --- --- ---

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Additive Distance Matrices

Distance based Phylogenetic

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Distance-based PhylogeneticMethods

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Distance Methods - UPGMA

UPGMA (Unweighted Pair-Group Methodwith Arithmetic mean) Sequentially find pair of taxa with smallest

distance between them, and define branching asmidpoint of two

Assumes the tree is additive and that rate ofchange is constant in all of the branches

DAB

2 D(AB)C2

D(ABC)D2

A

B

C

D

A

B

A

B

C

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Distance Methods - NJ Neighbor-Joining (NJ): useful when there are

different rates of evolution within a tree Each possible pair-wise alignment is examined. Calculate

distance from each sequence to every other sequence

Choose the pair with the lowest distance value and jointhem to produce the minimal length tree

Update distance matrix where joined node is substituted fortwo original taxa and then repeat process

A

B

C

DF

G

H A

D

B

C E

F

GH

2 11

A

D

B C

E

F

GH

2 1

3

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DNA Sequence Evolution

AAGACTT

TGGACTTAAGGCCT

-3 mil yrs

-2 mil yrs

-1 mil yrs

today

AGGGCAT TAGCCCT AGCACTT

AAGGCCT TGGACTT

TAGCCCA TAGACTT AGCGCTTAGCACAAAGGGCAT


AAGACTT

TGGACTTAAGGCCT


AAGGCCT TGGACTT

AGCGCTTAGCACAATAGACTTTAGCCCAAGGGCAT

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Phylogeny Problem

TAGCCCA TAGACTT TGCACAA TGCGCTTAGGGCAT

U V W X Y

U

V W

X

Y

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UPGMA(Unweighted pair group methodwith arithmetic mean)

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UPGMA (Unweighted Pair Group Method with Arithmetic Mean)

Assumes that rate of change along the branches of tree areconstant and distances are ultrametric (dAC < max(dAB, dBC))

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There are 12 changes in the sequence so

o + p = 12 m + n = 1512/2 = 6 15/2 = 7.5

To calculate q we need to calculate the average distance fromall sequences to each other

(MO + MP + NO + NP)/4 =(26 + 28 + 29 + 31)/4 = 28.5

q1 + 7.5 = 14.25q1 = 6.75q2 + 6.0 = 14.25q2 = 8.25

Therefore, q = 15

6.0

6.0

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Maximum parsimony

Parsimony is a special case of likelihood The tree with the smallest number of

mutations is the maximum parsimony

tree . Best tree is one where minimal changes

take place .

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Maximum parsimony (example)

Input: Four sequences

ACT

ACA

GTT

GTA

Question: which of the three trees has thebest MP scores?

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Maximum Parsimony

ACTACA GTT

GTA3

1

1

MP score = 5

ACT ACA

GTAGTT

ACA GTT

3 31

MP score = 11

GTA

ACT

GTT

ACA

ACA GTT

2 3 1

MP score = 10

ACA

GTT 31

1

3

Optimal MP tree

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Maximum Parsimony Example

1A AGAG T GCA

2AGC CG T GCG

3AGATAT C CA4AGAGAT C CG

four sequences, nine sites, threepossible unrooted trees

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Possible Trees I:

(3)AGATATCCA

AGCCGTGCG AGAGATCCG

(2)AGCCGTGCG (4)AGAGATCCG

(1)AAGAGTGCA4

0

42

0

Number of Mutations: 10

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Possible Trees II:

(3)AGATATCCA

AGGAGTGCA AGAGGTCCG

(2)AGCCGTGCG

(4)AGAGATCCG

(1)AAGAGTGCA1

4

53

1


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Possible Trees III:

(3)AGATATCCA

AGGAGTGCA AGATGTCCG

(2)AGCCGTGCG

(4)AGAGATCCG

(1)AAGAGTGCA1

5

53

2


Tree I has the topology with the least number of

mutations and thus is the most parsimonious tree.

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Some sites are informative, others arenot

Informative site: there are at least twodifferent kinds of nucleotides at thesite, each of which is represented in atleast two of the sequences under study.

Only informative sites are considered

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1A AGAG T GCA

2AGC CG T GCG

3AGATAT C CA4AGAGAT C CG

Three informative columns

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1 GGA

2 GGG

3ACA4AC G

Tree 1: 4Tree 2: 5

Tree 3: 6

1

2 4

3 1

3 4

2 1

4 2

3

1

2 4

3 1

3 4

2 1

4 2

3

Column 1

Column 2

Column 3

1

2 4

3 1

3 4

2 1

4 2

3

Is a substitution

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Maximum Parsimony Problems

Small Parsimony Problem: Given the phylogeny topology, compute the

internal nodes to minimize the total number of

mutations; Used to evaluate the phylogeny;

Polynomial time solvable.

Large Parsimony Problem : Given that we have a way of determining the

score of a given phylogeny, search through allpossible phylogenies to find the best one;

Proved to be NP-complete.

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Fitchs Algorithm forSmall

Parsimony Problem Consider each site separately;

Dynamic programming style;

Constructs a set of possible states (possiblenucleotides) for each internal node;

Start at the leaves of the phylogeny. Eachleaf is labeled with the singleton set

containing the nucleotide at that particularsite.

Traverse in a post order manner (all of thechildren of the current node have been

visited before the current node).

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Parsimony Example

Score is evaluated on each positionindependetly. Scores are then summed

over all positions.

Solved independently for each position

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Maximum Parsimony

1 2 3 4 5 6 7 8 9 10

Species 1 - A G G G T A A C T G

Species 2 - A C G A T T A T T A

Species 3 - A T A A T T G T C T

Species 4 - A A T G T T G T C G

How many possible unrooted trees?

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Maximum ParsimonyHow many possible unrooted trees?

1

3

2

4

1

2

3

4

1

4

3

2

1 2 3 4 5 6 7 8 9 10

Species 1 - A G G G T A A C T G

Species 2 - A C G A T T A T T A

Species 3 - A T A A T T G T C T

Species 4 - A A T G T T G T C G

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Maximum Parsimony1 2 3 4 5 6 7 8 9 10

1 -AG G G T A A C T G

2 -AC G A T T A T T A

3 -AT A A T T G T C T

4 -AA T G T T G T C G0

0

0

1

3

2

4

1

2

3

4

1

4

3

2

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1 - A G G G T A A C T G

2 - A C G A T T A T T A

3 - A T A A T T G T C T

4 - AAT G T T G T C G0 3

0 3

0 3

1

3

2

4

1

2

3

4

1

4

3

2

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Maximum Parsimony

4

1 - G

2 - C

3 - T

4 - A

1

2

3

4A

G

C

T

C

A

G

T

C1

3

2

4C

C

G

A

T1

4

3

2C

3

3

3

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2 - A C G A T T A T T A

3 - A TAA T T G T C T

4 - A A T G T T G T C G0 3 2

0 3 2

0 3 2

1

3

2

4

1

2

3

4

1

4

3

2

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2 - A C GAT T A T T A

3 - A T AAT T G T C T

4 - A A T G T T G T C G0 3 2 2

0 3 2 1

0 3 2 2

1

3

2

4

1

2

3

4

1

4

3

2

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Maximum Parsimony

4

1 - G

2 - A3 - A

4 - G

1

2

3

4G

G

A

A

A

G

G

A

A1

3

2

4

A

G

A

A

G1

4

3

2A

2

2

1

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Maximum Parsimony

0 3 2 2 0 1 1 1 1 3 14

0 3 2 1 0 1 2 1 2 3 15

0 3 2 2 0 1 2 1 2 3 16

1

3

2

4

1

2

3

4

1

4

3

2

H i h l ti l i

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How is phylogenetic analysisdone?

Start with a simple data set of 6 nucleotidesfrom 5 species:

Look at a single character

A ACGTAAB CCTTAA

C CGTCAA

D CGTCCGE CGTCCG

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Phylogenetic Analysis cont.

Look at position 1: species A has an Awhere everyone else has a C

Look at position 3: species A has an Gwhere everyone else has a C

Look at position 2: species A and B

have a C where everyone else has a G Continue on with every position

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Phylo. Analysis cont. Then you would map these changes to a tree

Either the tree is known or this data set isused to infer a tree

Usually an outgroup is included so as to givean idea of what is the ancestral character ateach state

This is very easy with few characters and

species, but most studies include 100 specieswith over 10,000 nucleotides from eachspecies

A computer program is needed in most

studies with large data sets

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Resulting Phylogeny

The phylogeny might look like this if wemapped all the changes from our data

set:

Comparative Protein Analysis

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Comparative Protein Analysis

Identify proteins within an organism that arerelated to each other and across differentspecies

Generate an evolutionary history of related

genes Locate insertions, deletions, and substitutions

that have occurred during evolution

CREATE CREASE -RELAPSE

GREASER

(Ancestor)

Time

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Multiple Sequence Alignments Place residues in columns that are derived

from a common ancestral residue

MSA can reveal sequence patterns

Demonstration of homology between >2

sequences

Identification of functionally important sites

Protein function prediction

Structure prediction

Search for weak but significant similarities in

databases Design PCR primers for related gene

identification

Genome sequencing: contig assembly

CREAT--E-

CREAS--E-

-RELAPSE-

GREAS--ER

CREATE

CREASE

GREASER

RELAPSE

123456789

SeqA

SeqB

SeqC

SeqD

C i f h d

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Comparison of MethodsNeighbor-

joining/UPGMAMaximum parsimony Maximum likelihood

Uses only pairwisedistances

Uses only sharedderived characters

Uses all data

Minimizes distancebetween nearest

neighbors

Minimizes total distance Maximizes tree likelihoodgiven specific parameter

values

Very fast Slow Veryslow

Easily trapped in localoptima

Assumptions fail whenevolution is rapid

Highly dependent onassumed evolution model

Good for generatingtentative tree

Best option whentractable (

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1. Find Related Sequences

BLAST

www.ncbi.nih.gov/BLASTMLEICLKLVGCKSKKGLSSSSSCYLEEALQRPVASDFEPQGLSEAARWNSKENLLAGPSENDPNLFVALY

DFVASGDNTLSITKGEKLRVLGYNHNGEWCEAQTKNGQGWVPSNYITPVNSLEKHSWYHGPVSRNAAEYLLSSGINGSFLVRESESSPGQRSISLRYEGRVYHYRINTASDGKLYVSSESRFNTLAELVHHHSTVADGLITTLHYPAPKRNKPTVYGVSPNYDKWEMERTDITMKHKLGGGQYGEVYEGVWKKYSLTVAVKTLKEDTMEVEEFLKEAAVMKEIKHPNLVQLLGVCTREPPFYIITEFMTYGNLLDYLRECNRQEVNAVVLLYMATQISSAMEYLEKKNFIHRDLAARNCLVGENHLVKVADFGLSRLMTGDTYTAHAGAKFPIKWTAPESLAYNKFSIKSDVWAFGVLLWEIATYGMSPYPGIDLSQVYELLEKDYRMERPEGCPEKVYELMRACWQWNPSDRPSFAEIHQAFETMFQESSISDEVEKELGKQGVRGAVSTLLQAPELPTKTRTSRRAAEHRDTTDVPEMPHSKGQGESDPLDHEPAVSPLLPRKERGPPEGGLNEDERLLPKDKKTNLFSALIKKKKKTAPTPPKRSSSFREMDGQPERRGAGEEEGRDISNGALAFTPLDTADPAKSPKPSNGAGVPNGALRESGGSGFRSPHLWKKSSTLTSSRLATGEEEGGGSSSKRFLRSCSASCVPHGAKDTEWRSVTLPRDLQSTGRQFDSSTFGGHKSEKPALPRKRAGENRSDQVTRGTVTPPPRLVKKNEEAADEVFKDIMESSPGSSPPNLTPKPLRRQVTVAPASGLPHKEEAGKGSALGTPAAAEPVTPTSKAGSGAPGGTSKGPAEESRVRRHKHSSESPGRDKGKLSRLKPAPPPPPAASAGKAGGKPSQSPSQEAAGEAVLGAKTKATSLVDAVNSDAAKPSQPGEGLKKPVLPATPKPQSAKPSGTPISPAPVPSTLPSASSALAGDQPSSTAFIPLISTRVSLRKTRQPPERIASGAITKGVVLDSTEALCLAISRNSEQMASHSAVLEAGKNLYTFCVSYVDSIQQMRNKFAFREAINKLENNLRELQICPATAGSGPAATQDFSKLLSSVKEISDIVQR

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2. Compile & Align Sequences

Compile Sequences into FASTA format

Align clustalW

clustalX

>Human

MPALGYKFSTW

>MouseMDGSTDYGILQINS

>Rat

MKKP..

>Murine_Leukemia_Virus

MTSR.

3 A l i th li d

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3.Analysing the aligned sequencematrix

PHYLIP

POY

PAUP, GCG

And many more... (274 softwarepackages described at one website)

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PHYLIP (Phylogeny InferencePackage)

Available free in Windows/MacOS/Linuxsystems

Parsimony, distance matrix and likelihoodmethods (bootstrapping and consensus trees)

Data can be molecular sequences, genefrequencies, restriction sites and fragments,distance matrices and discrete characters

http://evolution.genetics.washington.edu/phylip.html
http://evolution.genetics.washington.edu/phylip.htmlhttp://evolution.genetics.washington.edu/phylip.html

Documents

Phylogeny 2009