phylogeny 2009
TRANSCRIPT
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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
AGGGCAT TAGCCCT AGCACTT
AAGACTT
TGGACTTAAGGCCT
AGGGCAT TAGCCCT AGCACTT
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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Maximum Parsimony Example
Possible Trees I:
(3)AGATATCCA
AGCCGTGCG AGAGATCCG
(2)AGCCGTGCG (4)AGAGATCCG
(1)AAGAGTGCA4
0
42
0
Number of Mutations: 10
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Maximum Parsimony Example
Possible Trees II:
(3)AGATATCCA
AGGAGTGCA AGAGGTCCG
(2)AGCCGTGCG
(4)AGAGATCCG
(1)AAGAGTGCA1
4
53
1
Number of Mutations: 14
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Maximum Parsimony Example
Possible Trees III:
(3)AGATATCCA
AGGAGTGCA AGATGTCCG
(2)AGCCGTGCG
(4)AGAGATCCG
(1)AAGAGTGCA1
5
53
2
Number of Mutations: 16
Tree I has the topology with the least number of
mutations and thus is the most parsimonious tree.
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Maximum Parsimony Example
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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Maximum Parsimony Example
1A AGAG T GCA
2AGC CG T GCG
3AGATAT C CA4AGAGAT C CG
Three informative columns
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Maximum Parsimony Example
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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Maximum Parsimony1 2 3 4 5 6 7 8 9 10
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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Maximum Parsimony1 2 3 4 5 6 7 8 9 10
1 - A G G G T A A C T G
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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Maximum Parsimony1 2 3 4 5 6 7 8 9 10
1 - A G G G T A A C T G
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