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A
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Models of sequence evolution
GTR
HKY
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Jukes-Cantor
Felsenstein K2P
Tree building methods:some examples
Assessing phylogenetic data
Popular phylogenetic packages
More models of sequence evolution …
Currently, there are more than 60 models described
- plus gamma distribution and invariable sites
- accuracy of models rapidly decreases for highly divergent sequences
- problem: more complicated models tend to be less accurate (and slower)
How to pick an appropriate model?
- use a maximum likelihood ratio test
- implemented in Modeltest 3.06 (Posada & Crandall, 1998)
Tree building methods:some examples
Assessing phylogenetic data
Popular phylogenetic packages
More models of sequence evolution …
Example for Modeltest file
JC = 3369.2803
F81 = 3342.5513
K80 = 3294.6611
HKY = 3124.4182
TrNef = 3114.5491
TrN = 2993.6340
K81 = 2987.6548
K81uf = 2973.5620
TIMef = 2937.6196
TIM = 2932.9878
TVMef = 2930.3450
TVM = 2922.1970
SYM = 2921.3069
GTR = 2921.1187
A Equal base frequencies
Null model = JC -lnL0 = 3369.2803
Alternative model = F81 -lnL1 = 3342.5513
2(lnL1-lnL0) = 53.4580 df = 3
P-value = <0.000001
B
Model selected: TVM+G
-lnL = 2911.3660
C
Tree building methods:some examples
Assessing phylogenetic data
Popular phylogenetic packages
More models of sequence evolution …
Amino acid sequences
- infinitely more complicated than nucleotide sequences
- some amino acids can replace one another with relatively little effect on the structure and function of the final protein while other replacements can be functionally devastating
- from the standpoint of the genetic code, some amino acid changes can be made by a single DNA mutation while others require two or even three changes in the DNA sequence
- in practice, what has been done is to calculate tables of frequencies of all amino acid replacements within families of related protein sequences in the databanks: i.e. PAM and BLOSSUM
Tree building methods:some examples
Assessing phylogenetic data
Popular phylogenetic packages
Phylogenetic Inference II
Before describing any theoretical or practical aspects of phylogenetics, it is necessary to give some disclaimers. This area of computational biology is an intellectual minefield!
Neither the theory nor the practical applications of any algorithms are universally accepted throughout the scientific community.
The application of different software packages to a data set is very likely to give different answers; minor changes to a data set are also likely to profoundly change the result.
Disclaimers
Tree building methods:some examples
Assessing phylogenetic data
Popular phylogenetic packages
CS 177 Phylogenetics II
Tree building methods: some examples
Assessing phylogenetic data
Popular phylogenetic software packages
Tree building methods:some examples
Assessing phylogenetic data
Popular phylogenetic packages
helix
sheet
Are there Correct trees??
Phylogenetic Inference II
Despite all of all problems, it is actually quite simple to use computer programs calculate phylogenetic trees for data sets
Provided the data are clean, outgroups are correctly specified, appropriate algorithms are chosen, no assumptions are violated, etc., can the true, correct tree be found and proven to be scientifically valid?
Unfortunately, it is impossible to ever conclusively state what is the "true" tree for a group of sequences (or a group of organisms); taxonomy is constantly under revision as new data is gathered
Tree building methods:some examples
Assessing phylogenetic data
Popular phylogenetic packages
Phenetic methods construct trees (phenograms) by considering the current states of characters without regard to the evolutionary history that brought the species to their current phenotypes;phenograms are based on overall similarity
Cladistic methods construct trees (cladograms) rely on assumptions about ancestral relationships as well as on current data;cladograms are based on character evolution (e.g. shared derived characters)
Phenetics versus cladistics
Tree building methods:some examples
Assessing phylogenetic data
Popular phylogenetic packages
Tree building methods
Data type: genetic distance / character-state
Computational method: optimality criterion/clustering algorithmen
C lustering algorithmO ptim ality criterion
DA
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PE
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arac
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Dis
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PARS I M ON Y
UPGM A
N EI GHBO R-M I N I MUM EVO LUTI O N
LEAS T S QUARES
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J O I N I N G
COMPUTATI ONAL METHOD
FI TCH & MARG OLI AS H
Tree building methods:some examples
Assessing phylogenetic data
Popular phylogenetic packages
Tree building (distance based)
UPGMA
- The simplest of the distance methods is the UPGMA (Unweighted Pair Group Method using Arithmetic averages)
- Many multiple alignment programs such as PILEUP use a variant of UPGMA to create a dendrogram of DNA sequences which is then used to guide the multiple alignment algorithm
Tree building methods:some examples
Assessing phylogenetic data
Popular phylogenetic packages
UPGMA
A B C D E F G
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C 94 79 -
D 111 96 47 -
E 67 16 83 100 -
F 23 58 89 106 62 -
G 107 92 43 20 96 102 -
Tree building methods:some examples
Assessing phylogenetic data
Popular phylogenetic packages
UPGMA
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C 94 79 -
D 111 96 47 -
E 67 16 83 100 -
F 23 58 89 106 62 -
G 107 92 43 20 96 102 -
GD
Tree building methods:some examples
Assessing phylogenetic data
Popular phylogenetic packages
A B C E F DG
A -
B 63 -
C 94 79 -
E 67 16 83 -
F 23 58 89 62 -
DG 94 84 35 88 94 -
UPGMA
GD C
Tree building methods:some examples
Assessing phylogenetic data
Popular phylogenetic packages
A B E F CDG
A -
B 63 -
E 67 16 -
F 23 58 62 -
CDG 61 64 61 74 -
UPGMA
GD C A F
Tree building methods:some examples
Assessing phylogenetic data
Popular phylogenetic packages
UPGMA
AF B E CDG
AF -
B 98 -
E 106 16 -
CDG 112 64 61 -
B EGD C A F
Tree building methods:some examples
Assessing phylogenetic data
Popular phylogenetic packages
UPGMA
AF BE CDG
AF -
BE 188 -
CDG 112 108 -
B E GD C A F
Root
B EGD C A F
Tree building methods:some examples
Assessing phylogenetic data
Popular phylogenetic packages
Maximum Parsimony (MP)
outgroup a b c
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outgroup a b c
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ACG G GACG G GACG G GACG G G
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Tree building methods:some examples
Assessing phylogenetic data
Popular phylogenetic packages
- Parsimony involves evaluating all possible trees for each vertical column of sequence character (nucleotide position)
- only informative sites are considered
- each tree is given a score based on the number of evolutionary changes that are needed to explain the observed data
- finally, those trees that produce the smallest number of changes (shortest trees) overall for all sequence positions are identified
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Maximum Likelihood (ML)
outgroup a b c
AAA A AAA A
CCCC C
G G GG G GG G GG G G
T TTT TT
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outgroup a b c
A AAA
ACG G GACG G GACG G GACG G G
T TTT TT
TC
Tree building methods:some examples
Assessing phylogenetic data
Popular phylogenetic packages
- Maximum Likelihood uses probability calculations based on a specific model of sequence evolution to find a tree that best accounts for the variation in a set of sequences
- all possible trees for each nucleotide position are considered
- the less mutations needed to fit a tree to the data, the more likely the tree
- ML resembles MP in that the tree with the least number of changes will be most likely
- however, ML evaluates trees using explicit evolutionary models
- thus, the method can be used to explore relationships among more diverse taxa
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Computational methods for finding optimal trees
Possible evolutionary trees
2,027,02510
135,1359
10,3958
9547
1056
155
34
13
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unrooted(2n-5)!/(2n-3(n-3)!)
Taxa (n)
30 3.58 x 1036
. .
.
. .
.
Tree building methods:some examples
Assessing phylogenetic data
Popular phylogenetic packages
Computational methods for finding optimal trees
Exact algorithms
- “Guarantee” to find the optimal or “best” tree for the method of choice
- Two types used in tree building:
Exhaustive search: Evaluates all possible unrooted trees, choosing the one with the best score for the method
Branch-and-bound search: Eliminates part of the tree that only contain suboptimal solutions
Heuristic algorithms
- Approximate or “quick-and-dirty” methods that attempt to find the optimal tree for the method of choice, but cannot guarantee to do so
- Often operate by “hill-climbing” methods
Tree building methods:some examples
Assessing phylogenetic data
Popular phylogenetic packages
Heuristic algorithms
Searchfor global minimum GLOBAL
MAXIMUM
GLOBALMINIMUM
localminimum
localmaximum
Searchfor globalmaximum
Heuristic search algorithms are input order dependent and can get stuck in local
minima or maxima
GLOBALMAXIMUM
GLOBALMINIMUM
Rerunning heuristic searches using different input orders of taxa can
help find global minima or maxima
From NHGRI lecture, C.-B. Stewart
Tree building methods:some examples
Assessing phylogenetic data
Popular phylogenetic packages
Assessing Phylogenetic Data
Most data includes potentially misleading evidence of relationships
One should not only construct phylogenetic hypotheses but should also assess what ‘confidence’ can be placed in these hypotheses
How much support is there for a particular clade?
Question:
Tree building methods:some examples
Assessing phylogenetic data
Popular phylogenetic packages
Assessing Phylogenetic Data
How much support is there for a particular clade?
Ochromonas
Symbiodinium
ProrocentrumLoxodesSpirostomumum
Tetrahymena
EuplotesTracheloraphis
Gruberia
71
26
1659
1621
Ochromonas
Symbiodinium
ProrocentrumLoxodesSpirostomumum
Tetrahymena
EuplotesTracheloraphis
Gruberia
71
59
Bootstrapping/Jack-knifing:
Lots of randomized data sets are produced by sampling the real data with replacement
(or in jackknifing, by removing some random proportion of the data);
Frequencies of occurrence of groups are a measure of support for those groups
- Bootstrap proportions aren’t easily interpretable
- no indication for how good the data are but simply for how well the tree fits the data
Problems:
Tree building methods:some examples
Assessing phylogenetic data
Popular phylogenetic packages
Review available at: http://evolution.genetics.washington.edu/phylip/software.html
Popular phylogenetic software packages
Tree building methods:some examples
Assessing phylogenetic data
Popular phylogenetic packages