Christian M Zmasek, PhD
czmasek@burnham.org
15 June 2010
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Why perform phylogenetic inference?
Theoretical background
Methods
Software & Examples
(C) 2010 Christian M. Zmasek
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‘Tree of life’: The relationships amongst
different species
Infer the functions of proteins from family
members in model organisms or to refine
existing annotations through phylogenetic
analysis
A method to organize/cluster sequences
with biological justification
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RAT
RAT
MOUSE
MOUSE
HUMAN
RICE
Y
RICE
HUMAN
LIZARD
LIZARD
SHARK
Z
SHARK
Y
X
Z
: query sequence
: orthologous to query
: most similar to query
: gene duplication
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HUMAN
WHEAT
RAT
BARLEY
Y
Z
: query sequence
: orthologous to query
: most similar to query
: gene duplication
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A phylogeny is the evolutionary history of a species or a
group of species. Lately, the term is also being applied to the
evolutionary history of individual DNA or protein
sequences.
 The evolutionary history of organisms or sequences can be
illustrated using a tree-like diagram – a phylogenetic tree.
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Initially, phylogenetic trees were built based on the
morphology of organisms.
 Around 1960 molecular sequences were recognized
as containing phylogenetic information and hence
as valuable for tree building
 A tree built based on sequence data is called a gene
tree since it is a representation of the evolutionary
history of genes
 A tree illustrating the evolutionary history of
organisms is called a species tree
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Homologs are defined as sequences which share a common
ancestor (Fitch, 1966)
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This definition becomes unclear if mosaic proteins, which
are composed of structural units originating from different
genes are considered
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Phylogenetic trees make sense only if constructed based on
homologous sequences (whole genes/proteins, or domains)
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Homologous sequences can be divided into orthologs,
paralogs and xenologs:
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Orthologs: diverged by a speciation event (their last
common ancestor on a phylogenetic tree corresponds to a
speciation event)
 IMPORANT: Functional similarity does not imply orthology
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Paralogs: diverged by a duplication event (their last
common ancestor corresponds to a duplication)
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Xenologs: are related to each other by horizontal gene
transfer (via retroviruses, for example)
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 Orthologous sequences tend to have more similar
“functions” than paralogs
 Yet: Orthologs are mathematically defined,
whereas there is no definition of sequence
“function” (i.e. it is a subjective term)
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New genes evolve if mutations accumulate while
selective constraints are relaxed by gene duplication
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First recognized by Haldane (“… it [mutation
pressure] will favour polyploids, and particularly
allopolyploids, which possess several pairs of sets of
genes, so that one gene may be altered without
disadvantage…”
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Wheat
Rat
Human
Rat
Wheat
Human
Rat
Human
Wheat
Wheat
Rat
Human
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G2
G1
Multiple sequence alignment of homologous sequences
Pairwise distance calculation
Algorithmic
Methods Based on
Pairwise
Distances:
•UPGMA
•Neighbor Joining
Optimality Criteria
Based on Pairwise
Distances:
•Fitch-Margoliash
•Minimal Evolution
Optimality Criteria Based
on Character Data:
•Maximum Parsimony
•Maximum Likelihood
Bayesian Methods (MCMC)
“More accurate”
(in general)
Fast
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The simplest method to measure the distance
between two amino acid sequences is by their
fractional dissimilarity p (nd is the number of
aligned sequence positions containing non-identical
amino acids and ns is the number of aligned
sequence positions containing identical amino
acids):
nd
p
n d  ns
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Unfortunately, this is unrealistic -- does not
take into account:
 superimposed changes: multiple mutations at
the same sequence location
 different chemical properties of amino acids: for
example, changing leucine into isoleucine is more
likely and should be weighted less than changing
leucine into proline
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A more realistic approach for estimating
evolutionary distances is to apply maximum
likelihood to empirical amino acid replacement
models, such as PAM transition probability
matrices.
 The likelihood LH of a hypothesis H (an evolutionary
distance, for example) given some data D (an
alignment, for example) is the probability of D given
H: LH=P(D|H)
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UPGMA stands for unweighted pair group
method using arithmetic averages
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This is clustering
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This algorithm produces rooted trees based
under the assumption of a molecular clock.
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As opposed to UPGMA, neighbor joining
(NJ) is not misled by the absence of a
molecular clock
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NJ produces phylogenetic trees (not cluster
diagrams)
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Fitch-Margoliash
Minimal evolution (ME)
Maximum Parsimony (MP)
Maximum Likelihood (ML)
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Branch lengths are fitted to a tree according
to a unweighted least squares criterion, but
the optimality criterion to evaluate and
compare trees is to minimize the sum of all
branch lengths.
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Evaluate a given
topology
Example:
Sequence1: TGC
Sequence2: TAC
Sequence3: AGG
Sequence4: AAG
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Probabilistic methods can be used to assign a likelihood to a
given tree and therefore allow the selection of the tree which
is most likely given the observed sequences.
Probability for one residue a to change to b in time t along a
branch of a tree: P(b|a,t)
Its actual calculation is dependent on what model for
sequence evolution is used.
Poisson process:
 P(b|a,t)=1/20 + 19/20e-ut for a=b
 P(b|a,t)=1/20 + 1/20e-ut for a≠b
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Example: MrBayes
Use Markov Chain Monte Carlo (MCMC)
approach to sample over tree space
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To asses the reliability of trees
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Resampling with replacement (see example
on next slide)
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What is “good enough”?? >60%?, >90%?
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Original sequence alignment:
Sequence 1: ARNDCQ
Sequence 2: VRNDCQ
123456
Bootstrap resample 1:
Sequence 1: RRQCCA
Sequence 2: RRQCCV
226551
Bootstrap resample 2:
Sequence 1: AQCDCQ
Sequence 2: VQCDCQ
165456
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Multiple sequence alignment of homologous sequences
Pairwise distance calculation
Algorithmic
Methods Based on
Pairwise
Distances:
•UPGMA
•Neighbor Joining
Optimality Criteria
Based on Pairwise
Distances:
•Fitch-Margoliash
•Minimal Evolution
Optimality Criteria Based
on Character Data:
•Maximum Parsimony
•Maximum Likelihood
Bayesian Methods (MCMC)
“More accurate”
(in general)
Fast
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Mafft:
 http://mafft.cbrc.jp/alignment/software/
 Server: http://mafft.cbrc.jp/alignment/server/
T-Coffee:
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ClustalW:
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ftp://ftp-igbmc.u-strasbg.fr/pub/ClustalW/
Server: http://www.ebi.ac.uk/clustalw/
Probcons:
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http://www.tcoffee.org/Projects_home_page/t_coffee_home_page.html
Server: http://www.ch.embnet.org/software/TCoffee.html
Server: http://www.ebi.ac.uk/t-coffee/
http://probcons.stanford.edu/
Server: http://probcons.stanford.edu
Muscle:
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http://www.drive5.com/muscle/
Server: http://phylogenomics.berkeley.edu/cgi-bin/muscle/input_muscle.py
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List of programs: http://evolution.genetics.washington.edu/phylip/software.html
ML pairwise distance calculation (protein):
 TREE-PUZZLE: http://www.tree-puzzle.de/
Bootstrapping, pairwise distance calculation, UPGMA, NJ, Fitch-Margolish, ME:
 PHYLIP: http://evolution.genetics.washington.edu/phylip.html
ME:
 FastME (server): http://atgc.lirmm.fr/fastme/
 MEGA: http://www.megasoftware.net/
ML:
 PhyML (server): http://www.atgc-montpellier.fr/phyml/
 RAxML (server): http://phylobench.vital-it.ch/raxml-bb/
Bayesian (MCMC):
 MrBayes: http://mrbayes.csit.fsu.edu/
Parsimony (esp. on Macintosh), display:
 PAUP: http://paup.csit.fsu.edu/
Tree display:
 Archaeopteryx: http://www.phylosoft.org/archaeopteryx/
Hypothesis testing:
 HyPhy: http://www.hyphy.org/
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Richard Durbin et al.: Biological Sequence Analysis: Probabilistic Models of Proteins and
Nucleic Acids [http://www.amazon.com/Biological-Sequence-Analysis-ProbabilisticProteins/dp/0521629713/sr=1-1/qid=1170198997/ref=sr_1_1/102-49552971236120?ie=UTF8&s=books]
Joe Felsenstein: Inferring Phylogenies [http://www.amazon.com/Inferring-PhylogeniesJoseph-Felsenstein/dp/0878931775/sr=8-1/qid=1170198215/ref=pd_bbs_sr_1/102-49552971236120?ie=UTF8&s=books]
Ziheng Yang: Computational Molecular Evolution
[http://www.amazon.com/Computational-Molecular-Evolution-OxfordEcology/dp/0198567022/sr=1-1/qid=1170198731/ref=pd_bbs_sr_1/102-49552971236120?ie=UTF8&s=books]
Oliver Gascuel: Mathematics of Evolution & Phylogeny
[http://www.amazon.com/Mathematics-Evolution-Phylogeny-OlivierGascuel/dp/0198566107/sr=1-1/qid=1170198842/ref=sr_1_1/102-49552971236120?ie=UTF8&s=books]
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Download and install MrBayes: http://mrbayes.csit.fsu.edu/
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Read the tutorial: http://mrbayes.csit.fsu.edu/wiki/index.php/Tutorial
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Analyze the provided data set (“primates.nex”)
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Download and install PHYLIP:
http://evolution.genetics.washington.edu/phylip.html
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Perform seqboot (100x) – dnadist – neighbor (NJ) – consense on
“primates.nex” (you need to change the format accordingly)
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Compare the results (MrBayes vs. Phylip NJ)
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