Christian M Zmasek, PhD [email protected] 15 June 2010.
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Transcript of Christian M Zmasek, PhD [email protected] 15 June 2010.
Phylogenetic Inference
Christian M Zmasek, [email protected] June 2010
(C) 2010 Christian M. Zmasek 2
Overview
1. Why perform phylogenetic inference?
2. Theoretical background3. Methods4. Software & Examples
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1. Why perform phylogenetic inference?
‘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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Over-annotation due to database bias or gene loss
RAT
MOUSE
HUMAN
RICE
LIZARD
SHARK
RAT
MOUSE
HUMANRICE
LIZARD
SHARK
Y
Z
X
Z
Y
: query sequence
: orthologous to query
: most similar to query
: gene duplication
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Over-annotation due to unequal rates of evolution [phylogenetic tree ≠ clustering !!]
RAT
WHEAT
HUMAN
BARLEY
Y
Z
: query sequence
: orthologous to query
: most similar to query
: gene duplication
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2. Theoretical Background
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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Gene Trees/Species Trees
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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A gene tree which is also a species tree
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A gene tree of orthologs and paralogs based on Bcl-2 family protein sequences
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Homologs
Homologs are defined as sequences which share a common ancestor (Fitch, 1966)
This definition becomes unclear if mosaic proteins, which are composed of structural units originating from different genes are considered
Phylogenetic trees make sense only if constructed based on homologous sequences (whole genes/proteins, or domains)
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Orthologs, Paralogs, Xenologs
Homologous sequences can be divided into orthologs, paralogs and xenologs:
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
Paralogs: diverged by a duplication event (their last common ancestor corresponds to a duplication)
Xenologs: are related to each other by horizontal gene transfer (via retroviruses, for example)
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Orthologs, Paralogs example
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Caveat emptor: Orthology vs. Function
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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Gene Duplication
New genes evolve if mutations accumulate while selective constraints are relaxed by gene duplication
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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Gene Trees Vs. Species Trees – How Gene Duplications Can Be Detected
Hum
anR
at
Wheat
Hum
anR
at
Wheat
Hum
an
Rat
Wheat
Hum
anR
at
Wheat
G1 G2 S
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3. Methods
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
“More accurate”(in general)
Fast
Bayesian Methods (MCMC)
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Pairwise Distance Calculation
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):
p nd
nd ns
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Pairwise Distance Calculation
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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Pairwise Distance Calculation
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 vs …
UPGMA stands for unweighted pair group method using arithmetic averages
This is clustering
This algorithm produces rooted trees based under the assumption of a molecular clock.
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… Neighbor Joining
As opposed to UPGMA, neighbor joining (NJ) is not misled by the absence of a molecular clock
NJ produces phylogenetic trees (not cluster diagrams)
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Optimality Criteria Based on Character Data
Fitch-Margoliash Minimal evolution (ME) Maximum Parsimony (MP) Maximum Likelihood (ML)
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Minimal Evolution
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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Maximum Parsimony
Evaluate a given topology
Example:Sequence1: TGCSequence2: TACSequence3: AGGSequence4: AAG
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Maximum Likelihood
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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Bayesian Methods
Example: MrBayes Use Markov Chain Monte Carlo
(MCMC) approach to sample over tree space
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Bootstrap resampling
To asses the reliability of trees
Resampling with replacement (see example on next slide)
What is “good enough”?? >60%?, >90%?
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Bootstrap resampling: example
Original sequence alignment:Sequence 1: ARNDCQSequence 2: VRNDCQ 123456Bootstrap resample 1:Sequence 1: RRQCCASequence 2: RRQCCV 226551Bootstrap resample 2:Sequence 1: AQCDCQSequence 2: VQCDCQ 165456
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Summary
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
“More accurate”(in general)
Fast
Bayesian Methods (MCMC)
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4: Software for multiple sequence alignments
Mafft: http://mafft.cbrc.jp/alignment/software/ Server: http://mafft.cbrc.jp/alignment/server/
T-Coffee: 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/
ClustalW: ftp://ftp-igbmc.u-strasbg.fr/pub/ClustalW/ Server: http://www.ebi.ac.uk/clustalw/
Probcons: http://probcons.stanford.edu/ Server: http://probcons.stanford.edu
Muscle: http://www.drive5.com/muscle/ Server: http://phylogenomics.berkeley.edu/cgi-bin/muscle/input_muscle.py
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Software for phylogeny reconstruction 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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Books
Richard Durbin et al.: Biological Sequence Analysis: Probabilistic Models of Proteins and Nucleic Acids [http://www.amazon.com/Biological-Sequence-Analysis-Probabilistic-Proteins/dp/0521629713/sr=1-1/qid=1170198997/ref=sr_1_1/102-4955297-1236120?ie=UTF8&s=books]
Joe Felsenstein: Inferring Phylogenies [http://www.amazon.com/Inferring-Phylogenies-Joseph-Felsenstein/dp/0878931775/sr=8-1/qid=1170198215/ref=pd_bbs_sr_1/102-4955297-1236120?ie=UTF8&s=books]
Ziheng Yang: Computational Molecular Evolution [http://www.amazon.com/Computational-Molecular-Evolution-Oxford-Ecology/dp/0198567022/sr=1-1/qid=1170198731/ref=pd_bbs_sr_1/102-4955297-1236120?ie=UTF8&s=books]
Oliver Gascuel: Mathematics of Evolution & Phylogeny [http://www.amazon.com/Mathematics-Evolution-Phylogeny-Olivier-Gascuel/dp/0198566107/sr=1-1/qid=1170198842/ref=sr_1_1/102-4955297-1236120?ie=UTF8&s=books]
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5. “Homework”
Download and install MrBayes: http://mrbayes.csit.fsu.edu/
Read the tutorial: http://mrbayes.csit.fsu.edu/wiki/index.php/Tutorial
Analyze the provided data set (“primates.nex”)
Download and install PHYLIP: http://evolution.genetics.washington.edu/phylip.html
Perform seqboot (100x) – dnadist – neighbor (NJ) – consense on “primates.nex” (you need to change the format accordingly)
Compare the results (MrBayes vs. Phylip NJ)