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Phylogeny
- A brief introduction in 4 hours -
Outline
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Introduction
Practical approach
Evolutionary models
Distance-based methods / TP5_1
Databases and software
Sequence-based methods / TP5_2
What is phylogeny?
Phylogeny is the evolutionary
history and relationship of
species.
Why is phylogeny of interest in
a proteomics course?
What data types can be used
to infer phylogenies?
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Morphological characters
Physiological characters
Gene order (e.g. in mitochondria)
Sequence data
– Nucleotide sequences
– Amino acid sequences
• Mixed characters
• ….
What is a phylogenetic tree?
• A phylogenetic tree is a model about the
evolutionary relationship between species
(OTUs) based on homologous characters
• But not all trees are phylogenetic trees
– Dendrogram = general term for a branching
diagram
– Cladogram: branching diagram without
branch length estimates
– Phylogenetic tree or Phylogram: branching
diagram with branch length estimates
What is a phylogenetic tree?
• Rooted or unrooted
• bifurcating or multifurcating (solved or
unsolved)
Gene duplication
• Prokaryots: at least 50%
• Eukaryots: >90%
After gene duplication
• Coexistence (normally only for a short while)
• Mostly, only one copy is retained
– becomes nonfunctional (non-functionalization),
– becomes a pseudogene (pseudogenization)
– is lost
• Both copies are retained
– Distinct expression pattern
– Distinct subcellular location (rare)
– One copy keeps the original function, the other
copy acquires a new function (neofunctionalization)
– Deleterious mutations in both entries
(subfunctionalization)
Relationships within homologs
Frog gene A
Human gene A
Orthologs
Mouse gene A
Gene
duplication
Paralogs
Mouse gene B
Ancestral
gene
Human gene B
Frog gene B
Drosophila gene AB
Homologs
Orthologs
Homologs …
Homologs = Genes of common origin
Orthologs = 1. Genes resulting from a speciation event, 2. Genes originating
from an ancestral gene in the last common ancestor of the compared
genomes
Co-orthologs = Orthologs that have undergone lineage-specific gene
duplications subsequent to a particular speciation event
Paralogs = Genes resulting from gene duplication
Inparalogs = Paralogs resulting from lineage-specific duplication(s)
subsequent to a particular speciation event
Outparalogs = Paralogs resulting from gene duplication(s) preceding a
particular speciation event
One-to-one (1:1) orthologs = Orthologs with no (known) lineage-specific gene
duplications subsequent to a particular speciation event
One-to-many (1:n) orthologs: Orthologs of which at least one - and at most all
but one - has undergone lineage-specific gene duplication subsequent to a
particular speciation event
Many-to-many (n:n) orthologs = Orthologs which have undergone lineagespecific gene duplications subsequent to a particular speciation event
Xenologs = Orthologs derived by horizontal gene transfer from another
lineage
Relationships between
orthologs and paralogs
Frog gene A
Human gene A
Orthologs
(Group 1)
Mouse gene A
Gene
duplication
Inparalogs
of Group 2
Mouse gene B
Ancestral
gene
Human gene B
Frog gene B
Drosophila gene AB
Outparalogs
of Group 1
Co-orthologs
of Drosophila
gene AB
Orthologs
(Group 2)
Practical approach I
Actin-related protein 2 (first 60 columns of the alignment)
ARP2_A
ARP2_B
ARP2_C
ARP2_D
ARP2_E
MESAP---IVLDNGTGFVKVGYAKDNFPRFQFPSIVGRPILRAEEKTGNVQIKDVMVGDE
MDSQGRKVIVVDNGTGFVKCGYAGTNFPAHIFPSMVGRPIVRSTQRVGNIEIKDLMVGEE
MDSQGRKVVVCDNGTGFVKCGYAGSNFPEHIFPALVGRPIIRSTTKVGNIEIKDLMVGDE
MDSQGRKVVVCDNGTGFVKCGYAGSNFPEHIFPALVGRPIIRSTTKVGNIEIKDLMVGDE
MDSKGRNVIVCDNGTGFVKCGYAGSNFPTHIFPSMVGRPMIRAVNKIGDIEVKDLMVGDE
*:*
:* ******** *** *** . **::****::*: . *::::**:***:*
Species are:
Caenorhabditis briggsae
Drosophila melanogaster
Homo sapiens
Mus musculus
Schizosaccharomyces pombe
Can you build a dendrogram (tree) for the sequences of the alignment?
Can you assign the species to the corresponding sequences of the alignment?
Phylogenetic analysis
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Select Data
Alignment
Select a data model
Select a substitution model
Tree-building
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[Distance matrix]
Tree-building
6. Tree evaluation
Select data
• To be considered:
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Input data must be homolog!
Number of character states
Content of phylogenetic information
Size of the dataset
Automated cluster data from large datasets
etc
Alignment
• MSA methods
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ClustalW
muscle
MAFFT
Probcons
T-coffee
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• See previous course …
Data model
= Characters selected for the analysis
• To be considered:
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Each character should be homolog!
Missing data (in some OTU)
Number of characters
etc
Evolutionary models
Phylogenetic tree-building presumes particular
evolutionary models
The model used influences the outcome of the
analysis and should be considered in the
interpretation of the analysis results
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Which aspects are to be considered?
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4.
Frequencies of aa exchange
Change of aa frequencies during evolution
Between-site rate variation or Among-site substitution rate
heterogenity
Presence of invariable sites
Evolutionary models
Notation, e.g.
JTT
JTT + F
JTT + F + gamma (4 )
JTT + F + gamma (8 ) + I (under discussion)
JTT + F + I
It is not always the most complex model that produces
the best result.
The more complex the model, the more complex the
explanation of the results.
Tree-building methods
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Distance (matrix) methods
1. Calculate distances for all pairs of taxa based
on the sequence alignment
2. Construct a phylogenetic tree based on a
distance matrix
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Character-based (Sequence) methods
1. Constructs a phylogenetic tree based on the
sequence alignment
Step 1: Compute distances
1. Estimate the number of amino acid
substitutions between sequence pairs
p distance: ^p=nd/n
p = proportion (p distance)
nd= number of aa differences
n = number of aa used
Step 1: Compute distances
• Nonlinear relationship of p with t (time)
• Estimation of aa substitutions
– Poisson correction
• PC distance
– Gamma correction
• Gamma distance
Step 2: Tree-building
Common distance methods
• Neighbor Joining (NJ)
• UPGMA / WPGMA
• Least Square (LS)
• Minimal Evolution (ME)
Neighbor Joining (NJ)
• Saitou, Nei (1987)
• Principle
– Clustering method
– Simplified minimal evolution principle
– Neighbors = taxa connected by a single node in
an unrooted tree
– Computational process: Star tree, followed by a
successive joining of neighbors and the creation of
new pairs of neighbors
– Result:
• A single final tree with branch length estimates
• unrooted tree
Neighbor Joining (NJ)
• Sum of branch lengths in the star tree
• Calculate the sum of all branch lengths for all
possible neighbors …
Neighbor Joining (NJ)
• Calculate Length X-Y
• Calculate again sum of all branch length
Neighbor Joining (NJ)
Neighbor Joining (NJ)
• Advantage
– Very efficient
– Also for large datasets
• Disadvantage
– Does not examine all possible topologies
Bootstrap
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Used to test the robustness of a tree topology
by Bradley Efron (1979)
Felsenstein (1985)
Principle: new MSA datasets are created by
choosing randomly N columns from the
original MSA; where N is the length of the
original MSA
• 100-1000 replicates
• Bootstrap support values: (75%), 95%, 98%
TP5 -
st
1
part, Exercises 1-5
http://education.expasy.org/m07_phylo.html
Ortholog databases &
phylogenetic databases
Some databases providing orthologous groups
and trees
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COG/KOG
HOGENOM
Ensembl
OMA browser
OrthoDB
OrthoMCL
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Pfam
PANDIT
SYSTERS
TreeBase
Tree of Life
Phylogenetic software
Software packages
• Freely available
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Phylip
BioNJ
PhyML
Tree Puzzle
MrBayes
• Commercial
– PAUP
– MEGA
Phylogenetic servers
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http://www.phylogeny.fr/
http://bioweb.pasteur.fr/seqanal/phylogeny/intro-uk.html
http://atgc.lirmm.fr/phyml/
http://phylobench.vital-it.ch/raxml-bb/
http://www.fbsc.ncifcrf.gov/app/htdocs/appdb/drawpage.php?ap
pname=PAUP
• http://power.nhri.org.tw/power/home.htm
Sequence methods
Most common:
• Maximum Parsimony (MP)
• Maximum Likelihood (ML)
• Baysian Inference
Maximum Parsimony (MP)
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Originally developed for morphological
characters
Henning, 1966
William of Ockham: the best hypothesis is
the one that requires the smallest number of
assumptions
Maximum Parsimony (MP)
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Principle:
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Estimate the minimum number of substitutions for a given
topology
Parsimony-informative sites (exclude invariable sites and
singletons)
Searching MP trees
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Exhaustive search
Branch-and-bound (Hendy-Penny, 1982)
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Heuristic search
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Good but time-consuming, if m>20
Result tree might not be the most parsimonious tree
Result
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Multiple result trees are possible (strict consensus tree,
majority-rule consensus tree)
Most parsimonious tree vs true tree
Unrooted result trees
Maximum Parsimony (MP)
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Advantages
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Free from assumptions (model-free)
Disadvantages
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Does not take into account homoplasy
Long-branch attraction (LBA): creates wrong
topologies, if the substitution rate varies
extensively between lineages
Maximum Likelihood (ML)
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Cavalli-Sforza, Edwards (1967), gene frequency data
Felsenstein (1981), nucleotide sequences
Kishino (1990), proteins
Principle
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Maximizes the likelihood of observing the sequence data for a
specific model of character state changes
Likelihood of a site = Sum of probabilities of every possible
reconstruction of ancestral states at the internal nodes
Likelyhood of the tree = Product of the likelihoods for all sites
(=sum of log likelihoods)
Result = tree with the highest likelihood
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Maximized to estimate branch lengths, not topologies
Search strategies: rarely exhaustive, mostly heuristic
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NNI (Nearest neighbor interchanges)
TBR (Tree bisection-reconnection)
SPR (Subtree pruning and regrafting)
Number of possible trees
• Unrooted bifurcating trees:
• Rooted bifurcating trees:
Number of possible trees
Leaves
Rooted
Unrooted
Number of possible trees
Leaves
Unrooted
Rooted
3
1
3
4
3
15
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15
105
6
105
945
7
945
10395
8
10395
135135
9
135135
2027025
10
2027025
34459425
Maximum Likelihood (ML)
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Methods:
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ProML (Phylip)
PhyML
RaxML
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Tree evaluation
1. Topology
1. Comparison with species tree
2. Robustness, e.g. bootstrap
2. Branch lengths
TP5 –
nd
2
part, Exercise 6
http://education.expasy.org/m07_phylo.html