Transcript Document

Phylogeny
- A brief introduction in 4 hours -
Outline
•
•
•
•
•
•
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?
•
•
•
•
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
1.
2.
3.
4.
5.
Select Data
Alignment
Select a data model
Select a substitution model
Tree-building
•
•
[Distance matrix]
Tree-building
6. Tree evaluation
Select data
• To be considered:
–
–
–
–
–
–
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
–
–
–
–
–
–
ClustalW
muscle
MAFFT
Probcons
T-coffee
…
• See previous course …
Data model
= Characters selected for the analysis
• To be considered:
–
–
–
–
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
•
Which aspects are to be considered?
1.
2.
3.
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
•
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
•
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
•
•
•
•
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
•
•
•
•
•
•
COG/KOG
HOGENOM
Ensembl
OMA browser
OrthoDB
OrthoMCL
•
•
•
•
•
Pfam
PANDIT
SYSTERS
TreeBase
Tree of Life
Phylogenetic software
Software packages
• Freely available
–
–
–
–
–
Phylip
BioNJ
PhyML
Tree Puzzle
MrBayes
• Commercial
– PAUP
– MEGA
Phylogenetic servers
•
•
•
•
•
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)
•
•
•
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)
•
Principle:
–
–
–
Estimate the minimum number of substitutions for a given
topology
Parsimony-informative sites (exclude invariable sites and
singletons)
Searching MP trees
•
•
Exhaustive search
Branch-and-bound (Hendy-Penny, 1982)
–
•
Heuristic search
–
–
Good but time-consuming, if m>20
Result tree might not be the most parsimonious tree
Result
•
•
•
Multiple result trees are possible (strict consensus tree,
majority-rule consensus tree)
Most parsimonious tree vs true tree
Unrooted result trees
Maximum Parsimony (MP)
•
Advantages
–
•
Free from assumptions (model-free)
Disadvantages
–
–
Does not take into account homoplasy
Long-branch attraction (LBA): creates wrong
topologies, if the substitution rate varies
extensively between lineages
Maximum Likelihood (ML)
•
•
•
•
Cavalli-Sforza, Edwards (1967), gene frequency data
Felsenstein (1981), nucleotide sequences
Kishino (1990), proteins
Principle
–
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
–
–
–
•
•
Maximized to estimate branch lengths, not topologies
Search strategies: rarely exhaustive, mostly heuristic
•
•
•
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
5
15
105
6
105
945
7
945
10395
8
10395
135135
9
135135
2027025
10
2027025
34459425
Maximum Likelihood (ML)
•
Methods:
–
–
–
–
ProML (Phylip)
PhyML
RaxML
…
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