Upload
kenneth-roach
View
37
Download
0
Embed Size (px)
DESCRIPTION
Phylogenetic Trees Lecture 11. Sections 7.1, 7.2, in Durbin et al. Evolution. Evolution of new organisms is driven by Diversity Different individuals carry different variants of the same basic blue print Mutations - PowerPoint PPT Presentation
Citation preview
.
Phylogenetic TreesLecture 11
Sections 7.1, 7.2, in Durbin et al.
2
Evolution
Evolution of new organisms is driven by
Diversity Different individuals
carry different variants of the same basic blue print
Mutations The DNA sequence
can be changed due to single base changes, deletion/insertion of DNA segments, etc.
Selection bias
3
The Tree of Life
Sou
rce:
Alb
erts
et
al
4
D’après Ernst Haeckel, 1891
Tree of life- a better picture
5
Primate evolution
A phylogeny is a tree that describes the sequence of speciation events that lead to the forming of a set of current day species; also called a phylogenetic tree.
6
Historical Note Until mid 1950’s phylogenies were constructed by
experts based on their opinion (subjective criteria)
Since then, focus on objective criteria for constructing phylogenetic trees
Thousands of articles in the last decades
Important for many aspects of biology Classification Understanding biological mechanisms
7
Morphological vs. Molecular
Classical phylogenetic analysis: morphological features: number of legs, lengths of legs, etc.
Modern biological methods allow to use molecular features
Gene sequences Protein sequences
Analysis based on homologous sequences (e.g., globins) in different species
8
Morphological topology
BonoboChimpanzeeManGorillaSumatran orangutanBornean orangutanCommon gibbonBarbary apeBaboonWhite-fronted capuchinSlow lorisTree shrewJapanese pipistrelleLong-tailed batJamaican fruit-eating batHorseshoe bat
Little red flying foxRyukyu flying foxMouseRatVoleCane-ratGuinea pigSquirrelDormouseRabbitPikaPigHippopotamusSheepCowAlpacaBlue whaleFin whaleSperm whaleDonkeyHorseIndian rhinoWhite rhinoElephantAardvarkGrey sealHarbor sealDogCatAsiatic shrewLong-clawed shrewSmall Madagascar hedgehogHedgehogGymnureMoleArmadilloBandicootWallarooOpossumPlatypus
Archonta
Glires
Ungulata
Carnivora
Insectivora
Xenarthra
(Based on Mc Kenna and Bell, 1997)
9
Rat QEPGGLVVPPTDA
Rabbit QEPGGMVVPPTDA
Gorilla QEPGGLVVPPTDA
Cat REPGGLVVPPTEG
From sequences to a phylogenetic tree
There are many possible types of sequences to use (e.g. Mitochondrial vs Nuclear proteins).
10
DonkeyHorseIndian rhinoWhite rhinoGrey sealHarbor sealDogCatBlue whaleFin whaleSperm whaleHippopotamusSheepCowAlpacaPigLittle red flying foxRyukyu flying foxHorseshoe batJapanese pipistrelleLong-tailed batJamaican fruit-eating bat
Asiatic shrewLong-clawed shrew
MoleSmall Madagascar hedgehogAardvarkElephantArmadilloRabbitPikaTree shrewBonoboChimpanzeeManGorillaSumatran orangutanBornean orangutanCommon gibbonBarbary apeBaboon
White-fronted capuchinSlow lorisSquirrelDormouseCane-ratGuinea pigMouseRatVoleHedgehogGymnureBandicootWallarooOpossumPlatypus
Perissodactyla
Carnivora
Cetartiodactyla
Rodentia 1
HedgehogsRodentia 2
Primates
ChiropteraMoles+ShrewsAfrotheria
XenarthraLagomorpha
+ Scandentia
Mitochondrial topology(Based on Pupko et al.,)
11
Nuclear topology
Round Eared Bat
Flying Fox
Hedgehog
Mole
Pangolin
Whale
Hippo
Cow
Pig
Cat
Dog
Horse
Rhino
Rat
Capybara
Rabbit
Flying Lemur
Tree Shrew
Human
Galago
Sloth
Hyrax
Dugong
Elephant
Aardvark
Elephant Shrew
Opossum
Kangaroo
1
2
3
4
Cetartiodactyla
Afrotheria
Chiroptera
Eulipotyphla
Glires
Xenarthra
CarnivoraPerissodactyla
Scandentia+Dermoptera
Pholidota
Primate
(tree by Madsenl)
(Based on Pupko et al. slide)
12
Theory of Evolution
Basic idea speciation events lead to creation of different
species. Speciation caused by physical separation into
groups where different genetic variants become dominant
Any two species share a (possibly distant) common ancestor
13
Phylogenenetic trees
Leafs - current day species Nodes - hypothetical common ancestors Edges length - “time” from one speciation to the next
Aardvark Bison Chimp Dog Elephant
14
Dangers in Molecular Phylogenies
We have to emphasize that gene/protein sequence can be homologous for several different reasons:
Orthologs -- sequences diverged after a speciation event
Paralogs -- sequences diverged after a duplication event
Xenologs -- sequences diverged after a horizontal transfer (e.g., by virus)
15
Dangers of Paralogs
Speciation events
Gene Duplication
1A 2A 3A 3B 2B 1B
16
Dangers of Paralogs
Speciation events
Gene Duplication
1A 2A 3A 3B 2B 1B
If we happen to consider genes 1A, 2B, and 3A of species 1,2,3, we get a wrong tree that does not represent the phylogeny of the host species of the given sequences because duplication does not create new species.
In the sequel we assume all given sequences are orthologs.
S
SS
17
Types of Trees
A natural model to consider is that of rooted trees
CommonAncestor
18
Types of treesUnrooted tree represents the same phylogeny without
the root node
Depending on the model, data from current day species does not distinguish between different placements of the root.
19
Rooted versus unrooted treesTree a
ab
Tree b
c
Tree c
Represents the three rooted trees
20
Positioning Roots in Unrooted Trees
We can estimate the position of the root by introducing an outgroup:
a set of species that are definitely distant from all the species of interest
Aardvark Bison Chimp Dog Elephant
Falcon
Proposed root
21
Type of Data
Distance-based Input is a matrix of distances between species Can be fraction of residue they disagree on, or
alignment score between them, or …
Character-based Examine each character (e.g., residue)
separately
22
Two Methods of Tree Construction
Distance- A weighted tree that realizes the distances between the objects.
Parsimony – A tree with a total minimum number of character changes between nodes.
We start with distance based methods, considering the following question:Given a set of species (leaves in a supposed tree), and distances between them – construct a phylogeny which best “fits” the distances.
23
Exact solution: Additive sets
Given a set M of L objects with an L×L distance matrix:d(i,i)=0, and for i≠j, d(i,j)>0d(i,j)=d(j,i). For all i,j,k it holds that d(i,k) ≤ d(i,j)+d(j,k).
Can we construct a weighted tree which realizes these distances?
24
Additive sets (cont)
We say that the set M with L objects is additive if there is a tree T, L of its nodes correspond to the L objects, with positive weights on the edges, such that for all i,j, d(i,j) = dT(i,j), the length of the path from i to j in T.
Note: Sometimes the tree is required to be binary, and then the edge weights are required to be non-negative.
25
Three objects sets are additive:
For L=3: There is always a (unique) tree with one internal node.
( , )( , )( , )
d i j a bd i k a cd j k b c
ab
c
i
j
k
m
Thus0
2
1 )],(),(),([),( jidkjdkidmkdc
i j k
i 0 a+b a+c
j 0 b+c
k 0
26
How about four objects?
L=4: Not all sets with 4 objects are additive:
eg, there is no tree which realizes the below distances.
i j k l
i 0 2 2 2
j 0 2 2
k 0 3
l 0
27
The Four Points ConditionTheorem: A set M of L objects is additive iff any subset of four objects can be labeled i,j,k,l so that:
d(i,k) + d(j,l) = d(i,l) +d(k,j) ≥ d(i,j) + d(k,l) We call {{i,j},{k,l}} the “split” of {i,j,k,l}.
ik
lj
Proof:Additivity 4P Condition: By the figure...
28
4P Condition Additivity:Induction on the number of objects, L.For L ≤ 3 the condition is empty and tree exists. Consider 4 points which satisfy d(i,k) +d(j,l) = d(i,l) +d(j,k) ≥ d(i,j) + d(k,l)
a b
i j
k
m
c
y
l
n
fWe will construct a tree T with 4 leaves, s.t. dT(,x,y)=d(x,y) for all for each x,y in {i,j,k,L},
29
Tree construction for L=4
i
j
k
m
l
Construct the tree by the given distances as follows:1. Construct a tree for {i, j,k}, with internal vertex m2.Construct a tree for {i,k,l}, by adding the vertex n and the edge (n,l).
n
The construction guarantees that dT(,x,y)=d(x,y) for all (x,y) except (j,l).
By the 4-points condition, d(j,l) = d(i,l)+ d(j,k)- d(i,k)=dT(i,l)+ dT (j,k)- dT (i,k)= dT(j,l)
30
i
j
k l
Corollary from the construction
If d(i,k) +d(j,l) = d(i,l) +d(j,k) ≥ d(i,j) + d(k,l), then a tree which realizes all the distances except d(j,l), realizes also the distance d(j,l).*
*(j,l) can be replaced by any pair in {i,j}{k,l}.
31
Induction step for L>4: Remove Object L from the set By induction, there is a tree, T’, for {1,2,…,L-1}. For each pair of labeled nodes (i,j) in T’, let aij, bij, cij be
defined by the following figure:
aij
bij
cij
i
j
L
mij
1[ ( , ) ( , ) ( , )]
2ijc d i L d j L d i j
32
Induction step: Pick i and j that minimize cij.
T is constructed by adding L (and possibly mij) to T’, as in the figure. Then d(i,L) = dT(i,L) and d(j,L) = dT(j,L)
Remains to prove: For each k ≠ i,j: d(k,L) = dT(k,L).
aij
bij
cij
i
j
L
mij
T’
33
Induction step (cont.)Let k ≠i,j be an arbitrary node in T’. The minimality of Cij means that {{L,k},{i,j}} is not a split of {i,j,k,L}. Hence n, the branching point of k, is on the path from i to j.
It is given that d(x,y)=dT(x,y) for each x,y in {i,j,k,L}, except d(k,L). So by the corollary, d(k,L)=dT(k,L) too.
aij
bij
cij
i
j
L
mij
T’
k
n
35
Constructing additive trees:The neighbor joining problem
Let i, j be neighboring leaves in a tree, let k be their parent, and let
m be any other vertex.
The formula
shows that we can compute the distances of k to all other leaves.
This suggest the following method to construct tree from a
distance matrix:
1. Find neighboring leaves i,j in the tree,
2. Replace i,j by their parent k and recursively construct a tree T
for the smaller set.
3. Add i,j as children of k in T.
)],(),(),([),( jidmjdmidmkd 2
1
36
Neighbor Finding
How can we find from distances alone a pair of nodes which are neighboring leaves?
Closest nodes aren’t necessarily neighboring leaves.
AB
CD
Next we show one way to find neighbors from distances.
37
Neighbor Finding: Seitou&Nei method
Theorem (Saitou&Nei) Assume all edge weights are positive. If D(i,j) is minimal (among all pairs of leaves), then i and j are neighboring leaves in the tree.
)(),()(),(
:,
.),(
ji
ui
rrjidLjiD
ji
uidri
2
leavesFor
let , leaf aFor leaf a is
Definitions