msc in bio informaticsmscbioinformatics.uab.cat/base/documents/... · mscin bioinformatics module...
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![Page 1: MSc in Bio informaticsmscbioinformatics.uab.cat/base/documents/... · MScin Bioinformatics Module 2: Core BioinformaticsModule 2: Core Bioinformatics Molecular Evolution 22 Sebastián](https://reader034.vdocuments.net/reader034/viewer/2022050612/5fb33368eb5c99064230ddd4/html5/thumbnails/1.jpg)
Molecular Evolution and Phylogeny (2)Sebastián E. Ramos-Onsins
Centre of Research in Agricultural Genomics
(CRAG )
1
Module 2: Core BioinformaticsModule 2: Core Bioinformatics
MSc in Bioinformatics
Course 2014-15
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MSc in Bioinformatics Module 2: Core BioinformaticsModule 2: Core Bioinformatics
2 Sebastián E. Ramos-OnsinsMolecular Evolution
Representation of the genealogical relationships
among species, genes, population or even
individuals.
Phylogeny:
Ziheng Yang (2006)
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MSc in Bioinformatics Module 2: Core BioinformaticsModule 2: Core Bioinformatics
3 Sebastián E. Ramos-OnsinsMolecular Evolution
Phylogeny
A tree is a graphical representation of the relationships between
lineages using a tree structure in nodes and branches.
Rooted vs Unrooted Trees:
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MSc in Bioinformatics Module 2: Core BioinformaticsModule 2: Core Bioinformatics
4 Sebastián E. Ramos-OnsinsMolecular Evolution
Phylogeny
Cladogram vs Phylogram Trees:
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Qualitative Lengths are represented
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MSc in Bioinformatics Module 2: Core BioinformaticsModule 2: Core Bioinformatics
5 Sebastián E. Ramos-OnsinsMolecular Evolution
Phylogeny
Unsolved vs resolved Trees:
Star Tree Partially resolved Tree Resolved Tree
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MSc in Bioinformatics Module 2: Core BioinformaticsModule 2: Core Bioinformatics
6 Sebastián E. Ramos-OnsinsMolecular Evolution
Phylogeny
Species vs Gene Trees:
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Based on multiple information
of the species
Based on a single or few regions of
(ex.) DNA of the species
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MSc in Bioinformatics Module 2: Core BioinformaticsModule 2: Core Bioinformatics
7 Sebastián E. Ramos-OnsinsMolecular Evolution
Phylogeny
Ultrametric and AdditiveTrees: (not excludent)
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Ex: d45 <= d43 = d53
The distances between any three
nodes connected by the same internal
node are equal.
d15 = d1i + dij + djk + dk5
The distances between species on the tips of
the tree are equal to the sum of the lengths
of the branches connecting them.
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MSc in Bioinformatics Module 2: Core BioinformaticsModule 2: Core Bioinformatics
8 Sebastián E. Ramos-OnsinsMolecular Evolution
Phylogeny
Let’s create a tree history using R:
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MSc in Bioinformatics Module 2: Core BioinformaticsModule 2: Core Bioinformatics
9 Sebastián E. Ramos-OnsinsMolecular Evolution
Phylogeny
Tree-reconstruction Methods
- Distance Methods
- Maximum Parsimony
- Maximum Likelihood
- Bayesian Inference
![Page 10: MSc in Bio informaticsmscbioinformatics.uab.cat/base/documents/... · MScin Bioinformatics Module 2: Core BioinformaticsModule 2: Core Bioinformatics Molecular Evolution 22 Sebastián](https://reader034.vdocuments.net/reader034/viewer/2022050612/5fb33368eb5c99064230ddd4/html5/thumbnails/10.jpg)
MSc in Bioinformatics Module 2: Core BioinformaticsModule 2: Core Bioinformatics
10 Sebastián E. Ramos-OnsinsMolecular Evolution
Phylogeny
Tree-reconstruction Methods
- Distance Methods
- Maximum Parsimony
- Maximum Likelihood
- Bayesian Inference
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MSc in Bioinformatics Module 2: Core BioinformaticsModule 2: Core Bioinformatics
11 Sebastián E. Ramos-OnsinsMolecular Evolution
Phylogeny
Tree-reconstruction Methods
- Distance Methods
Two steps:
- Calculate the distance matrix.
- Reconstruct the phylogenetic tree from matrix.
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MSc in Bioinformatics Module 2: Core BioinformaticsModule 2: Core Bioinformatics
12 Sebastián E. Ramos-OnsinsMolecular Evolution
Phylogeny
Tree-reconstruction Methods
- Distance Methods
UPGMA (Unweighted Pair Group Method with Arithmetic Mean)
1 2 3 4
1 0
2 1 0
3 2 4 0
4 3 5 6 0
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MSc in Bioinformatics Module 2: Core BioinformaticsModule 2: Core Bioinformatics
13 Sebastián E. Ramos-OnsinsMolecular Evolution
Phylogeny
Tree-reconstruction Methods
- Distance Methods
UPGMA (Unweighted Pair Group Method with Arithmetic Mean)
1 2 3 4
1 0
2 1 0
3 2 4 0
4 3 5 6 0
3 4 5
3 0
4 6 0
5 3 4 0
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MSc in Bioinformatics Module 2: Core BioinformaticsModule 2: Core Bioinformatics
14 Sebastián E. Ramos-OnsinsMolecular Evolution
Phylogeny
Tree-reconstruction Methods
- Distance Methods
UPGMA (Unweighted Pair Group Method with Arithmetic Mean)
1 2 3 4
1 0
2 1 0
3 2 4 0
4 3 5 6 0
3 4 5
3 0
4 6 0
5 3 4 0
4 6
4 0
6 4.67 0
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MSc in Bioinformatics Module 2: Core BioinformaticsModule 2: Core Bioinformatics
15 Sebastián E. Ramos-OnsinsMolecular Evolution
Phylogeny
Tree-reconstruction Methods
- Distance Methods
UPGMA (Unweighted Pair Group Method with Arithmetic Mean)
node1 node2 go.to.n
ode
Div
1 1 - 5 0.5
2 2 - 5 0.5
3 3 - 6 1.5
4 4 - 7 2.33
5 2 1 6 1.0
6 5 3 7 0.83
7 6 4 - -
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0.5
1.5
2.33
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MSc in Bioinformatics Module 2: Core BioinformaticsModule 2: Core Bioinformatics
16 Sebastián E. Ramos-OnsinsMolecular Evolution
Phylogeny
Tree-reconstruction Methods
- Distance Methods
NJ (Neighbour-Joining): Minimum evolution tree criterion based on the
smallest sum of total length branches.
Starting from a star-tree, join the two nodes that give the minimum length
distance, repeat the process until resolve the tree.
From Yang 2006
To calculate the distances, it is assumed they are additive.
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MSc in Bioinformatics Module 2: Core BioinformaticsModule 2: Core Bioinformatics
17 Sebastián E. Ramos-OnsinsMolecular Evolution
Phylogeny
Tree-reconstruction Methods
- Distance Methods
- Maximum Parsimony
- Maximum Likelihood
- Bayesian Inference
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MSc in Bioinformatics Module 2: Core BioinformaticsModule 2: Core Bioinformatics
18 Sebastián E. Ramos-OnsinsMolecular Evolution
Phylogeny
Tree-reconstruction Methods
-Maximum Parsimony:
-Criterion based on minimum evolution.
-The best tree is the tree with the minimum number of changes.
-Reconstruct all possible trees assigning values to the internal nodes and score the
trees according to the number of changes.
-Heuristic methods are necessary for large samples.
-Long Branch Attraction (LBA) is specially problematic in MP trees; MP trees support
wrong reconstructions in case having longer branches (join together).
A
A
AG
G G G
A
A G A Aa
b d
c a
b
d
c
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MSc in Bioinformatics Module 2: Core BioinformaticsModule 2: Core Bioinformatics
19 Sebastián E. Ramos-OnsinsMolecular Evolution
Phylogeny
Tree-reconstruction Methods
- Distance Methods
- Maximum Parsimony
- Maximum Likelihood
- Bayesian Inference
![Page 20: MSc in Bio informaticsmscbioinformatics.uab.cat/base/documents/... · MScin Bioinformatics Module 2: Core BioinformaticsModule 2: Core Bioinformatics Molecular Evolution 22 Sebastián](https://reader034.vdocuments.net/reader034/viewer/2022050612/5fb33368eb5c99064230ddd4/html5/thumbnails/20.jpg)
MSc in Bioinformatics Module 2: Core BioinformaticsModule 2: Core Bioinformatics
20 Sebastián E. Ramos-OnsinsMolecular Evolution
Phylogeny
Tree-reconstruction Methods
- Maximum Likelihood:
-Criterion is the maximum probability tree.
-Calculate the probability of a tree for a given evolutionary model
-Computationally expensive calculations to obtain the ML tree.
-Nice statistical properties. Popular method and gives reasonable results.
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MSc in Bioinformatics Module 2: Core BioinformaticsModule 2: Core Bioinformatics
21 Sebastián E. Ramos-OnsinsMolecular Evolution
Phylogeny
Tree-reconstruction Methods
- Distance Methods
- Maximum Parsimony
- Maximum Likelihood
- Bayesian Inference
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MSc in Bioinformatics Module 2: Core BioinformaticsModule 2: Core Bioinformatics
22 Sebastián E. Ramos-OnsinsMolecular Evolution
Phylogeny
Tree-reconstruction Methods
- Bayesian Inference
Seek for a distribution of compatible trees with the highest probabilities
according to a given model and a prior distribution of the parameters included.
Main criticisms concerning the selection of the prior distributions.
Method also popular and gives reasonable results.
Based on the Bayes theorem (inverse probability theorem):
P(A|B) = P(A) x P(B|A)
P(B)
P(A) x P(B|A)
P(A) x P(B|A) + P(Ā) x P(B|Ā)=
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MSc in Bioinformatics Module 2: Core BioinformaticsModule 2: Core Bioinformatics
23 Sebastián E. Ramos-OnsinsMolecular Evolution
Phylogeny
Let’s do a simple tree reconstruction using R:
![Page 24: MSc in Bio informaticsmscbioinformatics.uab.cat/base/documents/... · MScin Bioinformatics Module 2: Core BioinformaticsModule 2: Core Bioinformatics Molecular Evolution 22 Sebastián](https://reader034.vdocuments.net/reader034/viewer/2022050612/5fb33368eb5c99064230ddd4/html5/thumbnails/24.jpg)
MSc in Bioinformatics Module 2: Core BioinformaticsModule 2: Core Bioinformatics
24 Sebastián E. Ramos-OnsinsMolecular Evolution
Phylogeny
Support of the phylogenetic Trees obtained
Different methods to contrast the support of phylogenetic trees
-Depending on the method of reconstruction (Bremer Support in MP)
-Non-parameteric methods of resampling (no model is assumed)
-Parametric methods (assuming a model)
![Page 25: MSc in Bio informaticsmscbioinformatics.uab.cat/base/documents/... · MScin Bioinformatics Module 2: Core BioinformaticsModule 2: Core Bioinformatics Molecular Evolution 22 Sebastián](https://reader034.vdocuments.net/reader034/viewer/2022050612/5fb33368eb5c99064230ddd4/html5/thumbnails/25.jpg)
MSc in Bioinformatics Module 2: Core BioinformaticsModule 2: Core Bioinformatics
25 Sebastián E. Ramos-OnsinsMolecular Evolution
Phylogeny
Support of the phylogenetic Trees obtained
Different methods to contrast the support of phylogenetic trees
-Depending on the method of reconstruction (Bremer Support in MP)
-Non-parameteric methods of resampling (no model is assumed)
-Parametric methods (assuming a model)
![Page 26: MSc in Bio informaticsmscbioinformatics.uab.cat/base/documents/... · MScin Bioinformatics Module 2: Core BioinformaticsModule 2: Core Bioinformatics Molecular Evolution 22 Sebastián](https://reader034.vdocuments.net/reader034/viewer/2022050612/5fb33368eb5c99064230ddd4/html5/thumbnails/26.jpg)
MSc in Bioinformatics Module 2: Core BioinformaticsModule 2: Core Bioinformatics
26 Sebastián E. Ramos-OnsinsMolecular Evolution
Phylogeny
Support of the phylogenetic Trees obtained
Non-parameteric methods of resampling (no model is assumed)
Jacknife
Bootstrap
-Draw a subset of the data
-This data is used to infer again the tree
-The support for the obtained tree is obtained from the number of
times the same clusters (nodes) are obtained in the
pseudoreplicates.
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MSc in Bioinformatics Module 2: Core BioinformaticsModule 2: Core Bioinformatics
27 Sebastián E. Ramos-OnsinsMolecular Evolution
Phylogeny
Support of the phylogenetic Trees obtained
Non-parameteric methods of resampling (no model is assumed)
Jacknife
Bootstrap
Assumptions:
-Data size is large, so we have accurate estimates of the error.
-Each position (column in the alignment) is independent from each
other.
Results:
The resulted values are not directly a probability value but a support
value of the reliability of the obtained tree.
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MSc in Bioinformatics Module 2: Core BioinformaticsModule 2: Core Bioinformatics
28 Sebastián E. Ramos-OnsinsMolecular Evolution
Phylogeny
Support of the phylogenetic Trees obtained
Non-parameteric methods of resampling (no model is assumed)
Bootstrap
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MSc in Bioinformatics Module 2: Core BioinformaticsModule 2: Core Bioinformatics
29 Sebastián E. Ramos-OnsinsMolecular Evolution
Phylogeny
Support of the phylogenetic Trees obtained
Non-parameteric methods of resampling (no model is assumed)
Bootstrap
1234567
ATCTTCT
GTCTTCT
ATGATCC
ATGAACC
AGGAACC
1
2
3
4
5
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MSc in Bioinformatics Module 2: Core BioinformaticsModule 2: Core Bioinformatics
30 Sebastián E. Ramos-OnsinsMolecular Evolution
Phylogeny
Support of the phylogenetic Trees obtained
Non-parameteric methods of resampling (no model is assumed)
Bootstrap
1234567
ATCTTCT
GTCTTCT
ATGATCC
ATGAACC
AGGAACC
Resampling
1137721
AACTTTA
GGCTTTG
AAGCCTA
AAGCCTA
AAGCCGA
1
2
3
4
5
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MSc in Bioinformatics Module 2: Core BioinformaticsModule 2: Core Bioinformatics
31 Sebastián E. Ramos-OnsinsMolecular Evolution
Phylogeny
Support of the phylogenetic Trees obtained
Non-parameteric methods of resampling (no model is assumed)
Bootstrap
Resampling Do Tree
1
2
3
4
5
1
2
3
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5
1234567
ATCTTCT
GTCTTCT
ATGATCC
ATGAACC
AGGAACC
1137721
AACTTTA
GGCTTTG
AAGCCTA
AAGCCTA
AAGCCGA
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MSc in Bioinformatics Module 2: Core BioinformaticsModule 2: Core Bioinformatics
32 Sebastián E. Ramos-OnsinsMolecular Evolution
Phylogeny
Support of the phylogenetic Trees obtained
Non-parameteric methods of resampling (no model is assumed)
Bootstrap
Resampling Do Tree
1
2
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4
5
1
2
3
4
5
+1
+1
+0
1234567
ATCTTCT
GTCTTCT
ATGATCC
ATGAACC
AGGAACC
1137721
AACTTTA
GGCTTTG
AAGCCTA
AAGCCTA
AAGCCGA
![Page 33: MSc in Bio informaticsmscbioinformatics.uab.cat/base/documents/... · MScin Bioinformatics Module 2: Core BioinformaticsModule 2: Core Bioinformatics Molecular Evolution 22 Sebastián](https://reader034.vdocuments.net/reader034/viewer/2022050612/5fb33368eb5c99064230ddd4/html5/thumbnails/33.jpg)
MSc in Bioinformatics Module 2: Core BioinformaticsModule 2: Core Bioinformatics
33 Sebastián E. Ramos-OnsinsMolecular Evolution
Phylogeny
Support of the phylogenetic Trees obtained
Non-parameteric methods of resampling (no model is assumed)
Bootstrap
Resampling Do Tree
1
2
3
4
5
1
2
3
4
5
+1
+1
+0
… and repeat again n times!
1234567
ATCTTCT
GTCTTCT
ATGATCC
ATGAACC
AGGAACC
1137721
AACTTTA
GGCTTTG
AAGCCTA
AAGCCTA
AAGCCGA
![Page 34: MSc in Bio informaticsmscbioinformatics.uab.cat/base/documents/... · MScin Bioinformatics Module 2: Core BioinformaticsModule 2: Core Bioinformatics Molecular Evolution 22 Sebastián](https://reader034.vdocuments.net/reader034/viewer/2022050612/5fb33368eb5c99064230ddd4/html5/thumbnails/34.jpg)
MSc in Bioinformatics Module 2: Core BioinformaticsModule 2: Core Bioinformatics
34 Sebastián E. Ramos-OnsinsMolecular Evolution
Phylogeny
Let’s do a Bootstrap analysis using R:
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MSc in Bioinformatics Module 2: Core BioinformaticsModule 2: Core Bioinformatics
35 Sebastián E. Ramos-OnsinsMolecular Evolution
Phylogeny
Support of the phylogenetic Trees obtained
Different methods to contrast the support of phylogenetic trees
-Depending on the method of reconstruction (Bremer Support in MP)
-Non-parameteric methods of resampling (no model is assumed)
-Parametric methods (assuming a model)
- Parametric bootstraping
- Bayesian Inference
![Page 36: MSc in Bio informaticsmscbioinformatics.uab.cat/base/documents/... · MScin Bioinformatics Module 2: Core BioinformaticsModule 2: Core Bioinformatics Molecular Evolution 22 Sebastián](https://reader034.vdocuments.net/reader034/viewer/2022050612/5fb33368eb5c99064230ddd4/html5/thumbnails/36.jpg)
MSc in Bioinformatics Module 2: Core BioinformaticsModule 2: Core Bioinformatics
36 Sebastián E. Ramos-OnsinsMolecular Evolution
Phylogeny
Support of the phylogenetic Trees obtained
Different methods to contrast the support of phylogenetic trees
-Depending on the method of reconstruction (Bremer Support in MP)
-Non-parameteric methods of resampling (no model is assumed)
-Parametric methods (assuming a model)
- Parametric bootstraping
Repetition of phylogeny based on a given model
![Page 37: MSc in Bio informaticsmscbioinformatics.uab.cat/base/documents/... · MScin Bioinformatics Module 2: Core BioinformaticsModule 2: Core Bioinformatics Molecular Evolution 22 Sebastián](https://reader034.vdocuments.net/reader034/viewer/2022050612/5fb33368eb5c99064230ddd4/html5/thumbnails/37.jpg)
MSc in Bioinformatics Module 2: Core BioinformaticsModule 2: Core Bioinformatics
37 Sebastián E. Ramos-OnsinsMolecular Evolution
Phylogeny
Support of the phylogenetic Trees obtained
Different methods to contrast the support of phylogenetic trees
-Depending on the method of reconstruction (Bremer Support in MP)
-Non-parameteric methods of resampling (no model is assumed)
-Parametric methods (assuming a model)
-Bayesian Inference
-Bayesian inference itself collects compatible trees assuming
the uncertainty of the tree
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MSc in Bioinformatics Module 2: Core BioinformaticsModule 2: Core Bioinformatics
38 Sebastián E. Ramos-OnsinsMolecular Evolution
Phylogeny
Phylogenomics: An approach to obtain the Species Tree
In case the speciation process is close among species, a gene tree can give
an erroneous topology:
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MSc in Bioinformatics Module 2: Core BioinformaticsModule 2: Core Bioinformatics
39 Sebastián E. Ramos-OnsinsMolecular Evolution
Phylogeny
Phylogenomics: An approach to obtain the Species Tree
In case the speciation process is close among species, a gene tree can give
an erroneous topology:
Incomplete Lineage Sorting
Anomalous Region
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MSc in Bioinformatics Module 2: Core BioinformaticsModule 2: Core Bioinformatics
40 Sebastián E. Ramos-OnsinsMolecular Evolution
Phylogeny
Phylogenomics: An approach to obtain the Species Tree
-Having a large number of regions (or also information from different
sources) can help to solve the incongruence.
-Heuristic methods based on a Supermatrix (concatenate all regions as
one) or on a Supertree (make a single tree from individual trees) are used.
-Likelihood-based methods are computationally expensive but are
statistically well supported.
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MSc in Bioinformatics Module 2: Core BioinformaticsModule 2: Core Bioinformatics
41 Sebastián E. Ramos-OnsinsMolecular Evolution
Phylogeny
Let’s try to obtain the species Tree using the library phybase in R:
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MSc in Bioinformatics Module 2: Core BioinformaticsModule 2: Core Bioinformatics
42 Sebastián E. Ramos-OnsinsMolecular Evolution
Phylogeny
Use of phylogenies for different objectives:
- Ancestral sequence reconstruction
- Dating ancestral events
- Detection of selection (Syn vs Nsyn positions)
- Correlation of the phylogenetic signal with phenotypic Traits