Prsentation PowerPoint

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Molecular Phylogeny
Fredj Tekaia Institut Pasteur tekaia_at_pasteur.fr
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Large scale comparative genome analysis revealed
significant evolutionary processes
Evolutionary processes include
Ancestor
species genome
selection
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Phylogeny analyses
Starting point a set of homologous, aligned
DNA or protein sequences Result of the
process a tree describing evolutionary
relationships between studied sequences i.e a
phylogenetic tree
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Plan Introduction Evolutionary processes
Homologs - Paralogs - Orthologs Key features
of phylogenetic trees Gene tree - species
tree Multiple sequence alignment Methods
for Phylogenetic tree construction Statistical
evaluation of phylogenetic trees Introduction
to Phylogenomy Introduction to Lateral Gene
Transfer.
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Within the field of phylogenetic reconstruction
and taxonomy there have been two different ways
and two different philosophies to the process of
reconstructing a phylogeny. One approach is the
phenetic approach. In this approach, a tree is
constructed by considering the phenotypic
similarities of the species without trying to
understand the evolutionary pathways of the
species. Since a tree constructed by this method
does not necessarily reflect evolutionary
relationships but rather is designed to represent
phenotypic similarity, trees constructed via this
method are called phenograms. A phylogenetic tree
based on such information is often termed a
dendrogram (a branching order that may or may not
be the correct phylogeny). The second approach is
called the cladistic approach. Via these methods,
a tree is reconstructed by considering the
various possible pathways of evolution and
choosing from amongst these the best possible
tree. Trees reconstructed via these methods are
called cladograms. The phenetic philosophy as a
way to do taxonomy is definitely incorrect.
However, this does not mean that phenetic methods
are necessarily poor estimates of the
cladogram. For character data where ancestral
forms are known and to construct a taxonomic
classification the cladistic approach is almost
certainly superior. However, the cladistic
methods are often difficult to implement with
assumptions that are not always satisfied with
molecular data. The phenetic approaches are
generally faster algorithms and often have nicer
statistical properties for molecular data. Hence,
there appears to be a place for both types of
methods in the analysis of molecular sequence
data.
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Examples of phylogenetic trees
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Pace (2001) described a tree of life based on
small subunit rRNA sequences. Pace, N. R.
(1997) Science 276, 734-740 This tree shows the
main three branches described by Woese and
colleagues.
This tree is referred to as the tree of life or
the universal tree.
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Chlamydiae
Fig. 1. Phylogeny of chlamydiae. 16S rRNA-based
neighbor-joining tree showing the affiliation of
environmental and pathogenic chlamydiae with
major bacterial phyla. Arrow, to outgroup. Scale
bar, 10 estimated evolutionary distance. Science
304728-30.2004.
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Chen et al. NAR 34 D363-D368 (2006)
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Hurles M (2004) Gene Duplication The Genomic
Trade in Spare Parts. PLoS Biol 2(7) e206.
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Molecular evolution
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Molecular Phylogenetic Analysis
Study of evolutionary relationships between genes
and species.
The actual pattern of evolutionary history is
the phylogeny or evolutionary tree which we try
to estimate.
A tree is a mathematical structure which is
used to model the actual evolutionary history of
a group of sequences or organisms.
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Molecular Phylogeny
Analysis Specifying the history of gene
evolution is one of the most important aims of
the current study of molecular evolution
Molecular phylogeny methods allow, from a given
set of aligned sequences, the suggestion of
phylogenetic trees (inferred trees) which aim at
reconstructing the history of successive
divergence which took place during the evolution,
between the considered sequences and their common
ancestor. These trees may not be the same as the
true tree Reconstruction of phylogenetic trees
is a statistical problem, and a reconstructed
tree is an estimate of a true tree with a given
topology and given branch length The accuracy
of this estimation should be statistically
established In practice, phylogenetic analyses
usually generate phylogenetic trees with accurate
parts and imprecise parts.
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Nucleotide, amino-acid sequences
3 different DNA positions but only one
different amino acid position 2 of the
nucleotide substitutions are therefore synonymous
and one is non-synonymous.
DNA yields more phylogenetic information than
proteins. The nucleotide sequences of a pair of
homologous genes have a higher information
content than the amino acid sequences of the
corresponding proteins, because mutations that
result in synonymous changes alter the DNA
sequence but do not affect the amino acid
sequence. (Amino-acid sequences are more
efficiently aligned).
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Phenetics and
Cladistics Phenetics (Michener and Sokal, 1957)
Pheneticists argued that classifications should
encompass as many variable characters as
possible, these characters being analysed by
rigorous mathematical methods. Such methods (exp.
distance based) place a greater emphasis on the
relationships among data sets than the paths they
have taken to arrive at their current
states. Cladistics (Hennig 1966) emphasizes the
need for large datasets but differs from
phenetics in that it does not give equal weight
to all characters. Cladists, are generally more
interested in evolutionary pathways than in
relationships (exp. maximum parsimony).
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Key features of phylogenetic trees
An unrooted tree





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Rooted and Unrooted trees
An important distinction in phylogenetics
trees that make an inference about a common
ancestor and the direction of evolution, and
those that do not.
In rooted trees a single node is designated as
a common ancestor, and a unique path leads from
it through evolutionary time to any other node.
Unrooted trees only specify the relationships
between nodes and say nothing about the direction
in which evolution occured.
Roots can usually be assigned to unrooted trees
through the use of an outgroup.
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Key features of phylogenetic trees
The numbers of possible rooted (NR) and unrooted
(NU) trees for n sequences are given by
NR (2n-3)!/2n-2(n-2)! NU (2n-5)!/2n-3(n-3)!

• n NR NU
• 1
  1
• 3
  1
• 15 3
• 105 15
• 34459425 2027025

Note that only one of all possible trees can
represent the true tree that represents
phylogenetic relationships among the sequences.
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Gene tree - Species tree
The two events - mutation and speciation- are not
expected to occur at the same time. So gene trees
cannot represent species tree.
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• Tree construction how to proceed?
• 1. Consider the set of sequences to analyse
• 2. Align "properly" these sequences
• 3. Apply phylogenetic making tree methods
• 4. Evaluate statistically the obtained
  phylogenetic tree.
• Methodology
• 1- Multiple alignment
• 2- Bootstrapping
• 3- Consensus tree construction and evaluation

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Alignment is essential preliminary to tree
construction
If errors in indel placement are made in a
multiple alignment then the tree reconstructed by
phylogenetic analysis is unlikely to be correct.
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Alignment and Gaps
The quality of the alignment is essential
each column of the alignment (site) is supposed
to contain homologous residues (nucleotides,
amino acids) that derive from a common
ancestor.gt Unreliable parts of the alignment
must be omitted from further phylogenetic
analysis. Most methods take into account only
substitutions gaps (insertion/deletion events)
are not used.gt gaps-containing sites are
ignored.
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Steps in Multiple Sequence Alignments
A common strategy of several popular multiple
sequence alignment algorithms is to 1- generate
a pairwise distance matrix based on all possible
pairwise alignments between the sequences being
considered 2- use a statistically based approach
to construct an initial tree 3- realign the
sequences progressively in order of their
relatedness according to the inferred tree 4-
construct a new tree from the pairwise distances
obtained in the new multiple alignment 5- repeat
the process if the new tree is not the same as
the previous one.
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Procedure
An efficient procedure consists of aligning
amino-acid sequences and use the resulting
alignment as template for corresponding
nucleotide sequences. Alignment is then garanteed
at the codon level.
Note clean multiple alignment from gaps common
to the majority of considered sequences
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Example
SPO2.113.dna
gtKLLA-IPF5339 ATGGCTCCACCTACGAAAATACTCGGTCTTGACACT
CAGCAGAGAATGCTTCAACGTGGT GAAAATTGCAGTTTAAAGTCTCTGG
TACAGAATGAATGTGCTTTTAATGGTAATGACTAT GTGTGTACGCCTTT
CAAAAGACTATTTGAACAATGCATGGTGAAGGATGGACGTGTATTA AAC
ATTGAGGTAACAAATCTGAACACCAACAGATGA gtKLWA-IPF3854 AT
GGCACCGCCCACAGTAGTGTTTGGCAAAGAGGAACTAGAGCCTCTCTTGC
GCAATGTT ATGGCGACGTGTATCTTCAAGTCTCTGACTCAAAGCGAATG
CAACTTTGACGGTCATCAA TATGTTTGTGTACCTTTCAAGAGGGTGTTC
AAAGAATGCAAGGTGGATGGGAAATCAATC AGAATAGAGGTGACAGATA
GAAACACCAACAAGGCAAAAGCTGATGAGATGGTTGACAGT TTCTGGAA
TTCCCGAAAGTCATTTACACGGAATTGA
SPO2.113.pep
gtKLLA-IPF5339 MAPPTKILGLDTQQRMLQRGENCSLKSLVQNECAFN
GNDYVCTPFKRLFEQCMVKDGRVL NIEVTNLNTNR gtKLWA-IPF3854
MAPPTVVFGKEELEPLLRNVMATCIFKSLTQSECNFDGHQYVCVPFKRV
FKECKVDGKSI RIEVTDRNTNKAKADEMVDSFWNSRKSFTRN
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Alignment of aa sequences gtKLLA-IPF5339 MAPPTKIL
GLDTQQRMLQ-RGENCSLKSLVQNECAFNGNDYVCTPFKRLFEQCMVKDG
RVLNIEVTNLNTNR-------------------- gtKLWA-IPF3854 M
APPTVVFGKEELEPLLRNVMATCIFKSLTQSECNFDGHQYVCVPFKRVFK
ECKV-DGKSIRIEVTDRNTNKAKADEMVDSFWNSRKSFTRN
Using corresponding dna sequences gtKLLA-IPF5339 A
TGGCTCCACCTACGAAAATACTCGGTCTTGACACTCAGCAGAGAATGCTT
CAACGTGGTGAAAATTGCAGTTTAAAGTCTCTGGTACAGAATGAATGTGC
TTTTAATGGTAATGACTATGTGTGTACGCCTTTCAAAAGACTATTTGAAC
AATGCATGGTGAAGGATGGACGTGTATTAAACATTGAGGTAACAAATCTG
AACACCAACAGATGA gtKLWA-IPF3854 ATGGCACCGCCCACAGTAGT
GTTTGGCAAAGAGGAACTAGAGCCTCTCTTGCGCAATGTTATGGCGACGT
GTATCTTCAAGTCTCTGACTCAAAGCGAATGCAACTTTGACGGTCATCAA
TATGTTTGTGTACCTTTCAAGAGGGTGTTCAAAGAATGCAAGGTGGATGG
GAAATCAATCAGAATAGAGGTGACAGATAGAAACACCAACAAGGCAAAAG
CTGATGAGATGGTTGACAGTTTCTGGAATTCCCGAAAGTCATTTACACGG
AATTGA
Construct corresponding dna alignment
gtKLLA-IPF5339 ATGGCTCCACCTACGAAAATACTCGGTCTTGAC
ACTCAGCAGAGAATGCTTCAA---CGTGGTGAAAATTGCAGTTTAAAGTC
TCTGGTACAGAATGAATGTGCTTTTAATGGTAATGACTATGTGTGTACGC
CTTTCAAAAGACTATTTGAACAATGCATGGTGAAGGATGGACGTGTATTA
AACATTGAGGTAACAAATCTGAACACCAACAGA-----------------
------------------------------------------- gtKLWA-
IPF3854 ATGGCACCGCCCACAGTAGTGTTTGGCAAAGAGGAACTAG
AGCCTCTCTTGCGCAATGTTATGGCGACGTGTATCTTCAAGTCTCTGACT
CAAAGCGAATGCAACTTTGACGGTCATCAATATGTTTGTGTACCTTTCAA
GAGGGTGTTCAAAGAATGCAAGGTG---GATGGGAAATCAATCAGAATAG
AGGTGACAGATAGAAACACCAACAAGGCAAAAGCTGATGAGATGGTTGAC
AGTTTCTGGAATTCCCGAAAGTCATTTACACGGAAT
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Phylogenetic tree construction methods
A phylogenetic tree is characterised by its
topology (form) and its length (sum of its branch
lengths) Each node of a tree is an
estimation of the ancestor of the elements
included in this node
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Phylogenetic tree construction methods
There are three main families of Methods
Parsimony Distance Methods Maximum likelihood
Methods
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Phylogenetic tree construction methods
Methods directly based on sequences Maximum
Parsimony find a phylogenetic tree that
explains the data, with as few evolutionary
changes as possible. Maximum likelihood find
a tree that maximizes the probability of the
genetic data given the tree. Methods indirectly
based on sequences Distance based methods
(Neighbour Joining (NJ)) find a tree such that
branch lengths of paths between sequences
(species) fit a matrix of pairwise distances
between sequences.
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Parsimony
The concept of parsimony is at the heart of all
character-based methods of phylogenetic
reconstruction. The 2 fundamental ideas of
biological parsimony are 1- mutations are
exceedingly rare events (?) 2- the more
unlikely events a model invokes, the less likely
the model is to be correct.
As a result, the relationship that requires the
fewest number of mutations to explain the current
state of the sequences being considered, is the
relationship that is most likely to be correct.
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Parsimony
Informative and Uninformative Sites
Multiple sequence alignment, for a parsimony
approach, contains positions that fall into two
categories in terms of their information content
those that have information (are informative)
and those that do not (are uninformative). Example
seq 1 2 3 4 5 6 1 G G G G G G 2 G G G A G T 3 G
G A T A G 4 G A T C A T Position 1 is said
invariant and therefore uninformative, because
all trees invoke the same number of mutations
(0) Position 2 is uninformative because 1
mutation occurs in all three possible
trees Position 3 idem, because 2 mutations
occur Position 4 requires 3 mutations in all
possible trees. Positions 5 and 6 are
informative, because one of the trees invokes
only one mutation and the other 2 alternative
trees both require 2 mutations.
In general, for a position to be informative
regardless of how many sequences are aligned, it
has to have at least 2 different nucleotides, and
each of these nucleotides has to be present at
least twice.
Krane Raymer 2002
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Maximum Parsimony (Fitch, 1977) Parsimony
criterion consists of determining the minimum
number of changes (substitutions) required to
transform a sequence to its nearest neighbor. The
maximum parsimony algorithm searches for the
minimum number of genetic events (nucleotide
substitutions or amino-acid changes) to infer the
most parsimonious tree from a set of
sequences. The best tree is the one which needs
the fewest changes. Problems 1. within
practical computational limits, this often leads
to the generation of tens or more "equally most
parsimonious trees" which makes it difficult to
justify the choice of a particular tree 2. long
computation time is needed to construct a tree.
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Maximum Parsimony (Fitch, 1977),...
The Maximum parsimony method takes account of
information pertaining to character variation in
each position of the sequences multiple
alignment, to recreate the series of nucleotide
changes. The assumption, possibly erroneous, is
that evolution follows the shortest possible
route and that the correct phylogenetic tree is
therefore the one that requires the minimum
number of nucleotide changes to produce the
observed differences between the sequences. Trees
are therefore constructed at random and the
nucleotide changes that they involve, calculated
until all possible topologies have been examined
and the one requiring the smallest number of
steps identified. This is presented as the most
likely inferred tree.
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Distance Methods

• Each phylogenetic tree induces a matrix of
  distances between sequence pairs
• A given distance matrix corresponds to a single
  phylogenetic tree

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Constructing Phylogenetic trees using Distance
Methods
a) Sequence alignment b) Matrix of evolutionary
distances between pairs of sequences c) Distance
methods fit a tree to this matrix.
k
lk
Di,j the distance between i and j sequences
i
li
lc
lr
lm
l
d(i,m) li lc lr lj
lj
m
j
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Constructing Phylogenetic trees using Distance
Methods Di,j the distance between i and j
sequences di,j sum of branches on the tree
path from i to j The phylogeny makes an
estimation of the distance for each pair as the
sum of branch lengths in the path from one
sequence to another through the tree. A measure
of how close is the tree to D is given by the
least square criterion   ?( Di,j - di,j )2/
D2ij i,j The phylogenetic tree topology is
constructed by using a cluster analysis method
(like the NJ method). 1. easy to perform 2.
fast calculation 3. fit for sequences having
high similarity scores drawbacks 1. all
sites are generally equally treated (do not take
into account differences of substitution rates )
2. not applicable to distantly related
sequences 3. Some of the information is lost,
particularly those pertaining to the identities
of the ancestral and derived nucleotides at each
position in the multiple alignment
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Example of distance mesureKimuras two
parameter distance (DNA)

• Hypotheses of the model
• a)All sites evolve independently and following
  the same process.
• b) Substitutions occur according to two
  probabilities
• One for transitions, one for transversions.
• Transitions G ltgtA or C ltgtT Transversions
  other changes
• c) The base substitution process is constant in
  time.
• Quantification of evolutionary distance (d) as
  a function of the fraction of observed
  differences (p transitions, q transversions)

Kimura (1980) J. Mol. Evol. 16111
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Neighbor-Joining method (Saitou Nei 1987)


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Maximum likelihood
This approach is a purely statistically based
method. Probabilities are considered for every
individual nucleotide substitution in a set of
sequence alignment. Exp.
Since transitions (exchanging purine for a purine
and pyrimidine for a pyrimidine) are observed
roughly 3 times as often as transversions
(exchanging a purine for a pyrimidine or vice
versa) it can be reasonably argued that a
greater likelihood exists that the sequence with
C and T are more closely related to each other
than they are to the sequence with G.
Calculation of probabilities is complicated by
the fact that the sequence of the common ancestor
to the sequences considered being unknown.
Furthermore multiple substitutions may have
occurred at one or more sites and that all sites
are not necessarily independent or equivalent.
.. C.. ..T.. ..G..
Still, objective criteria can be applied to
calculating the probability for every site and
for every possible tree that describes the
relationships of the sequences in a multiple
alignment.
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Maximum likelihood According to this method, the
bases (nucleotides or amino acids) of all
sequences at each site are considered separately
(as independent), and the log-likelihood of
having these bases are computed for a given
topology by using a particular probability model.
This log-likelihood is added for all sites, and
the sum of the log-likelihood is maximized to
estimate the branch length of the tree. This
procedure is repeated for all possible
topologies, and the topology that shows the
highest likelihood is chosen as the final
tree. Notes 1. This is the best justified
method from a theoretical viewpoint 2. ML
estimates the branch lengths of the final tree
3. ML methods are usually consistent 4.
Sequence simulation experiments have shown that
this method works better than all others in most
cases. Drawbacks  they need long computation
time to construct a tree.
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• The choice of the outgroup
• Most of phylogenetic methods construct unrooted
  trees.
• It is best to root such trees on biological
  grounds.
• The most used technique consists of including
  in the sequence data set to be analysed, a
  sequence which has some relation with the
  considered sequences without belonging to the
  same family.
• The aim is to normalize the branches of the
  unrooted tree relatively to the length of the
  branch related to the outgroup.

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• Evaluation of different methods
• None of the previous methods of phylogenetic
  reconstruction makes any garantee that they yield
  the one true tree that describes the evolutionary
  history of a set of aligned sequences
• There is at present no statistical method
  allowing comparisons of trees obtained from
  different phylogenetic methods nevertheless many
  attempts have been made to compare the relative
  consistency of the existing methods.
• The consistency depends on many factors,
  including the topology and branch lengths of the
  real tree, the transition/transversion rate and
  the variability of the substitution rates.
• In practice, one infers phylogeny between
  sequences which do not generally meet the
  specified hypothesis.
• One expects that if sequences have strong
  phylogenetic relationships, different methods
  will result in the same phylogenetic tree.

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• Statistical evaluation of the obtained
  phylogenetic tree
• The accuracy is dependent on the considered
  multiple sequence alignments
• ML estimates branch lengths, their degree of
  significance and their confidence limits
• At present only sampling techniques allow to
  test the topology of a phylogenetic tree
• Bootstrapping
• It consists of drawing columns from a sample of
  aligned sequences,
• with replacement, until one gets a data set of
  the same size as the
• original one (usually some columns are sampled
  several times and
• others left out).

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Bootstrapping
Constructs a new multiple alignment at random
from the real alignment, with the same size. Note
that the same column can be sampled more than
once, and consequently some columns are not
sampled.
ATAGCCATA ATACCCATG ATACCCATA ATAGCCATA ATCCCCCAT
TCAAATGCA TCGAATCCA TCAAATCCA TCAAATGCA TCAACACCC
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Properties of Bootstrap procedure
Internal branches supported by 90 of
replicates are considered as statistically
significant. The bootstrap procedure only
detects if sequence length is enough to support a
particular node. The bootstrap procedure does
not help determining if the tree-building method
is good. A wrong tree can have 100 bootstrap
support for all its branches!
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• Methodology
• 1. Consider the set of sequences to analyse
• 2. Align "properly" these sequences
• 3. Apply phylogenetic making tree methods
• 4. Evaluate statistically the obtained
  phylogenetic tree.
• 1- Multiple alignment
• 2- Bootstrapping (100 samples)
• 3. Apply phylogenetic making tree methods
• 4- Consensus tree construction and evaluation

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Tree and sequence simulation experiment
P, PHYML F, fastDNAml L, NJML D, DNAPARS N, NJ
5000 random trees 40 taxa, 500 bases no molecular
clock varying tree length K2P, a 2
Manolo Gouy
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Introduction to Phylogenomy
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Pace (2001) described a tree of life based on
small subunit rRNA sequences. Pace, N. R.
(1997) Science 276, 734-740 This tree shows the
main three branches described by Woese and
colleagues.
This tree is referred to as the tree of life or
the universal tree.
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Introduction to Phylogenomy
Species tree construction The phylogeny of
single genes may be different from the phylogeny
of their corresponding species Idea of
considering many genes instead of only one gene
to estimate species phylogeny Concatenation of
the set of genes common to the considered
species Estimate species phylogeny from the
concatenated genes Some difficulties related
to this procedure.
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Problems with species tree construction main
difficulties in species tree construction include
extensive incongruence between alternative
phylogenies generated from single-gene data sets
Genes don't evolve at the same rate or in the
same way, the evolutionary history inferred from
one gene, say for rRNA, may be different from
what another gene appears to show.
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phylogenomic tree (based on concatenation of
a gene sample common to the considered species)
genes don't evolve at the same rate or in the
same way
a limited number of genes are shared among all
species
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These methods suffer difficulties related to the
phylogenetic tree construction global sequence
alignment (quality, gaps,...) substitution
variations between genes different
evolutionary histories of genes substitution
saturation...
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Horizontal/Lateral Gene Transfer (HGT/LGT)
Genome comparisons (particularly bacterial)
show that during evolution a significant number
of genes were laterally transferred from one
species to another Tranferred genes are very
difficult to detect such?
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Lateral gene transfer and the nature of bacterial
innovation Howard Ochman, Jeffrey G. Lawrence and
Eduardo A. Groisman (2000) Nature 405,
299-304. Unlike eukaryotes, which evolve
principally through the modification of existing
genetic information, bacteria have obtained a
significant proportion of their genetic diversity
through the acquisition of sequences from
distantly related organisms. Horizontal gene
transfer produces extremely dynamic genomes in
which substantial amounts of DNA are introduced
into and deleted from the chromosome. These
lateral transfers have effectively changed the
ecological and pathogenic character of bacterial
species.
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Plus denotes the presence and minus the absence
of a trait in more than 85 of strains.
Evolutionary relationships among species are
based on nucleotide sequence information. In many
cases, genes acquired by horizontal transfer
confer the species-specific traits.
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Lengths of bars denote the amount of
protein-coding DNA. For each bar, the native DNA
is blue foreign DNA identifiable as mobile
elements, including transposons and
bacteriophages, is yellow, and other foreign DNA
is red. The percentage of foreign DNA is noted to
the right of each bar. 'A' denotes an Archaeal
genome.
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Lateral gene transfer - what a problem for
phylogenetics! How lateral gene transfer between
prokaryotes subsequent to the origins of
organelles can lead to erroneous inferences of
eukaryotic gene origins. In the lower panel, a
case of lateral gene transfer (LGT) is depicted
as described in the text. The mechanism of LGT
sketched here is intended to mean conjugation,
but many mechanisms of lateral gene transfer are
known and for the purposes of the figure, the
mechanism is irrelevant. In the upper panel, the
tree that would be constructed from those
sequences is shown - although the plant obtained
its gene from a cyanobacterium, LGT makes it look
as though it came from Bacillus. As outlined in
the text, there is a fine line that separates
inferences drawn from phylogenetic data analysis
and the evolutionary process itself. Pinning down
the role of lateral gene transfer is a very tough
problem.
Rujan Martin Trends Genet 2001
Mar17(3)113-20.
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Evolution by Domains/Motifs Using MEME/MAST
programs
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Simple case SPO5.11
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SPO10.135
ancestor?
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P5.2096
P5.2063
ancestral part?
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References
Phylogeny programs  http//evolution.genetics.w
ashington.edu/phylip/software.html
MEGA http//www.megasoftware.net/
PAML http//abacus.gene.ucl.ac.uk/software/paml
.html
Books
Fundamental concepts of Bioinformatics. Dan E.
Krane and Michael L. Raymer
Genomes 2 edition. T.A. Brown
Molecular Evolution A phylogenetic
Approach Page, RDM and Holmes, EC Blackwell
Science
Manolo Gouy http//www.inra.fr/internet/Projets
/agroBI/PHYLO/Gouy.pdf