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Large scale genomes comparisons Bioinformatics
aspects (Introduction)
Fredj Tekaia Institut Pasteur tekaia_at_pasteur.fr

EMBO Bioinformatic and Comparative Genome
Analysis Course Institut Pasteur Paris June 27 -
July 9, 2011
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Starting from genomes (whole sequence, whole gene
sequences or whole protein sequences of given
species) what Large-scale Genome Comparisons
include?
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Large-scale genome comparisons Comparing a
genome (in terms of whole sequence, whole set of
predicted genes or whole set of predicted
proteins) to itself (intra-species comparisons)
or to another genome (inter-species comparisons).
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Plan Completely sequences genomes Large
scale genome comparisons Results mining
clusters of orthologs and analyses References.
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• Large scale genome comparisons
• -Duplication
• -Conservation
• -Specificity (species-specific genes, proteins)
• -Paralogues, orthologues
• -Families (clusters) of paralogues, of
  orthologues
• -Genomes organisations (duplicated, conserved
  genes)
• -Search for shared motifs in proteins of the same
  cluster
• Protein conservation profiles
• -Selection pressure analyses
• (synonymous, non synonymous substitutions,..),.

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Comparative genomics
Analysis and comparisons of genomes from
different species.  Helps understanding the
similarity and differences between genomes, their
evolution and the evolution of their genes.
Intra-genomic comparisons help understanding
the degree of duplication (genome regions genes)
and genes organization,...
Inter-genomic comparisons help understanding
the degree of similarity between genomes degree
of conservation between genes Determination of
syntenic regions i.e regions conserved in
different species
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Synteny
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Evolution
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Speciation - Duplication
Speciation Duplication Inparalogs Orthologs O
utparalogs Loss of genes
Predict these events by comparing genomes?
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Orthologs / Paralogs

• How to detect orthologous genes?
• - easy way best reciprocal hit (RBH)

1a
1b
2.1a
2.1b
2.2a
2.2b
3a
3b
Organism B
Organism A
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Orthologs / Paralogs
- more rigorous make a phylogenetic tree of
this gene family
1a
1b
2.1a
2.1b
2.2a
2.2b
3a
3b
- more rigorous look at synteny conservation
2.1b
3b
1b
2a
3a
1a
Organism B
2.2b
5b
4b
Organism A
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Large scale comparative analysis of predicted
proteomes revealed significant evolutionary
processes
Expansion, Exchange and Deletion.
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Gene duplications are traditionally considered as
a major evolutionary source for protein new
functions
Understanding how duplications happened and how
important is this evolutionary process is a key
goal of genome analysis
gt Some examples
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Colours reveal Duplications
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Kellis et al. Nature, 2004
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Nature Reviews Genetics 3 827-837
(2002) SPLITTING PAIRS THE DIVERGING FATES OF
DUPLICATED GENES
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Hurles M (2004) Gene Duplication The Genomic
Trade in Spare Parts. PLoS Biol 2(7) e206.
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Genome duplication. a, Distribution of Ks values
of duplicated genes in Tetraodon (left) and
Takifugu (right) genomes. Duplicated genes
broadly belong to two categories, depending on
their Ks value being below or higher than 0.35
substitutions per site since the divergence
between the two puffer fish (arrows). b, Global
distribution of ancient duplicated genes (Ks gt
0.35) in the Tetraodon genome. The 21 Tetraodon
chromosomes are represented in a circle in
numerical order and each line joins duplicated
genes at their respective position on a given
pair of chromosomes.
Jaillon et al. Nature 431, 946-857. 2004.
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Inter-genome Comparaisons base composition,
codons, amino acids,... degree of conservation
between genomes, orthologues determination,
families (clusters) of orthologues. gene
dictionary, gene conservation profiles,
genome trees construction, genomes multiple
alignments.
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Search for similarity
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Methods Important to know how algorithms that
allow sequence comparisons work, There are
many comparisons methods, Among most used
BLAST FASTA Smith-Waterman algorithm
dynamic programming method HMM (Hidden Markov
Model)
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Sequence Comparaisons
V I T K L G T C V G S V I T K L G T C V G S V I S
. . . T Q V G S V . S K . G T Q V . S
Identity Similarity Homology
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• Comparison of 2 sequences
• Aims at finding the optimal alignment the one
  that shows most similar regions and regions that
  are less similar.
• In describing sequence comparisons, three
  different terms are commonly used 
• Identity, Similarity and Homology.
• Need for a score that evaluates
• - matches
• - mismatches
• - gaps
• and a method that evaluates the numerous possible
  alignments.

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Identity Refers to the occurence of identical
nucleotides or amino acids in the same position
in aligned sequences  Identity is objective
and well defined Identity can be quantified
Percent i.e the number of identical matches
divided by the length of the aligned region.
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Similarity Sequence similarity takes
approximate matches into account, and is
meaningful only when such substitutions are
scored according to some measure of difference
with conservative substitutions assigned more
favorable scores than non-conservative ones
(substitution matrices). Given a
number of parameters (alphabet, scoring matrix,
filtering procedure, etc...), the similarity of
an aligned region is defined by a score
calculated on that region The score depends
on the chosen parameters Contrarily to
homology  expression like significant or weak
similarity are often used.
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Homology Sequence homology underlies  common
ancestry and sequence conservation Homology
can be inferred, under suitable conditions from
sequence similarity The main objective of
sequence similarity searching studies aims at
inferring homology between sequences Homology
is not a measure. It is an all or none
relashionship (i.e homology exits or does not
exist. Expressions like  significant or weak
homology are meaningless!). Sequence similarity
is a measure of the matching characters in an
alignment, whereas homology is a statement of
common evolutionary origin.
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Local Alignment
Global Alignment
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Compare one query sequence to a BLAST formatted
database
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Amino acid scoring schemes (substitution
matrices) All algorithms comparing protein
sequences rely on some schemes to score the
equivalence of each of the 210 possible pairs of
amino acids. As a result what a local
alignment program produces depends strongly upon
the scores it uses. implicitly a scheme may
represent a particular theory of evolution,
choice of a matrix can strongly influence the
outcome of an analysis. The scores in the matrix
are integer values which assign a positive score
to identical or similar character pairs, and a
negative value to dissimilar character pairs. Sij
(ln(qij/pipj))/?u qij are target frequencies
for aligned pairs of amino acids, the pi and pj
are background frequencies, and ?u is a
statistical parameter.
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Examples of substitution matrices PAM250
substitution matrix, scale ln(2)/3 0.231049
Expected score -0.844, Entropy 0.354 bits
Lowest score -8, Highest score 17 A R N
D C Q E G H I L K M F P S T W Y
V B Z X A 2 -2 0 0 -2 0 0 1 -1 -1 -2
-1 -1 -3 1 1 1 -6 -3 0 0 0 0 -8 R -2 6 0
-1 -4 1 -1 -3 2 -2 -3 3 0 -4 0 0 -1 2 -4
-2 -1 0 -1 -8 N 0 0 2 2 -4 1 1 0 2 -2 -3
1 -2 -3 0 1 0 -4 -2 -2 2 1 0 -8 D 0 -1 2
4 -5 2 3 1 1 -2 -4 0 -3 -6 -1 0 0 -7 -4
-2 3 3 -1 -8 C -2 -4 -4 -5 12 -5 -5 -3 -3 -2 -6
-5 -5 -4 -3 0 -2 -8 0 -2 -4 -5 -3 -8 Q 0 1 1
2 -5 4 2 -1 3 -2 -2 1 -1 -5 0 -1 -1 -5 -4
-2 1 3 -1 -8 E 0 -1 1 3 -5 2 4 0 1 -2 -3
0 -2 -5 -1 0 0 -7 -4 -2 3 3 -1 -8 G 1 -3 0
1 -3 -1 0 5 -2 -3 -4 -2 -3 -5 0 1 0 -7 -5
-1 0 0 -1 -8 H -1 2 2 1 -3 3 1 -2 6 -2 -2
0 -2 -2 0 -1 -1 -3 0 -2 1 2 -1 -8 I -1 -2 -2
-2 -2 -2 -2 -3 -2 5 2 -2 2 1 -2 -1 0 -5 -1
4 -2 -2 -1 -8 L -2 -3 -3 -4 -6 -2 -3 -4 -2 2 6
-3 4 2 -3 -3 -2 -2 -1 2 -3 -3 -1 -8 K -1 3 1
0 -5 1 0 -2 0 -2 -3 5 0 -5 -1 0 0 -3 -4
-2 1 0 -1 -8 M -1 0 -2 -3 -5 -1 -2 -3 -2 2 4
0 6 0 -2 -2 -1 -4 -2 2 -2 -2 -1 -8 F -3 -4 -3
-6 -4 -5 -5 -5 -2 1 2 -5 0 9 -5 -3 -3 0 7
-1 -4 -5 -2 -8 P 1 0 0 -1 -3 0 -1 0 0 -2 -3
-1 -2 -5 6 1 0 -6 -5 -1 -1 0 -1 -8 S 1 0 1
0 0 -1 0 1 -1 -1 -3 0 -2 -3 1 2 1 -2 -3
-1 0 0 0 -8 T 1 -1 0 0 -2 -1 0 0 -1 0 -2
0 -1 -3 0 1 3 -5 -3 0 0 -1 0 -8 W -6 2 -4
-7 -8 -5 -7 -7 -3 -5 -2 -3 -4 0 -6 -2 -5 17 0
-6 -5 -6 -4 -8 Y -3 -4 -2 -4 0 -4 -4 -5 0 -1 -1
-4 -2 7 -5 -3 -3 0 10 -2 -3 -4 -2 -8 V 0 -2 -2
-2 -2 -2 -2 -1 -2 4 2 -2 2 -1 -1 -1 0 -6 -2
4 -2 -2 -1 -8 B 0 -1 2 3 -4 1 3 0 1 -2 -3
1 -2 -4 -1 0 0 -5 -3 -2 3 2 -1 -8 Z 0 0 1
3 -5 3 3 0 2 -2 -3 0 -2 -5 0 0 -1 -6 -4 -2
2 3 -1 -8 X 0 -1 0 -1 -3 -1 -1 -1 -1 -1 -1 -1
-1 -2 -1 0 0 -4 -2 -1 -1 -1 -1 -8 -8 -8 -8 -8
-8 -8 -8 -8 -8 -8 -8 -8 -8 -8 -8 -8 -8 -8 -8 -8
-8 -8 -8 1
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BLOSUM62 Clustered Scoring Matrix in 1/2 Bit
Units Cluster Percentage gt 62 Lowest score
-4, Highest score 11 A R N D C Q E G
H I L K M F P S T W Y V B Z X A
4 -1 -2 -2 0 -1 -1 0 -2 -1 -1 -1 -1 -2 -1 1
0 -3 -2 0 -2 -1 0 -4 R -1 5 0 -2 -3 1 0 -2
0 -3 -2 2 -1 -3 -2 -1 -1 -3 -2 -3 -1 0 -1 -4 N
-2 0 6 1 -3 0 0 0 1 -3 -3 0 -2 -3 -2 1
0 -4 -2 -3 3 0 -1 -4 D -2 -2 1 6 -3 0 2 -1
-1 -3 -4 -1 -3 -3 -1 0 -1 -4 -3 -3 4 1 -1 -4 C
0 -3 -3 -3 9 -3 -4 -3 -3 -1 -1 -3 -1 -2 -3 -1
-1 -2 -2 -1 -3 -3 -2 -4 Q -1 1 0 0 -3 5 2 -2
0 -3 -2 1 0 -3 -1 0 -1 -2 -1 -2 0 3 -1 -4 E
-1 0 0 2 -4 2 5 -2 0 -3 -3 1 -2 -3 -1 0
-1 -3 -2 -2 1 4 -1 -4 G 0 -2 0 -1 -3 -2 -2 6
-2 -4 -4 -2 -3 -3 -2 0 -2 -2 -3 -3 -1 -2 -1 -4 H
-2 0 1 -1 -3 0 0 -2 8 -3 -3 -1 -2 -1 -2 -1
-2 -2 2 -3 0 0 -1 -4 I -1 -3 -3 -3 -1 -3 -3 -4
-3 4 2 -3 1 0 -3 -2 -1 -3 -1 3 -3 -3 -1 -4 L
-1 -2 -3 -4 -1 -2 -3 -4 -3 2 4 -2 2 0 -3 -2
-1 -2 -1 1 -4 -3 -1 -4 K -1 2 0 -1 -3 1 1 -2
-1 -3 -2 5 -1 -3 -1 0 -1 -3 -2 -2 0 1 -1 -4 M
-1 -1 -2 -3 -1 0 -2 -3 -2 1 2 -1 5 0 -2 -1
-1 -1 -1 1 -3 -1 -1 -4 F -2 -3 -3 -3 -2 -3 -3 -3
-1 0 0 -3 0 6 -4 -2 -2 1 3 -1 -3 -3 -1 -4 P
-1 -2 -2 -1 -3 -1 -1 -2 -2 -3 -3 -1 -2 -4 7 -1
-1 -4 -3 -2 -2 -1 -2 -4 S 1 -1 1 0 -1 0 0 0
-1 -2 -2 0 -1 -2 -1 4 1 -3 -2 -2 0 0 0 -4 T
0 -1 0 -1 -1 -1 -1 -2 -2 -1 -1 -1 -1 -2 -1 1
5 -2 -2 0 -1 -1 0 -4 W -3 -3 -4 -4 -2 -2 -3 -2
-2 -3 -2 -3 -1 1 -4 -3 -2 11 2 -3 -4 -3 -2 -4 Y
-2 -2 -2 -3 -2 -1 -2 -3 2 -1 -1 -2 -1 3 -3 -2
-2 2 7 -1 -3 -2 -1 -4 V 0 -3 -3 -3 -1 -2 -2 -3
-3 3 1 -2 1 -1 -2 -2 0 -3 -1 4 -3 -2 -1 -4 B
-2 -1 3 4 -3 0 1 -1 0 -3 -4 0 -3 -3 -2 0
-1 -4 -3 -3 4 1 -1 -4 Z -1 0 0 1 -3 3 4 -2
0 -3 -3 1 -1 -3 -1 0 -1 -3 -2 -2 1 4 -1 -4 X
0 -1 -1 -1 -2 -1 -1 -1 -1 -1 -1 -1 -1 -1 -2 0
0 -2 -1 -1 -1 -1 -1 -4 -4 -4 -4 -4 -4 -4 -4 -4
-4 -4 -4 -4 -4 -4 -4 -4 -4 -4 -4 -4 -4 -4 -4 1
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PAM matrices (Dayhoff et al. (1978)) PAM stands
for point accepted mutation. 1 PAM
corresponds to 1 amino acid change per 100
residues, 1 PAM 1 divergence, Extrapolate
to predict patterns at longer distances. Assumptio
ns  replacements are independent of
surrounding residues, sequences being
compared are of average composition, all sites
are equally mutable, Source of error  small,
globular proteins were used to derive PAM
matrices (departure from average
composition) errors in PAM1 are magnified up
to PAM250,.... does not account for conserved
blocks or motifs. Strategy  PAM40 short
alignments, highly similar PAM120 average
similarity PAM250 longer , weaker local
alignments.
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• Position Specific Scoring Matrix (PSSM)
• Conserved motifs are identified and amino acid
  profile matrix for each motif is calculated.
• This matrix (n x 20 aa ) is representative of the
  relative amino acid probabilities at specific
  positions and is characteristic of a protein
  family.
• -Such matrices are used by the profile database
  searching programs (including PSI-BLAST and HMM
  based programs).

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Example of a PSSM matrices determined (PSI-BLAST
program) A R N D C Q E G
H I L K M F P S T W Y V 1 M -1
-1 -2 -3 -1 0 -2 -3 -2 1 2 -1 5 0 -2 -1 -1
-1 -1 1 2 S 1 -1 1 0 -1 0 0 0 -1 -2
-2 0 -1 -2 -1 4 1 -3 -2 -2 3 S 1 -1 1
0 -1 0 0 0 -1 -2 -2 0 -1 -2 -1 4 1 -3 -2
-2 4 S 1 -1 1 0 -1 0 0 0 -1 -2 -2 0
-1 -2 -1 4 1 -3 -2 -2 5 S 1 -1 1 0 -1
0 0 0 -1 -2 -2 0 -1 -2 -1 4 1 -3 -2 -2
6 G 0 -2 0 -1 -3 -2 -2 6 -2 -4 -4 -2 -3 -3
-2 0 -2 -2 -3 -3 7 L -1 -2 -3 -4 -1 -2 -3
-4 -3 2 4 -2 2 0 -3 -2 -1 -2 -1 1 8 K
-1 2 0 -1 -3 1 1 -2 -1 -3 -2 5 -1 -3 -1 0
-1 -3 -2 -2 9 Q -1 1 0 0 -3 5 2 -2 0
-3 -2 1 0 -3 -1 0 -1 -2 -1 -2 10 Q -1 1
0 0 -3 5 2 -2 0 -3 -2 1 0 -3 -1 0 -1 -2
-1 -2 11 G 0 -2 0 -1 -2 -2 -2 6 -2 -4 -4
-2 -3 -3 -2 0 -2 -2 -3 -3 12 L -1 -2 -3 -4
-1 -2 -3 -4 -3 2 4 -2 2 0 -3 -2 -1 -2 -1 1
13 A 4 -1 -2 -2 0 -1 -1 0 -2 -1 -1 -1 -1
-2 -1 1 0 -3 -2 0 14 Q -1 1 0 0 -3 5
2 -2 0 -3 -2 1 0 -3 -1 0 -1 -2 -1 -2 15
K -1 2 0 -1 -3 1 1 -2 -1 -3 -2 5 -1 -3 -1
0 -1 -3 -2 -2 16 K -1 2 0 -1 -3 1 1 -2
-1 -3 -2 5 -1 -3 -1 0 -1 -3 -2 -2 17 K -1
2 0 -1 -3 1 1 -2 -1 -3 -2 5 -1 -3 -1 0 -1
-3 -2 -2 18 F -2 -3 -3 -3 -2 -3 -3 -3 -1 0
0 -3 0 6 -4 -2 -2 1 3 -1 19 Q -1 1 0
0 -3 5 3 -2 0 -3 -2 1 0 -3 -1 0 -1 -2 -1
-2 20 L -1 -2 -3 -4 -1 -2 -3 -4 -3 2 4 -2
2 0 -3 -2 -1 -2 -1 1 21 E -1 0 0 2 -4
2 5 -2 0 -3 -3 1 -2 -3 -1 0 -1 -3 -2 -2
22 F -2 -3 -3 -3 -2 -3 -3 -3 -1 0 0 -3 0 6
-4 -2 -2 1 3 -1 23 D -2 -2 1 6 -3 0 2
-1 -1 -3 -4 -1 -3 -3 -1 0 -1 -4 -3 -3
.................................................
.................... 573 I -1 -3 -3 -3 -1 -3
-3 -4 -3 4 2 -3 1 0 -3 -2 -1 -3 -1 3 574
P -1 -2 -2 -1 -3 -1 -1 -2 -2 -3 -3 -1 -2 -4 7
-1 -1 -4 -3 -2 575 L -1 -2 -3 -4 -1 -2 -3 -4
-3 2 4 -2 2 0 -3 -2 -1 -2 -1 1
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(2) Compare the word list to the database and
identify exact matches.
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E-values Statistics of HSP scores are
characterized by two parameters, K and ?. The
expected number of HSPs with score at least S is
given by E Kmne-?S (Karlin Altschul,1990). m
and n are sequence lengths. E is the E-value
for the score S. Bit scores S (?S
lnK)/ln2 The E-value corresponding to a given
bit score is  E mn2-S. (note
mn). P-values The probability of finding
exactly a HSPs with score gt S is given by  P(a)
e-E.Ea/a! (Poisson distribution), where E is
the E-value of S given by the above equation.
Finding zero HSP with score gtS is P(0) e-E,
so the probability of finding at least one such
HSP is  P 1 - e-E.
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Large-scale proteome comparisons
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The expected number of HSPs with score at least S
is given by E Kmne-?S. m and n are sequence
and database lengths.
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Systematic Analysis of Completely Sequenced
Organisms
In silico species specific comparisons
Degree of ancestral duplication and of
ancestral conservation between pairs of species
Families of paralogs (Partition-MCL)
Families of orthologs (Partition-MCL)
Determination of the protein dictionary
(orthologs)
Determination of protein conservation profiles
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Orthologs - inparalogs - outparalogs
Sequence similarities between out-paralogs
should be larger than those between orthologs and
in-paralogs Orthology assignments are
consistent among several genome pairs
Orthologues are present in syntenic order
Kuzniar A, van Ham RC, Pongor S, Leunissen JA.
(2008). The quest for orthologs finding the
corresponding gene across genomes.Trends Genet.
24(11)539-51. Review.
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Altenhoff AM, Dessimoz C. (2009). Phylogenetic
and functional assessment of orthologs inference
projects and methods. PLoS Comput Biol.
5(1)e1000262. Kuzniar A, van Ham RC, Pongor
S, Leunissen JA. (2008). The quest for orthologs
finding the corresponding gene across
genomes.Trends Genet. 24(11)539-51. Review.
Gabaldon T. (2008). Large-scale assignment of
orthology back to phylogenetics?Genome Biol.
9(10)235. Moreno-Hagelsoeb G, Latimer K.
(2008). Choosing BLAST options for better
detection of orthologs as reciprocal best hits.
Bioinformatics. 3 319-324. Chen F, Mackey AJ,
Vermunt JK, Roos DS (2007). Assessing performance
of orthology detection strategies applied to
eukaryotic genomes. PLoS ONE. 2e383.
Goodstadt L, Ponting CP (2006). Phylogenetic
reconstruction of orthology, paralogy, and
conserved synteny for dog and human. PLoS Comput
Biol. 2e133.
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Working Examples
Comparing S. cerevisiae (SC) genome with C.
elegans (CE) genome
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SC vs SC
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SC/CE
CE/SC
Reciprocal Best Hits (RBH)
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Conclusion
Large-scale analyses of Completely sequenced
genomes allow a systematic vision of genes,
genome organization and their macro as well their
micro evolutions. Starting step for further
evolutionary analyses that will be dealt with
during this course.
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Practical sessions (see text)