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Summer Bioinformatics Workshop 2008
Comparative Genomics and
Phylogenetics
Chi-Cheng Lin, Ph.D., Professor
Department of Computer Science
Winona State University – Rochester Center
[email protected]
Summer Bioinformatics Workshop 2008
Outline
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Comparative Genomics
Phylogenetics
Phylogenetic Tree
Phylgenetics Applications
Gene Tree vs. Species Tree
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Summer Bioinformatics Workshop 2008
Comparative Genomics
• Analysis and comparison of genomes from
different species
• Purposes
– to gain a better understanding of how species have
evolved
– to determine the function of genes and non-coding
regions of the genome
• The functions of human genes and other DNA
regions often are revealed by studying their
parallels in nonhumans.
– Researchers have learned a great deal about the
function of human genes by examining their
counterparts in simpler model organisms such as the
mouse.
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Summer Bioinformatics Workshop 2008
Comparative Genomics
• Features looked at when comparing genomes:
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sequence similarity
gene location
length and number of coding regions within genes
amount of non-coding DNA in each genome
highly conserved regions maintained in organisms
• Computer programs that can line up multiple
genomes and look for regions of similarity
among them are used.
• Many of these sequence-similarity tools, such as
BLAST, are accessible to the public over the
Internet.
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Summer Bioinformatics Workshop 2008
Of Mice and Men
• The full complement of human
chromosomes can be cut into about
150 pieces, then reassembled into a
reasonable approximation of the
mouse genome.
• The colors of the mouse
chromosomes and the numbers
alongside indicate the human
chromosomes containing
homologous segments.
• This piecewise similarity between the
mouse and human genomes means
that insights into mouse genetics are
likely to illuminate human genetics
as well.
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Source: http://www.ornl.gov/sci/techresources/Human_Genome/publicat/tko/06_img.html
Summer Bioinformatics Workshop 2008
Phylogenetics
• Phylogenetics
– Study of evolutionary relationships
(sequences / species)
– Infer evolutionary relationship from
shared features
• Phylogeny
– Relationship between organisms
with common ancestor
• Phylogenetic tree
– Graph representing evolutionary
history of sequences / species
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Source of image: http://superfrenchie.com/Pics/Blog/culture/evolution.jpg
Summer Bioinformatics Workshop 2008
Phylogenetics
• Premise
– Members sharing common evolutionary history
(i.e., common ancestor) are more related to each
other
– Can infer evolutionary relationship from shared
features
• Long history of phylogenetics
– Historically - based on analysis of observable features
(e.g., morphology, behavior, geographical distribution)
– Now - mostly analysis of DNA / RNA / amino acid
sequences
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Summer Bioinformatics Workshop 2008
Phylogenetics
• Goals
– Understand relationship of sequence to similar sequences
– Construct phylogenetic tree representing evolutionary history
• Motivation / application
– Identify closely related families
• Use phylogenetic relationships to predict gene function
– Follow changes in rapidly evolving species (e.g., viruses)
• Analysis can reveal which genes are under selection
• Provide epidemiology for tracking infections & vectors
• Relationship to multiple sequence alignment (MSA)
– Alignment of sequences should take evolution into account
– More precise phylogenetic relationships  Improved MSA
– CLUTALW (http://www.ebi.ac.uk/clustalw/), a popular MSA
program, can produce alignment that is then used to build
phylogenetic tree.
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Summer Bioinformatics Workshop 2008
Phylogenetic Tree Terminology
• Leaf / terminal node / taxon
– Node with no children
– Original sequence
• Join / internal node
– Point of joining two leaves / clusters
– Inferred common ancestor
• Branches
– Represent change
– Length represents evolutionary distance
• Cluster / clade
– All sequences in subtree with common
ancestor (treated as single node)
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Summer Bioinformatics Workshop 2008
Phylogenetic Tree Terminology
• Binary tree
– Each edge that splits must connect to two children
• Rooted tree
– Contains a single ancestor of all nodes
– Evolution proceeds from root to leaves of tree
• Unrooted tree
– No single ancestor node
– No direction of evolution
• Molecular clock assumption (rooted tree)
– Mutations occur at constant rate
– Distance from root to leaves same for each leaf
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Summer Bioinformatics Workshop 2008
Rooted and Unrooted Trees
Rooted Tree
Unrooted Tree
Orangutan
Orangutan
Human
Human
Chimpanzee
Chimpanzee
Gorilla
Direction of evolution
Gorilla
Root
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Summer Bioinformatics Workshop 2008
Possible Ways of Drawing Tree
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Summer Bioinformatics Workshop 2008
Applications –
Building Tree of
Life
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Summer Bioinformatics Workshop 2008
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Source: http://gi.cebitec.uni-bielefeld.de/people/boecker/bilder/tree_of_life_new.gif
Summer Bioinformatics Workshop 2008
Applications – Mammal Systematics
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Source: http://www.isem.univ-montp2.fr/PPP/PM/RES/Phylo/Mamm/PHYLMOL-Placentalia%7EEnglish.jpg
Summer Bioinformatics Workshop 2008
Application –
Epidemiology (CSI!)
• Which patients
are more likely
infected by the
dentist?
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Source: http://trc.ucdavis.edu/djbegun/Lect_12.1.html
Summer Bioinformatics Workshop 2008
Application – Modern
Human Evolution
• Based on mtDNA
genome
• Example
– Global mtDNA diversity
analysis (Ingman et al.,
2000 Nature. Volume
408:708-713)
– Africans have twice as
much diversity among them
as do non-Africans 
Africans have a longer
genetic history
– More recent population
expansion for non-Africans
– Africans and non-Africans
diverged recently
 Out of Africa
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Source of image: Ingman et al., 2000, Nature. Volume 408: 708-713
Summer Bioinformatics Workshop 2008
Gene Tree vs.
Species Tree
• Gene typically
diverges before
speciation
• Phylogenetic tree
based on divergence
of one single
homologous gene
– Evolutionary history of
gene
– Gene tree rather than
species tree
• More genes are
needed to build
species trees
Source of image: http://www.bioinf2.leeds.ac.uk/b/genomics.html
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