Genome analysis.

1
Genome analysis.

• Genome the sum of genes and intergenic
  sequences of a haploid cell.

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The value of genome sequences lies in their
annotation

• Annotation Characterizing genomic features
  using computational and experimental methods
• Genes Four levels of annotation
• Gene Prediction Where are genes?
• What do they look like?
• What do they encode?
• What proteins/pathways involved in?

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Koonin Galperin
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Accuracy of genome annotation.

• In most genomes functional predictions has been
  made for majority of genes 54-79.
• The source of errors in annotation
• - overprediction (those hits which are
  statistically significant in the database search
  are not checked)
• - multidomain protein (found the
  similarity to only one domain, although the
  annotation is extended to the whole protein).
• The error of the genome annotation can be as big
  as 25.

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Sample genomes
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So much DNA so few genes
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Human Genome project.
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Comparative genomics - comparison of gene number,
gene content and gene location in genomes..
Campbell Heyer Genomics
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Analysis of gene order (synteny).

• Genes with a related function are frequently
  clustered on the chromosome.
• Ex E.coli genes responsible for synthesis of Trp
  are clustered and order is conserved between
  different bacterial species.
• Operon set of genes transcribed simultaneously
  with the same direction of transcription

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Analysis of gene order (synteny).
Koonin Galperin Sequence, Evolution, Function
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Analysis of gene order (synteny).

• The order of genes is not very well conserved if
  identity between prokaryotic genomes is less
  than 50
• The gene neighborhood can be conserved so that
  all neighboring genes belong to the same
  functional class.
• Functional prediction can be based on gene
  neighboring.

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Role of junk DNA in a cell.

• There is almost no correlation between the number
  of genes and organisms complexity.
• There is a correlation between the amount of
  nonprotein-coding DNA and complexity.

13
New interpretation of introns.

• Modern introns envaded eukaryotes late in
  evolution, they are derived from self-splicing
  mobile genetic elements similar to group II
  introns.
• Nucleus which separates transcription and
  translation, appears only in eukaryotes. For
  prokaryotes there would not be time for introns
  to splice themselves out.
• Hypothesis important regulatory role of introns.

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Regulatory role of non-coding regions.

• Micro-RNAs control timing of processes in
  development and apoptosis.
• Introns RNAs inform about the transcription of a
  particular gene.
• Alternative splicing can be regulated by
  non-coding regions.
• Non-coding regions can be very well conserved
  between the species and many genetic deseases
  have been linked to variations/mutations in
  non-coding regions.

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COGs Clusters of Orthologous Genes.

• Orthologs genes in different species that
  evolved from a common ancestral gene by
  speciation
• Paralogs paralogs are genes related by
  duplication within a genome.

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Classwork I Comparing microbial genomes.

• Go to http//www.ncbi.nlm.nih.gov/genomes/lproks.c
  gi
• Select Thermus thermophilus genome
• View TaxTable
• What gene clusters do you see which are common
  with Archaea?

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Systems biology.

• Integrative approach to study the relationships
  and interactions between various parts of a
  complex system.
• Goal to develop a model of interacting
  components for the whole system.

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Basic notions of networks.

• Network (graph) a set of vertices connected via
  edges.
• The degree of a vertex the total number of
  connections of a vertex.
• Random networks networks with a disordered
  arrangement of edges.

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Properties of networks.

• Vertex degree distribution/connectivity.
• Clustering coefficient.
• Network diameter.

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Characteristics of networks vertex degree
distribution.
K2
K2
K3
K1
P(k,N) degree distribution, k - degree of the
vertex, N - number of vertices. If vertices are
statistically independent and connections are
random, the degree distribution completely
determines the statistical properties of a
network.
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Characteristics of networks vertex degree
distribution.
22
Characteristics of networks clustering
coefficient.

• The clustering coefficient characterizes the
  density of connections in the environment close
  to a given vertex.

d total number of edges connecting nearest
neighbors n number of nearest verteces for a
given vertex
C 2/6
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Characteristics of networks diameter,
small-world.

• Diameter of a network shortest path along the
  existing links averaged over all pairs of
  verteces. Distance between two verteces the
  smallest number of steps one can take to reach on
  vertex from another.
• Small-world character of the networks any two
  verteces can be connected by relatively short
  paths.
• For random networks the diameter increases
  logarithmically with the addition of new verteces.

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Different network modelsErdos-Renyi model.

• Start with the fixed set of vertices.
• Iterate the following process
• Chose randomly two vertices and connect them
  by an edge.
• Stop at certain number of edges.

ln(P(k))
Degree distribution Poisson distribution, ?
average degree
ln( k )
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Different network models model 2.

• At each step, a new vertex is added to the graph
• Simultaneously, a pair of randomly chosen
  vertices is connected by an edge.
• This is a non-equilibrium model the total
  number of vertices is not fixed.

ln(P(k))
Degree distribution exponential distribution.
ln(k)
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Different network models Barabasi-Alberts.

• Model of preferential attachment.
• At each step, a new vertex is added to the graph
• The new vertex is attached to one of old vertices
  with probability proportional to the degree of
  that old vertex.

ln(P(k))
Degree distribution power law distribution.
ln(k)
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Power Law distribution
Multiplying k by a constant, does not change the
shape of the distribution scale free
distribution.
From T. Przytycka
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Difference between scale-free and random networks.
Random networks are homogeneous, most nodes have
the same number of links. Scale-free networks
have a few highly connected verteces.
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Example 1 the large-scale organization of
metabolic networks.
Glycolysis metabolic network
enzymes
subsbstrate
Slide credit Hagai Ginsburg
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Example 1 the large-scale organization of
metabolic networks.

• Jeong et al, Nature, 2000
• Compared metabolic networks of 43 organisms.
• Verteces substrates connected with each other
  through links/metabolic reactions.

Results - Scale-free nature of metabolic
networks for all organisms, ? 2.2 - Diameters
of metabolic networks for all organisms are the
same.
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Biological interpretations of power-law
connectivity.

• Few verteces dominate the overall connectivity of
  network.
• Self-similarity of networks.
• Small diameter, respond quickly to a mutation
  which can destroy an enzyme, activate different
  paths quickly.

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Protein-protein interaction networks.

• Sneppen Maslov
• Verteces proteins, edges connect those proteins
  which interact in a cell
• Network 3278 interactions,1289 proteins
• Scale free network, g 2.5 /- 0.3

Sneppen Maslov