Predicting interactions between genes based on genome Sequence comparisons The

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Predicting interactions between genes based on
genome Sequence comparisonsThe genomic
context component of STRINGBioinformatics
seminar series5-10-2004Berend Snel
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To do

• Seminar (today) please ask questions
• Article a gene co-expression network for global
  discovery of conserved genetic modules
• Make schedule for article discussion (today)
• Read article (next couple of days)
• 5 minute discussion per person of the article
  (Preferentially Monday 11 October)

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http//string.embl.de
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Contents

• Predicting functional interactions between
  proteins
• Genomic context methods
• General
• Gene fusion
• Gene order
• Presence / absence of genes across genomes
• Integration and benchmarking of predictions
• Interaction networks
• In addition to genomic context functional
  genomics data

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Complete genomes, now what?

• Post-genomic era we have the parts list
  (complete genomes)
• to understand the cell we need to know the
  functions of the genes

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For most genes in any genome we need function
prediction

• E. Coli, the most intensively studied organism
• only 1924 genes (43) have been (partially)
  experimentally characterized.

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Predicting protein function
What is function ? Various levels of
description Sequence similarity/homology has
the largest relevance for Molecular Function.
This aspect of protein function is best
conserved. Molecular function can often be
predicted from similarities between protein
sequences (BLAST), or structures.
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BLAST
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Beyond homology and molecular function

• Homolgy based function prediction works very
  well, but
• a large fraction of genes are poorly described
  (no homologs, uncharacterized homologs this
  holds for 60 of the human genes)
• There are other aspects of function functional
  associations, e.g. the target of a protein kinase
  or a transcriptional regulator
• Thus predicting these associations

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• Genome sequences
• Allowing us to interpret the function of proteins
  within the context in which they occur
• Reverse this process predict the function of a
  protein from the context in which it tends to
  occur ? prediction of protein function/pathways
  from genome sequences Use the genome sequences
  (through comparative genome analysis) for
  interaction prediction genomic context methods
• Genomic context methods have been shown to be
  reliable indicators for functional associations

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There are many types of functional associations
(AKA functional interactions, interactions,
functional links, functional relations) in
molecular biology
Cellular process
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Types of functional associations
metabolic pathways filling gaps
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Types of functional associations
Transcription regulation
Signalling pathways
P
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Types of functional associations
Cellular process
Protein complexes
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Contents

• Predicting functional interactions between
  proteins
• Genomic context methods
• General
• Gene fusion
• Gene order
• Presence / absence of genes across genomes
• Integration and benchmarking of predictions
• Interaction networks
• In addition to genomic context functional
  genomics data

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Genomic context is an tool to predict functional
associations between genes

• Use the genome sequences (through comparative
  genome analysis) for interaction prediction
  genomic context methods
• Genomic context methods have been shown to be
  reliable indicators for functional interaction
• Genomic context is also known as in silico
  interaction prediction, or genomic associations

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Genomic context methods detect evolutionary
traces in genomes of functionally associated
proteins
trpA
trpB
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Three different genomic context methods in STRING

• Gene fusion, Rosetta stone method
• Conserved gene order between divergent genomes
• Co-occurrence of genes across genomes,
  phylogenetic profiles

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All genomic context methods use orthologs
corresponding genes between genomes

• Orthologs not just homologs related by
  speciation
• Orthologs are very likely to have the same
  function
• orthologs genomes alignment sequence

Gene Duplication
Speciation
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Contents

• Predicting functional interactions between
  proteins
• Genomic context methods
• General
• Gene fusion
• Gene order
• Presence / absence of genes across genomes
• Integration and benchmarking of predictions
• Interaction networks
• In addition to genomic context functional
  genomics data

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Gene fusion

• i.e. the orthologs of two genes in another
  organism are fused into one polypeptide
• A very reliable indicator for functional
  interaction partly because it is an relatively
  infrequent evolutionary event 3470 distinct
  fusions when surveying 179 genomes

Fusion
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Gene fusion an example
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Contents

• Predicting functional interactions between
  proteins
• Genomic context methods
• General
• Fusion
• Gene order
• Presence / absence of genes across genomes
• Integration and benchmarking of predictions
• Interaction networks
• In addition to genomic context functional
  genomics data

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Gene order evolves rapidly
But
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Differential retention of divergent / convergent
gene pairs suggests that conservation implies a
functional association
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Comparison to pathways conservation implies a
functional association
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Conserved gene order

• i.e. genes that are present over sufficiently
  large evolutionary distances in the same gene
  cluster
• Contributes by far the most predictions

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Conserved gene order
NB1 predicting operons is not trivial in fact
conserved gene order or functional association is
a major clue NB2 using only operons without
requiring conservation results in much less
reliable function prediction
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Conserved gene order an example from metabolism
of propionyl-CoA
target
query
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Conserved gene order an example from metabolism
of propionyl-CoA
Biochemical assays confirm the function of
members of COG0346 as a DL-methylmalonyl-CoA
racemase
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Contents

• Predicting functional interactions between
  proteins
• Genomic context methods
• General
• Gene fusion
• Gene order
• Presence / absence of genes across genomes
• Integration and benchmarking of predictions
• Interaction networks
• In addition to genomic context functional
  genomics data

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Presence / absence of genes
Gene content ? co-evolution. (The easy case, few
genomes. )
Differences between gene Content reflect
differences in Phenotypic potentialities
Genomes share genes for phenotypes they have in
common
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Presence / absence of genes
L. innocua (non-pathogen)
L. monocytogenes (pathogen)
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Presence / absence of genes
Genes involved in pathogenecity
L. monocytogenes (pathogenic)
L. innocua (non-pathogenic)
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Generalization phylogenetic profiles /
co-occurence
species 1 species 2 species 3 species 4
species 5 ...... ... .. ..
Gene 1 Gene 2 Gene 3 ....
species 1 species 2 species 3 species 4
species 5 ...... ... .. ..
Gene 1 1 0 1 1 0 1
Gene 2 1 1 0 0 1
0 Gene 3 0 1 0 0 1
0 ....
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but phylogenetic signal in gene content!
Escherichia coli
Haemophilus influenzae
\s sp1 sp2 sp3 sp4 sp1 \1 0.2 0.4
0.2 sp2 \1 0.9 0.1 sp3
\1 0.3 sp4 \1

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Co-occurrence of genes across genomes

• i.e. two genes have the same presence/ absence
  pattern over multiple genomes they have
  co-evolved
• AKA phylogenetic profiles

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Predicting function of a disease gene protein
with unknown function, frataxin, using
co-occurrence of genes across genomes

• Friedreichs ataxia
• No (homolog with) known function

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Frataxin has co-evolved with hscA and hscB
indicating that it plays a role in iron-sulfur
cluster assembly
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cyaY Yfh1
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Iron-Sulfur (2Fe-2S) cluster in the Rieske protein
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Prediction
Confirmation
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The opposite of co-occurrenceanti-correlation /
complementary patterns predicting analogous
enzymes
Genes with complementary phylogenetic profiles
tend to have a similar biochemical function.
A
B
A
B
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Complementary patterns in thiamin biosynthesis
predict analogous enzymes
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Prediction of analogous enzymes is confirmed
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Contents

• Predicting functional interactions between
  proteins
• Genomic context methods
• General
• Gene fusion
• Gene order
• Presence / absence of genes across genomes
• Integration and benchmarking of predictions
• Interaction networks
• In addition to genomic context functional
  genomics data

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Benchmark and integration KEGG maps
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Integrating genomic context scores into one
single score

• Compare each individual method against an
  independent benchmark (KEGG), and find
  equivalency
• Multiply the chances that two proteins are not
  interacting and subtract from 1 naive bayesian
  i.e. assuming independence

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0.8
0.6
Fraction same KEGG map
0.4
Fusion
Gene Order
0.2
Co-occurrence
0
0
0.2
0.4
0.6
0.8
1
Score
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Benchmark
100000
10000
1000
Coverage (number of predicted links between
orthologous groups)
Integrated
Gene Order (norm.)
Gene Order (abs.)
100
Cooccurrence
Fusion (norm.)
Fusion (abs.)
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0.5
0.6
0.7
0.8
0.9
1.0
Accuracy (fraction of confirmed predictions,
i.e. same KEGG map)
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Performance of genomic context compared to
high-throughput interaction data
purified complexes TAP
Purified Complexes HMS-PCI
genomic context
mRNA co-expression
two methods
synthetic lethality
Coverage
combined evidence
fraction of reference set covered by data
yeast two-hybrid
three methods
raw data
filtered data
parameter choices
Accuracy
fraction of data confirmed by reference set
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Genomic context biochemistry by other means
Despite the high performance of genomic context
methods, as a tool for function prediction it is
not a button press method It is more like
biochemistry by other means. Often quite a lot
of manual input and expert knowledge from the
researcher is needed to distill associations into
a concrete function prediction Small-scale
bioinformatics?
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Contents

• Predicting functional interactions between
  proteins
• Genomic context methods
• General
• Fusion
• Gene order
• Co-occurrence across genomes
• Integration and benchmarking of predictions
• Interaction networks
• In addition to genomic context functional
  genomics data

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STRING allows a network view
e.g. see not only to which genes the query gene
has an association, but also what the relations
are among these other genes
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STRING
Network output (depth1)
Assigning
uncharacterized archeal proteins
to a network around
Archeal flagellins
Archeal flagellin biosynth. ATPase
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STRING
Type IV secretion pathway
Network (depth2)
Connecting associated cellular processes
Archeal flagellins
Archeal flagella components
Chemotaxis- related
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STRING
Network (depth3)
Zooming out to other cellular processes
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Using the local network to detect
multi-functional proteins
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Contents

• Predicting functional interactions between
  proteins
• Genomic context methods
• General
• Fusion
• Gene order
• Co-occurrence across genomes
• Integration and benchmarking of predictions
• Interaction networks
• In addition to genomic context functional
  genomics data

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• STRING currently in addition includes
• Functional association data from large scale /
  high-throughput biochemical experiments
  (functional genomics data)
• protein complex purification
• yeast-2-hybrid
• ChIP-on-chip
• micro-array gene expression
• known functional relations, so called legacy
  data, as present in PubMed abstracts and
  databases like MIPS or KEGG.

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