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5th Annual Cytoscape Symposium
Amsterdam Medical Center
November 8 2007
Large-scale interaction technologies
PHYSICAL
Protein-gene
(transcriptional, ChIP-Chip22, 39)
ORDERED
Cause and effect
Signal transducing
Protein-RNA (RIP-chip80)
Protein-protein
(kinase-substrate
arrays21, LUMIER81)
GENETIC
Epistatic orderings
a < b OR b < a
(EMAP28, 83)
Knock-down expression profiles
(RNAi32, deletion mutants36, 37)
Expression QTLs41, 42
Protein-compound82
UNORDERED
Ambiguous
directionality
Protein-protein
(co-IP/MS/MS18-20, Y2H15, 84-86)
Gene-gene
(co-regulon87)
Synthetic lethality
ab << a, b, wt
(SGA88, dSLAM31, 71, EMAP28, 83,
chemogenomic profiling89)
Beyer, Bandyopadhyay, and Ideker Nat. Rev. Genetics (2007)
Like sequence, protein interaction data
are exponentially growing…
EMBL Database Growth
DIP Database Growth
total nucleotides (gigabases)
total interactions
90,000
80,000
10
70,000
60,000
50,000
40,000
5
30,000
20,000
10,000
0
1980
0
1990
2000
2000
2001
2002
2003
2004
2005
www.cytoscape.org
OPEN SOURCE Java platform for
integration of systems biology data
•Layout and query of interaction
networks (physical and genetic)
•Visual and programmatic integration
of molecular state data (attributes)
•The ultimate goal is to provide the
tools to facilitate all aspects of
pathway assembly and annotation.
RECENT NEWS
•Version 2.5 released Summer 2007;
Scalability+efficiency now equivalent
to best commercial packages
•The Cytoscape Consortium is a 501(c)3 non-for-profit in the State of California
•The Cytoscape ® Registered Trademark awarded
JOINTLY CODED with Agilent, ISB, Pasteur, Sloan-Kettering, UCSF, Unilever, U Toronto
Thinking about the parallels between
processing of genomes and interactomes
Beyer, Bandyopadhyay, and Ideker Nat. Rev. Genetics (2007).
Integration of genetic and physical maps:
From the Genome (a) to the Interactome (b)
http://gai.nci.nih.gov/
Kelley and Ideker, Nat. Biotech (2005)
Gaining power in
gene association
studies with
Cytoscape
Trey Ideker
University of California San Diego
MAPPING DISEASE GENES
• Gene association studies measure the
association between a disease trait (e.g. diabetic
/ non-diabetic) and a panel of SNPs (or other
polymorphic genetic markers) distributed across
the genome
• The goal is to identify SNPs (or haplotypes) that
correlate with incidence of the disease
• The phenotypic trait can also be quantitative,
e.g. blood pressure, age, weight, body fat index
• These are so-called Quantitative Trait Loci
• Expression Quantitative
Trait Loci (eQTLs) look for
associations between
SNPs and gene
expression levels.
• All-vs-All analysis: ALL
SNPs are evaluated for
association with ALL gene
expression levels.
• This process can
generate thousands of
associations.
Genes
EXPRESSION AS A TRAIT
SNPs
MANY CHALLENGES
•
•
•
Fine Mapping: Due to the spacing of genetic markers
and/or linkage disequilibrium, several genes can reside
near each SNP marker. Typically, only one of these
genes is responsible for the observed expression
phenotype. Identifying the true causative gene
requires additional data, since all genes at a locus are
indistinguishable based on the eQTL data alone.
Lack of mechanistic explanation: A gene-phenotype
association typically lends little insight into the
underlying molecular mechanism.
Lack of statistical power: Many real gene-phenotype
associations may have only weak association signals.
But boosting the signal involves genotyping prohibitive
numbers of individuals.
Cause and effect interactions
Epistatic orderings
(SGA / EMAP)
Knock-down expression profiles
(RNAi, deletion mutants)
Expression QTLs
Knockout causes up-regulation
Knockout causes down-regulation
NETWORK INFERENCE
Kulp DC, Jagalur M (2006) Causal inference of regulator-target pairs by gene
mapping of expression phenotypes. BMC genomics 7: 125.
Lee SI, Pe'er D, Dudley AM, Church GM, Koller D (2006) Identifying regulatory
mechanisms using individual variation reveals key role for chromatin modification.
Proceedings of the National Academy of Sciences of the United States of America
103: 14062-14067.
Schadt EE, Lamb J, Yang X, Zhu J, Edwards S, Guhathakurta D, Sieberts SK,
Monks S, Reitman M, Zhang C, Lum PY, Leonardson A, Thieringer R, Metzger JM,
Yang L, Castle J, Zhu H, Kash SF, Drake TA, Sachs A, Lusis AJ (2005) An
integrative genomics approach to infer causal associations between gene
expression and disease. Nature genetics 37: 710-717.
Tu Z, Wang L, Arbeitman MN, Chen T, Sun F (2006) An integrative approach for
causal gene identification and gene regulatory pathway inference. Bioinformatics
(Oxford, England) 22: e489-496.
Large-scale interaction technologies
PHYSICAL
Protein-gene
(transcriptional, ChIP-Chip22, 39)
ORDERED
Cause and effect
Signal transducing
Protein-RNA (RIP-chip80)
Protein-protein
(kinase-substrate
arrays21, LUMIER81)
GENETIC
Epistatic orderings
a < b OR b < a
(EMAP28, 83)
Knock-down expression profiles
(RNAi32, deletion mutants36, 37)
Expression QTLs41, 42
Protein-compound82
UNORDERED
Ambiguous
directionality
Protein-protein
(co-IP/MS/MS18-20, Y2H15, 84-86)
Gene-gene
(co-regulon87)
Synthetic lethality
ab << a, b, wt
(SGA88, dSLAM31, 71, EMAP28, 83,
chemogenomic profiling89)
Beyer, Bandyopadhyay, and Ideker Nat. Rev. Genetics (2007)
Integration of cause-and-effect
interactions with physical networks
Perturbation
effects
Perturbation causes up-regulation
Perturbation causes down-regulation
TF-promoter binding
Protein-protein binding
Yeang, Mak et al. Genome Biology 2005
A systems approach to mapping DNA
damage networks
Numbers of promoters bound
by each of 30 transcription
factors (TFs) before and after
exposure to methyl-methane
sulfonate (MMS)
-MMS only
+MMS only
both
Workman, Mak, et al. Science 2006
Integration of cause-and-effect
interactions with physical networks
Perturbation
effects
Perturbation causes up-regulation
Perturbation causes down-regulation
TF-promoter binding
Protein-protein binding
Yeang, Mak et al. Genome Biology 2005
Validation of binding with knockout data yields a large regulatory network
Workman, Mak, et al. Science 2006
Back to the main problem – interpreting
eQTL associations with protein networks
Application to genome-wide eQTLs in yeast
(Brem 2005)
• Associations between expression levels and 2,956
genetic markers measured across 112 yeast strains
• All locus–target pairs with a gene association p-value ≤
0.05 were considered, yielding a total of 819,283 locus–
target associations.
• These associations were provided to eQED to predict
the causal gene behind each locus-target pair.
• eQED made 131,863 predictions of which causal gene in
the locus which controlled the expression changes
observed in the target.
Examples
Network example, and causal gene prediction accuracy
Method
Number of Correct
Predictions*
Random
118
Tu et al.
262
eQED
392
eQED (multi-locus)
438
* Out of 548 gold-standard cause-effect pairs
compiled from yeast gene-expression knockout
studies by Hughes et al. (2007) and Hu et al. (2007)
and a gene over-expression study by Chua et al.
(2006).
Conserved human/yeast
MAP kinase cascades
Human
Yeast
Collaboration with Prolexys and Burnham Institute
eQTL association: Silpa Suthram, Andreas Beyer
Collaborators: Roded Sharan (Tel
Aviv), Richard Karp (Berkeley)
DNA Damage Networks:
Craig Mak
Chris Workman
Collaborators:
Leona Samson (MIT)
Tom Begley (U Albany)
Funding: NIEHS, NIGMS, Unilever, Packard Fellowship
Websites: www.cytoscape.org
www.cytoscape.org
OPEN SOURCE Java platform for
integration of systems biology data
•Layout and query of interaction
networks (physical and genetic)
•Visual and programmatic integration
of molecular state data (attributes)
•The ultimate goal is to provide the
tools to facilitate all aspects of
pathway assembly and annotation.
RECENT NEWS
•Version 2.5 released Summer 2007;
Scalability+efficiency now equivalent
to best commercial packages
•The Cytoscape Consortium is a 501(c)3 non-for-profit in the State of California
•The Cytoscape ® Registered Trademark awarded
JOINTLY CODED with Agilent, ISB, Pasteur, Sloan-Kettering, UCSF, Unilever, U Toronto
http://CellCircuits.org