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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