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STRING Modeling of biological systems through cross-species data integration Lars Juhl Jensen promoter analysis Jensen et al., Bioinformatics, 2000 genome visualization Pedersen et al., Journal of Molecular Biology, 2000 protein function prediction STRING integrate diverse evidence functional interactions Bork et al., Current Opinion in Structural Biology, 2005 179 proteomes genomic context methods phylogenetic profiles Cell Cellulosomes Cellulose anti-correlated profiles analogous enzymes Morett et al., Nature Biotechnology, 2003 gene neighborhood bidirectional promoters Korbel et al., Nature Biotechnology, 2004 gene fusion evolution statistics (the original sin) scoring and benchmarking raw quality scores gene neighborhood sum of intergenic distances many types of evidence not directly comparable calibrate vs. gold standard curated knowledge KEGG Kyoto Encyclopedia of Genes and Genomes STKE Signal Transduction Knowledge Environment Reactome MIPS Munich Information center for Protein Sequences primary experimental data Jensen et al., Drug Discovery Today: Targets, 2004 microarray expression data GEO Gene Expression Omnibus physical protein interactions BIND Biomolecular Interaction Network Database MINT Molecular Interactions Database GRID General Repository for Interaction Datasets DIP Database of Interacting Proteins HPRD Human Protein Reference Database von Mering et al., Nucleic Acids Research, 2005 literature mining MEDLINE SGD Saccharomyces Genome Database The Interactive Fly OMIM Online Mendelian Inheritance in Man co-mentioning different gene names curated synonyms lists NLP Natural Language Processing Gene and protein names Cue words for entity recognition Verbs for relation extraction [nxgene The GAL4 gene] [nxexpr The expression of [nxgene the cytochrome genes [nxpg CYC1 and CYC7]]] is controlled by [nxpg HAP1] Jensen et al., Nature Reviews Genetics, 2006 combine all evidence naïve Bayesian scheme spread over many species transfer based orthology Target species ? Source species defining functional modules qualitative modeling the mitochondrial system RCCs predicting “mode of action” Jensen et al., Drug Discovery Today: Targets, 2004 Jensen et al., Drug Discovery Today: Targets, 2004 Acknowledgments • The STRING team (EMBL) – – – – – – – Christian von Mering Berend Snel Martijn Huynen Sean Hooper Mathilde Foglierini Julien Lagarde Peer Bork • Literature mining project (EML Research) – Jasmin Saric – Rossitza Ouzounova – Isabel Rojas • New genomic context methods (EMBL) – Jan Korbel – Peer Bork • Modeling of yeast mitochondria (EMBL) – Fabiana Perocchi – Lars Steinmetz • Inspiration for presentation – Dick Clarence Hardt – Anders Gorm Pedersen Thank you!