Prediction of protein networks through data integration
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Prediction of protein networks through data integration
Lars Juhl Jensen
EMBL Heidelberg
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prediction of interactions
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STRING
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functional interactions
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373 genomes
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model organism databases
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Ensembl
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Genome Reviews
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RefSeq
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genomic context methods
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gene neighborhood
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gene fusion
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phylogenetic profiles
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Cell
Cellulosomes
Cellulose
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correct interactions
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wrong associations
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phylogenetic profiles
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SVDSingular Value Decomposition
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Euclidian distance
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gene neighborhood
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sum of intergenic distances
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raw quality scores
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rank by reliability
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not comparable
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Euclidian distance
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sum of intergenic distances
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benchmarking
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calibrate vs. gold standard
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raw quality scores
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probabilistic scores
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curated knowledge
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many sources
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KEGGKyoto Encyclopedia of Genes and Genomes
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Reactome
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PIDNCI-Nature Pathway Interaction Database
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STKESignal Transduction Knowledge Environment
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MIPSMunich Information center
for Protein Sequences
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Gene Ontology
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different gene identifiers
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synonyms list
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literature mining
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MEDLINE
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SGDSaccharomyces Genome Database
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The Interactive Fly
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OMIMOnline Mendelian Inheritance in Man
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co-mentioning
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NLPNatural Language Processing
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Gene and protein namesCue words for entity recognitionVerbs for relation extraction
[nxgene The GAL4 gene]
[nxexpr The expression of [nxgene the cytochrome genes [nxpg CYC1 and CYC7]]]is controlled by[nxpg HAP1]
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calibrate vs. gold standard
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primary experimental data
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gene expression
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GEOGene Expression Omnibus
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expression compendia
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protein interactions
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BINDBiomolecular Interaction Network Database
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BioGRIDGeneral Repository for Interaction Datasets
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DIPDatabase of Interacting Proteins
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IntAct
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MINTMolecular Interactions Database
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HPRDHuman Protein Reference Database
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many sources
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different gene identifiers
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redundancy
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not comparable
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merge data by publication
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raw quality scores
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calibrate vs. gold standard
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combine all evidence
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spread over many species
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transfer by orthology
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naïve Bayesian scoring
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prediction of interactions
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NetworKIN
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the idea
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phosphoproteomics
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mass spectrometry
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phosphorylation sites
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Phospho.ELM
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in vivo
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kinases are unknown
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computational methods
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NetPhosK
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Scansite
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sequence motifs
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kinase families
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overprediction
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no context
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what a kinase could do
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not what it actually does
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context
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co-activators
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scaffolders
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protein networks
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the algorithm
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NetworKIN
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benchmarking
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Phospho.ELM
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2.5-fold better accuracy
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context is crucial
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global statistics
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visualization
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ATM signaling
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experimental validation
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summary
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reanalysis
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benchmarking
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integration
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complementary data types
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computational methods
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reproduce what is know
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biological discoveries
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testable hypotheses
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Acknowledgments
The STRING database– Christian von Mering
– Michael Kuhn
– Berend Snel
– Martijn Huynen
– Sean Hooper
– Samuel Chaffron
– Julien Lagarde
– Mathilde Foglierini
– Peer Bork
Literature mining– Jasmin Saric
– Rossitza Ouzounova
– Isabel Rojas
The NetworKIN method– Rune Linding
– Gerard Ostheimer
– Francesca Diella
– Karen Colwill
– Jing Jin
– Pavel Metalnikov
– Vivian Nguyen
– Adrian Pasculescu
– Jin Gyoon Park
– Leona D. Samson
– Rob Russell
– Peer Bork
– Michael Yaffe
– Tony Pawson