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Protein interaction networksfrom yeast to human
Lars Juhl Jensen
EMBL Heidelberg
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Current Opinion in Structural Biology, 2005
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STRING
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integrate diverse evidence
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functional interactions
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Bork et al., Current Opinion in Structural Biology, 2005
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hundreds of proteomes
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evolution
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statistics
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(the original sin)
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prokaryotes
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genomic context methods
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gene fusion
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gene neighborhood
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phylogenetic profiles
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Cell
Cellulosomes
Cellulose
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eukaryotes
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integrate diverse datasets
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Jensen et al., Drug Discovery Today: Targets, 2004
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curated knowledge
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MIPSMunich Information center
for Protein Sequences
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KEGGKyoto Encyclopedia of Genes and Genomes
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STKESignal Transduction Knowledge Environment
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Reactome
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literature mining
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co-mentioning
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NLPNatural Language Processing
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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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primary experimental data
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microarray expression data
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GEOGene Expression Omnibus
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physical protein interactions
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BINDBiomolecular Interaction Network Database
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MINTMolecular Interactions Database
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GRIDGeneral Repository for Interaction Datasets
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DIPDatabase of Interacting Proteins
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HPRDHuman Protein Reference Database
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problems
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many sources
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(different gene identifiers)
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many types of evidence
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questionable quality
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not directly comparable
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spread over many species
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huge synonyms lists
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calculate raw quality scores
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calibrate vs. gold standard
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KEGGKyoto Encyclopedia of Genes and Genomes
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von Mering et al., Nucleic Acids Research, 2005
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transfer based on orthology
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combine all evidence
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Bork et al., Current Opinion in Structural Biology, 2005
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Networ-Kin™
Phospho-peptidedata (from MS)
Predict the kinase class(NetPhosK and Scansite)
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the human interactome
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yeast two-hybrid
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1936
13
4
4
1385
65
18465
Stelzl et al. Rual et al.
Small-scale studies
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32
0
3
4
18
4
23
Stelzl et al. Rual et al.
Small-scale studies
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62 8 39
Small-scale studies
Stelzl et al. Rual et al.
852
17
473
432
69
260
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3.5% and 21% sensitivity
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in a couple of years
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the human interactome
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100% = 1/5?
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the yeast interactome
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five years ago
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yeast two-hybrid
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1150
117
117
72
4053
118
4469
Uetz et al. Ito et al.
Small-scale studies
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162
53
34
72
180
29
338
Uetz et al. Ito et al.
Small-scale studies
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511 189 616
Small-scale studies
Uetz et al. Ito et al.
439
178
759
897
190
1347
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19% and 12% sensitivity
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three years ago
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complex purification and MS
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14186
784
1178
287
19492
230
3475
Gavin et al. Ho et al.
Small-scale studies
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2341
125
656
287
2725
42
149
Gavin et al. Ho et al.
Small-scale studies
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4431 1465 5041
Small-scale studies
Gavin et al. Ho et al.
14913
1071
632
8047
517
746
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63% and 41% sensitivity
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today
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combining all available data
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scoring and filtering
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de Lichtenberg et al., Science, 2005
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de Lichtenberg et al., Science, 2005
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de Lichtenberg et al., Science, 2005
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de Lichtenberg et al., Science, 2005
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de Lichtenberg et al., Science, 2005
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the challenge
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how to get from here …
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1936
13
4
4
1385
65
18465
Stelzl et al. Rual et al.
Small-scale studies
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… to there …
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de Lichtenberg et al., Science, 2005
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yeast two-hybrid
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complex purification and MS
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scoring and filtering
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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
• Cell cycle studies (CBS)– Ulrik de Lichtenberg
– Thomas Skøt Jensen
– Søren Brunak
• Networ-Kin™ (Mt. Sinai)– Rune Linding
– Tony Pawson
• Inspiration for presentation– Lawrence Lessig
– Dick Clarence Hardt
– Anders Gorm Pedersen
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Thank you!