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Prediction of protein networks through data integration Lars Juhl Jensen EMBL Heidelberg

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MIPS Retreat, Kloster Frauenchiemsee, Chiemsee, Germany, July 9-10, 2007

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Page 1: Prediction of protein networks through data integration

Prediction of protein networks through data integration

Lars Juhl Jensen

EMBL Heidelberg

Page 2: Prediction of protein networks through data integration

prediction of interactions

Page 3: Prediction of protein networks through data integration

STRING

Page 4: Prediction of protein networks through data integration
Page 5: Prediction of protein networks through data integration

functional interactions

Page 6: Prediction of protein networks through data integration

373 genomes

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model organism databases

Page 8: Prediction of protein networks through data integration

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

Page 25: Prediction of protein networks through data integration
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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

Page 34: Prediction of protein networks through data integration

Euclidian distance

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sum of intergenic distances

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benchmarking

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calibrate vs. gold standard

Page 38: Prediction of protein networks through data integration
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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

Page 46: Prediction of protein networks through data integration

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

Page 60: Prediction of protein networks through data integration
Page 61: Prediction of protein networks through data integration

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

Page 67: Prediction of protein networks through data integration

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

Page 79: Prediction of protein networks through data integration
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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

Page 110: Prediction of protein networks through data integration
Page 111: Prediction of protein networks through data integration

the algorithm

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NetworKIN

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Page 114: Prediction of protein networks through data integration

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

Page 135: Prediction of protein networks through data integration

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


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