Introduction to STRING

Lars Juhl JensenEMBL Heidelberg

STRING

integrate diverse evidence

functional interactions

hundreds of proteomes

Ensembl

SWISS-PROT

prokaryotes

genomic context methods

gene fusion

gene neighborhood

phylogenetic profiles

Cell

Cellulosomes

Cellulose

eukaryotes

data integration

curated knowledge

MIPSMunich Information center

for Protein Sequences

Reactome

KEGGKyoto Encyclopedia of Genes and Genomes

STKESignal Transduction Knowledge Environment

literature mining

co-mentioning

NLPNatural Language Processing

MEDLINE

SGDSaccharomyces Genome Database

The Interactive Fly

OMIMOnline Mendelian Inheritance in Man

primary experimental data

microarray expression data

GEOGene Expression Omnibus

SMDStanford Microarray Database

physical protein interactions

BINDBiomolecular Interaction Network Database

MINTMolecular Interactions Database

GRIDGeneral Repository for Interaction Datasets

DIPDatabase of Interacting Proteins

HPRDHuman Protein Reference Database

problems

many sources

different gene identifiers

many types of evidence

questionable quality

not directly comparable

spread over many species

parsers

synonyms lists

quality scores

benchmarking

orthology

how is it actually done?

gene fusion

Find in A genes that matcha the same gene in B

Exclude overlappingalignments

Calibrate againstKEGG maps

Calculate all-against-allpairwise alignments

gene neighborhood

Identify runs of adjacent geneswith the same direction

Score each gene pair based onintergenic distances

Calibrate against KEGG maps

Infer associationsin other species

phylogenetic profiles

Align all proteins against all

Calculate best-hit profile

Join similar species by PCA

Calculate PC profile distances

Calibrate against KEGG maps

literature co-occurrence

Associate abstracts with species

Identify gene names in title/abstract

Count (co-)occurrences of genes

Test significance of associations

Calibrate against KEGG maps

Infer associations in other species

physical interaction data

Make binaryrepresentationof complexes

Yeast two-hybriddata sets are

inherently binary

Calculate scorefrom number of

(co-)occurrences

Calculate scorefrom non-shared

partners

Calibrate against KEGG maps

Infer associations in other species

Combine evidence from experiments

calibrate against KEGG

transfer by orthology

orthologous groups

fuzzy orthology

?

Source species

Target species

combine all evidence

AcknowledgmentsThe STRING team (EMBL)– Christian von Mering– Berend Snel– Martijn Huynen– Sean Hooper– Samuel Chaffron– Julien Lagarde– Mathilde Foglierini– Peer Bork

Literature mining project(EML Research)– Jasmin Saric– Rossitza Ouzounova– Isabel Rojas

Thank you!