STRINGPrediction of protein networks through

integration of diverse large-scale data sets

Lars Juhl JensenEMBL Heidelberg

STRING integrates many types of evidence

Genomic neighborhood

Species co-occurrence

Gene fusions

Database imports

Exp. interaction data

Microarray expression data

Literature co-mentioning

Integrating physical interaction screens

Make binaryrepresentationof complexes

Make binaryrepresentationof complexes

Yeast two-hybriddata sets are

inherently binary

Yeast two-hybriddata sets are

inherently binary

Calculate scorefrom number of

(co-)occurrences

Calculate scorefrom number of

(co-)occurrences

Calculate scorefrom non-shared

partners

Calculate scorefrom non-shared

partners

Calibrate against KEGG mapsCalibrate against KEGG maps

Infer associations in other speciesInfer associations in other species

Combine evidence from experimentsCombine evidence from experiments

Gene fusion: predicting physical interactions

Detect multiple proteinsmatching to one proteinDetect multiple proteinsmatching to one protein

Exclude overlappingalignments

Exclude overlappingalignments

Infer associations inother species

Infer associations inother species

Calibrate againstKEGG maps

Calibrate againstKEGG maps

Mining microarray expression databases

Re-normalize arraysby modern methodto remove biases

Re-normalize arraysby modern methodto remove biases

Buildexpression

matrix

Buildexpression

matrix

Combinesimilar arrays

by PCA

Combinesimilar arrays

by PCA

Construct predictorby Gaussian kerneldensity estimation

Construct predictorby Gaussian kerneldensity estimation

Calibrateagainst

KEGG maps

Calibrateagainst

KEGG maps

Inferassociations inother species

Inferassociations inother species

Gene neighborhood: predicting co-expression

Identify runs of adjacent geneswith the same direction

Identify runs of adjacent geneswith the same direction

Score each gene pair based onintergenic distances

Score each gene pair based onintergenic distances

Calibrate against KEGG mapsCalibrate against KEGG maps

Infer associationsin other species

Infer associationsin other species

Co-mentioning in the scientific literature

Associate abstracts with speciesAssociate abstracts with species

Identify gene names in title/abstractIdentify gene names in title/abstract

Count (co-)occurrences of genesCount (co-)occurrences of genes

Test significance of associationsTest significance of associations

Calibrate against KEGG mapsCalibrate against KEGG maps

Infer associations in other speciesInfer associations in other species

Phylogenetic profile: co-mentioning in genomes

Align all proteins against allAlign all proteins against all

Calculate best-hit profileCalculate best-hit profile

Join similar species by PCAJoin similar species by PCA

Calculate PC profile distancesCalculate PC profile distances

Calibrate against KEGG mapsCalibrate against KEGG maps

Multiple evidence types from several species

Score calibration against a common reference

• Many diverse types of evidence– The quality of each is judged by

very different raw scores

– These are all calibrated against the same reference set

• Requirements for a reference– Must represent a compromise

of the all types of evidence

– Broad species coverage

• Both a strength and a weakness– Scores for all evidence types

are directly comparable

– The type of interaction is currently not predicted

Getting more specific – generally speaking

Other possible improvements

• Bidirectionally transcribed gene pairs: a new genomic context method that may work on eukaryotes too[Korbel et al., Nature Biotechnology 2004]

• Information extraction from PubMed using shallow parsing[Saric et al., Proceedings of ACL 2004]

• Add more types of experiment types, e.g. protein expression levels

• Infer functional relations from feature similarity

• Hook up STRING with a robot

Acknowledgments

• The STRING team– Christian von Mering

– Berend Snel

– Martijn Huynen

– Daniel Jaeggi

– Steffen Schmidt

– Mathilde Foglierini

– Peer Bork

• ArrayProspector web service– Julien Lagarde

– Chris Workman

• NetView visualization tool– Sean Hooper

• Analysis of yeast cell cycle– Ulrik de Lichtenberg

– Thomas Skøt

– Anders Fausbøll

– Søren Brunak

• Web resources– string.embl.de

– www.bork.embl.de/ArrayProspector

– www.bork.embl.de/synonyms

Thank you!