STRINGCross-species integration of known and

predicted protein-protein interactions

Lars Juhl JensenEMBL Heidelberg

STRING provides a protein network based on integration of diverse types of evidence

Genomic neighborhood

Species co-occurrence

Gene fusions

Database imports

Exp. interaction data

Microarray expression data

Literature co-mentioning

Inferring functional modules fromgene presence/absence patterns

Restingprotuberances

Protractedprotuberance

Cellulose

© Trends Microbiol, 1999

CellCell wall

Anchoring proteins

Cellulosomes

Cellulose

The “Cellulosome”

Genomic context methods

© Nature Biotechnology, 2004

Formalizing the phylogenetic profile method

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

Predicting functional and physical interactions from gene fusion/fission events

Find in A genes that matcha the same gene in B

Find in A genes that matcha the same gene in B

Exclude overlappingalignments

Exclude overlappingalignments

Calibrate againstKEGG maps

Calibrate againstKEGG maps

Calculate all-against-allpairwise alignments

Calculate all-against-allpairwise alignments

Inferring functional associations from evolutionarily conserved operons

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

Score calibration against a common reference

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

very different raw scores

– Quality differences exist among data sets of the same type

• Solved by calibrating all scores against a common reference– Scores are directly comparable

– Probabilistic scores allow evidence to be combined

• Requirements for the reference– Must represent a compromise of

the all types of evidence

– Broad species coverage

Integrating physical interaction screens

Complexpull-down

experiments

Complexpull-down

experiments

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

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

?

Source species

Target species

Evidence transfer based on “fuzzy orthology”

• Orthology transfer is tricky– Correct assignment of orthology

is difficult for distant species

– Functional equivalence cannot be guaranteed for in-paralogs

• These problems are addressed by our “fuzzy orthology” scheme– Confidence scores for functional

equivalence are calculated from all-against-all alignment

– Evidence is distributed across possible pairs according to confidence scores in the case of many-to-many relationships

Multiple evidence types from several species

Getting more specific – generally speaking

• Benchmarking against one common reference allows integration of heterogeneous data

• The different types of data do not all tell us about the same kind of functional associations

• It should be possible to assign likely interaction types from supporting evidence types

• The aim: to construct an accurate, qualitative models of biological systems or processes

• The models should be accurate even at the level of individual interactions

• This allows specific, testable hypotheses to be made based on high-throughput experimental data

Conclusions

• Genomic context methods are able to infer the function of many prokaryotic proteins from genome sequences alone

• Integration of large-scale experimental data allows similar predictions to be made for eukaryotic proteins

• Benchmarking is a prerequisite for data integration

• Cross-species transfer is essential for making the most of the available data

• Try STRING at http://string.embl.de

Acknowledgments

• The STRING team– Christian von Mering– Berend Snel– Martijn Huynen– Daniel Jaeggi– Steffen Schmidt– Mathilde Foglierini– Peer Bork

• ArrayProspector– Julien Lagarde– Chris Workman

• New context methods– Jan Korbel– Christian von Mering– Peer Bork

Thank you!