STRING Cross-species integration of known and predicted protein-protein interactions Lars Juhl Jensen EMBL 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 from gene presence/absence patterns T Resting protuberances Protracted protuberance Cellulose © Trends Microbiol, 1999 Cell Cell wall Anchoring proteins Cellulosomes Cellulose The “Cellulosome”

Genomic context methods © Nature Biotechnology, 2004

Formalizing the phylogenetic profile method Align all proteins against all Calculate best-hit profile Join similar species by PCA Calculate PC profile distances Calibrate against KEGG maps

Predicting functional and physical interactions from gene fusion/fission events Find in A genes that match a the same gene in B Exclude overlapping alignments Calibrate against KEGG maps Calculate all-against-all pairwise alignments

Inferring functional associations from evolutionarily conserved operons Identify runs of adjacent genes with the same direction Score each gene pair based on intergenic distances Calibrate against KEGG maps Infer associations in 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

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 Complex pull-down experiments Yeast two-hybrid data sets are inherently binary Calculate score from number of (co-)occurrences Calculate score from non-shared partners Calibrate against KEGG maps Infer associations in other species Combine evidence from experiments

Mining microarray expression databases Re-normalize arrays by modern method to remove biases Build expression matrix Combine similar arrays by PCA Construct predictor by Gaussian kernel density estimation Calibrate against KEGG maps Infer associations in other 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 ? Source species Target species

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

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

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

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!