STRING Prediction of protein networks through integration of diverse large-scale data sets

1. STRINGPrediction of protein networks throughintegration of diverse large-scale data sets Lars Juhl JensenEMBL Heidelberg

2. STRING integrates many types of evidence Genomic neighborhood Species co-occurrence Gene fusions Exp. interaction data Microarray expression data Database imports Literature co-mentioning

3. Make binary representation of complexes 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 Combine evidence from experiments Infer associations in other species Integrating physical interaction screens

4. Gene fusion: predicting physical interactions Detect multiple proteins matching to one protein Exclude overlapping alignments Calibrate against KEGG maps Infer associations in other species

5. 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 Mining microarray expression databases

6. 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 Gene neighborhood: predicting co-expression

7. Co-mentioning in the scientific literature 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

8. Align all proteins against all Calculate best-hit profile Join similar species by PCA Calculate PC profile distances Calibrate against KEGG maps Phylogenetic profile: co-mentioning in genomes

9. Multiple evidence types from several species

10. 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

11. Getting more specific – generally speaking

12. 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 

13. 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 Acknowledgments

14. Thank you!