The document summarizes STRING, a database that integrates diverse evidence like genomic neighborhood, gene fusions, and protein-protein interaction data to infer functional associations between proteins across species. It describes several methods used by STRING, including genomic context methods, phylogenetic profiles, and orthology transfer, which allow predictions to be made even for eukaryotic proteins. Benchmarking various data sources against a common reference allows their integration and cross-species transfer of functional information.

1. STRING Cross-species integration of known and predicted protein-protein interactions Lars Juhl Jensen EMBL Heidelberg

2. 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

3. 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”

4. Genomic context methods © Nature Biotechnology, 2004

5. 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

6. 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

7. 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

9. 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

10. 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

12. Multiple evidence types from several species

16. Thank you!