The document discusses methods for integrating diverse evidence like genomic context, gene fusions, expression data, and protein-protein interaction screens to infer functional associations between proteins across species. It describes benchmarking different data sources against a common reference to make scores comparable and combining evidence probabilistically. The methods are applied to construct an accurate protein interaction network for the yeast cell cycle by extracting a periodically expressed subset and integrating temporal expression and interaction data.

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 Calculate pairwise linear correlation coefficients Calibrate against KEGG maps Infer associations in other species

12. Multiple evidence types from several species

14. Getting the parts list Yeast culture Microarrays Gene expression Expression profile 600 periodically expressed genes (with associated peak times) that encode “dynamic proteins” The parts list New analysis Cho & Spellman et al.

16. Extracting a cell cycle interaction network Cell cycle microarray data Physical PPI interactions with confidence scores Expand the set of proteins to include non-periodic proteins that are strongly connected to periodic proteins Raw Data Node selection List of periodically expressed proteins with peak time Interactions Require compatible compartments and high confidence Extract cell cycle network

19. Cdc28p and its interaction partners

21. Assembly of the pre-replication complex

26. Thank you!