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STRING Prediction of functional relations, modules, and networks from heterogeneous genome-scale data Lars Juhl Jensen EMBL Heidelberg
Cross-species integration of diverse data ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
STRING provides a modular protein network by integrating 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 rends in Microbiology Resting protuberances Protracted protuberance Cellulose © Trends Microbiol, 1999 Cell Cell wall Anchoring  proteins Cellulosomes Cellulose The “Cellulosome”
Genomic context methods © Nature Biotechnology, 2004
Score calibration against a common reference ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Integrating physical interaction screens 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 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” ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],? Source species Target species
Multiple evidence types from several species
Predicting and defining metabolic pathways and other functional modules Image: Molecular Biology of the Cell, 3 . rd edition Metabolism overview Defined manually: cutting metabolic maps into pathways Purine biosynthesis Histidine biosynthesis Defined objectively: standard clustering of genome-scale data
 
Getting more specific – generally speaking ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Conclusions ,[object Object],[object Object],[object Object],[object Object],[object Object]
Acknowledgments ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Thank you!

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STRING - Prediction of functional relations, modules, and networks from heterogeneous genome-scale data

  • 1. STRING Prediction of functional relations, modules, and networks from heterogeneous genome-scale data Lars Juhl Jensen EMBL Heidelberg
  • 2.
  • 3. STRING provides a modular protein network by integrating diverse types of evidence Genomic neighborhood Species co-occurrence Gene fusions Database imports Exp. interaction data Microarray expression data Literature co-mentioning
  • 4. Inferring functional modules from gene presence/absence patterns T rends in Microbiology Resting protuberances Protracted protuberance Cellulose © Trends Microbiol, 1999 Cell Cell wall Anchoring proteins Cellulosomes Cellulose The “Cellulosome”
  • 5. Genomic context methods © Nature Biotechnology, 2004
  • 6.
  • 7. Integrating physical interaction screens 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 Infer associations in other species Combine evidence from experiments
  • 8. 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
  • 9.
  • 10. Multiple evidence types from several species
  • 11. Predicting and defining metabolic pathways and other functional modules Image: Molecular Biology of the Cell, 3 . rd edition Metabolism overview Defined manually: cutting metabolic maps into pathways Purine biosynthesis Histidine biosynthesis Defined objectively: standard clustering of genome-scale data
  • 12.  
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  • 14.
  • 15.