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

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Lars Juhl Jensen
Lars Juhl Jensen

12th International Conference on Intelligent Systems for Molecular Biology, Genome Annotation SIG, Scottish Exhibition & Conference Center, Glasgow, Scotland, July 29-August 4, 2004

Technology
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STRING Prediction of protein networks through integration of diverse large-scale data sets Lars Juhl Jensen EMBL Heidelberg
STRING integrates many types of evidence Genomic neighborhood Species co-occurrence Gene fusions Database imports Exp. interaction data Microarray expression data Literature co-mentioning
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
Gene fusion: predicting physical interactions Detect multiple proteins matching to one protein Exclude overlapping alignments Infer associations in other species Calibrate against KEGG maps
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
Gene neighborhood: predicting co-expression 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
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
Phylogenetic profile: co-mentioning in genomes Align all proteins against all Calculate best-hit profile Join similar species by PCA Calculate PC profile distances Calibrate against KEGG maps
Multiple evidence types from several species
Score calibration against a common reference ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Getting more specific – generally speaking
Other possible improvements ,[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]
Thank you!

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

  • 1. STRING Prediction of protein networks through integration of diverse large-scale data sets Lars Juhl Jensen EMBL Heidelberg
  • 2. STRING integrates many types of evidence Genomic neighborhood Species co-occurrence Gene fusions Database imports Exp. interaction data Microarray expression data Literature co-mentioning
  • 3. 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
  • 4. Gene fusion: predicting physical interactions Detect multiple proteins matching to one protein Exclude overlapping alignments Infer associations in other species Calibrate against KEGG maps
  • 5. 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
  • 6. Gene neighborhood: predicting co-expression 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
  • 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. Phylogenetic profile: co-mentioning in genomes Align all proteins against all Calculate best-hit profile Join similar species by PCA Calculate PC profile distances Calibrate against KEGG maps
  • 9. Multiple evidence types from several species
  • 10.
  • 11. Getting more specific – generally speaking
  • 12.
  • 13.