STRING - Prediction of protein networks through integration of diverse large-scale data sets
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The document discusses the integration of diverse large-scale data sets by STRING to predict protein networks using evidence like genomic neighborhoods, co-occurrence, gene fusions, and expression data. It details methods for scoring interactions and inferring associations across different species while calibrating against KEGG maps. Additionally, it acknowledges the contributions of various team members and future improvements to enhance predictive capabilities.
![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 ](https://image.slidesharecdn.com/jen04talk5-1217966087669177-9/85/STRING-Prediction-of-protein-networks-through-integration-of-diverse-large-scale-data-sets-12-320.jpg)
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. 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. Acknowledgments 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
- 14. Thank you!