STRING - Prediction of functionally associated proteins from heterogeneous genome scale data sets

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EBI Industry Workshop, European Bioinformatics Institute, Hinxton, England, September 27-29, 2004

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  • STRING - Prediction of functionally associated proteins from heterogeneous genome scale data sets

    1. 1. STRING Prediction of functionally associated proteins from heterogeneous genome scale data sets Lars Juhl Jensen EMBL Heidelberg
    2. 2. Cross-species integration of diverse data <ul><li>Challenges and promises of large-scale data integration </li></ul><ul><ul><li>Explosive increase in both the amounts and different types of high-throughput data sets that are being produced </li></ul></ul><ul><ul><li>These data are highly heterogeneous and lack standardization </li></ul></ul><ul><ul><li>Most data sets are error-prone and suffer from systematic biases </li></ul></ul><ul><ul><li>Experiments should be integrated across model organisms </li></ul></ul><ul><li>STRING is a web resource that integrates and transfers diverse large-scale data across 100+ species, but it is not </li></ul><ul><ul><li>a primary repository for experimental data </li></ul></ul><ul><ul><li>a curated database of complexes or pathways </li></ul></ul><ul><ul><li>a substitute for expert annotation </li></ul></ul>
    3. 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. 4. Two modes of operation “ Protein mode” Separate network for each species “ COG mode” One network covering all species
    5. 5. 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”
    6. 6. 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
    7. 7. Score calibration against a common reference <ul><li>Different pieces of evidence are not directly comparable </li></ul><ul><ul><li>A different raw quality score is used for each evidence type </li></ul></ul><ul><ul><li>Quality differences exist among data sets of the same type </li></ul></ul><ul><li>Solved by calibrating all scores against a common reference </li></ul><ul><li>Requirements for the reference </li></ul><ul><ul><li>Must represent a compromise of the all types of evidence </li></ul></ul><ul><ul><li>Broad species coverage </li></ul></ul><ul><ul><li>Our chosen reference is KEGG metabolic maps </li></ul></ul>
    8. 8. 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
    9. 9. 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
    10. 10. Evidence transfer based on “fuzzy orthology” <ul><li>Orthology transfer is tricky </li></ul><ul><ul><li>Correct assignment of orthology is difficult for distant species </li></ul></ul><ul><ul><li>Functional equivalence cannot be guaranteed for in-paralogs </li></ul></ul><ul><li>These problems are addressed by our “fuzzy orthology” scheme </li></ul><ul><ul><li>Confidence scores for functional equivalence are calculated from all-against-all alignment </li></ul></ul><ul><ul><li>Evidence is distributed across possible pairs according to confidence scores in the case of many-to-many relationships </li></ul></ul>? Source species Target species
    11. 11. 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
    12. 12. 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
    13. 13. 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
    14. 14. The power of cross-species transfer and evidence integration
    15. 15. 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
    16. 16. Getting more specific – generally speaking <ul><li>Benchmarking against one common reference allows integration of heterogeneous data </li></ul><ul><li>The different types of data do not all tell us about the same kind of functional associations </li></ul><ul><li>It should be possible to assign likely interaction types from supporting evidence types </li></ul><ul><li>An accurate model of the yeast mitotic cell cycle </li></ul><ul><li>Approach </li></ul><ul><ul><li>High confidence set of physical interactions </li></ul></ul><ul><ul><li>Custom analysis of cell cycle expression data </li></ul></ul><ul><li>Observations </li></ul><ul><ul><li>Dynamic assembly of cell cycle complexes </li></ul></ul><ul><ul><li>Temporal regulation of Cdk specificity </li></ul></ul>
    17. 17. Summary <ul><li>Quality assessment of each individual large-scale data set is a prerequisite for successful data integration </li></ul><ul><li>High confidence prediction of functional associations and modules is possible when combining lines of evidence </li></ul><ul><li>Transfer of evidence between species is an increasingly important aspect of large-scale data integration </li></ul><ul><li>Take a look at STRING – an update is in the pipeline </li></ul>
    18. 18. Acknowledgments <ul><li>The STRING team </li></ul><ul><ul><li>Christian von Mering </li></ul></ul><ul><ul><li>Berend Snel </li></ul></ul><ul><ul><li>Martijn Huynen </li></ul></ul><ul><ul><li>Daniel Jaeggi </li></ul></ul><ul><ul><li>Steffen Schmidt </li></ul></ul><ul><ul><li>Mathilde Foglierini </li></ul></ul><ul><ul><li>Peer Bork </li></ul></ul><ul><li>ArrayProspector web service </li></ul><ul><ul><li>Julien Lagarde </li></ul></ul><ul><ul><li>Chris Workman </li></ul></ul><ul><li>NetView visualization tool </li></ul><ul><ul><li>Sean Hooper </li></ul></ul><ul><li>Analysis of yeast cell cycle </li></ul><ul><ul><li>Ulrik de Lichtenberg </li></ul></ul><ul><ul><li>Thomas Skøt </li></ul></ul><ul><ul><li>Anders Fausbøll </li></ul></ul><ul><ul><li>Søren Brunak </li></ul></ul><ul><li>Web resources </li></ul><ul><ul><li>string.embl.de </li></ul></ul><ul><ul><li>www.bork.embl.de/ArrayProspector </li></ul></ul><ul><ul><li>www.bork.embl.de/synonyms </li></ul></ul>
    19. 19. Thank you!
    20. 20. STRING Examples for practical session Lars Juhl Jensen EMBL Heidelberg

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