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  1. 1. A little semantics … can go a long way!<br />What is the Semantic Web and how can it be used to accelerate translational research and biological discovery<br />Helena F. Deus<br />
  2. 2. Data is what you find on the Web<br />
  3. 3. Data, data everywhere<br />Sequences<br />Microarrays<br />Electrophoresis<br />Chrystalography<br />In vitro experiments<br />
  4. 4. What pathways is my protein involved in?<br />
  5. 5. Building bridges<br />If you could have only 3 apps to do all your work, which ones would they be?<br />
  6. 6. Building bridges<br />Statistics<br />
  7. 7. Building bridges<br />species<br />cc5<br />sub-<br />type<br />
  8. 8. Biological Knowledge Continuum<br />Metabolome<br />Knowledge Continuum<br />Medical Records<br />Microarrays<br />Proteome<br />Microbiome<br />Genome<br />Sequences<br />Protein Gels<br />
  9. 9. Enabling Translational Research<br />
  10. 10. Re-Using Data in Biology<br />~20 000 genes<br />~100 interesting genes/proteins<br />~ 10 interesting pathways<br />~5 genes/proteins testable in the lab<br />High-throughput technologies<br />Literature<br />Browse databases<br />Computational statistics<br />Hypothesis Generation<br />“I like to call it low-input, high-throughput, no-output biology.” <br />
  11. 11. Writing the story <br />??<br />
  12. 12. !!<br />
  13. 13. Computers can make life easier!<br />Statistics<br />
  14. 14. A Little Semantics<br />mecA<br />Strain1<br />hasGene<br />“resistance to<br /> met”<br />causes<br />mecA<br />Strain1<br />Sample1<br />origin<br />pneumon<br />disease<br />Sample1<br />
  15. 15. Principle #1<br />Use URL to name<br />things<br />Principle #2<br />Organize data in Triples<br />A Little Semantics<br />http://mecA<br />http://Strain1<br />hasGene<br />“resistance to<br /> met”<br />causes<br />mecA<br />http://Strain1<br />Sample1<br />origin<br />pneumon<br />disease<br />Sample1<br />
  16. 16. A Little Semantics<br />http://mecA<br />http://Strain1<br />hasGene<br />“resistance to<br /> met”<br />causes<br />http://mecA<br />http://Strain1<br />Sample1<br />origin<br />pneumon<br />disease<br />Sample1<br />
  17. 17. ... a lot of knowledge networking!<br />epidermal growth factor receptor<br />rea:Membrane<br />nci:has_description<br />rea:keyword<br />CCCCGGCGCAGCGCGGCCGCAGCAGCCTCCGCCCCCCGCACGGTGTGAGCGCCCGACGCGGCCGAGGCGG …<br />nih:sequence<br />rea:Receptor<br />nih:EGFR<br />nih:EGFR<br />rea:keyword<br />nih:organism<br />rea:keyword<br />Homo sapiens<br />rea:Transferase<br />nih:interacts<br />nih:EGF<br />nih:organism<br />Reactome<br />NCBI<br />
  18. 18. Linked Data Cloud – the Story so Far<br />Src: http://linkeddata.org/<br />
  19. 19. How to make use of that data?<br />What are the microbial Staphylococcus strains, belonging to clonal complex 5 and collected in Portugal? And when were they collected?<br />Staphylococcus<br />Clonal Complex 5<br />Date of <br />Collection<br />Portugal<br />
  20. 20. How to make use of that data?<br />What are the microbial Staphylococcus strains, belonging to clonal complex 5 and collected in Portugal?<br />?Strain :hasClonalComplex 5 <br /> :hasSpeciesStaphylococcus<br /> :hasOrigin Portugal <br />And when were those isolates collected?<br />?Sample :hasIsolate ?Strain ;<br /> :wasCollected ?Date <br />
  21. 21. Linking genomes<br />
  22. 22. Linking Diseases<br />Src: Kwang-Il Goh et al. The human disease network PNAS 2007 104 (21)<br />
  23. 23. Genetic Landscape<br />Source: Science 22 January 2010: Vol. 327 no. 5964 pp. 425-431 <br />
  24. 24. How about the statistics?<br />
  25. 25. Plugging data to the Web of the Future<br />
  26. 26. Statements per rule<br /> 0 <br />350 <br />2500<br />2000<br /> 50 <br />1500<br />1000<br />300 <br />500 <br />0 <br />0 <br />100 <br />200 <br />300 <br />400 <br />500 <br />600 <br />700 <br />800 <br />900 <br />1000<br />Sessions<br />Rules<br />0 <br />10<br />20<br />30<br />40<br />50<br />60<br />70<br /> 100<br />0 <br />5 <br />250 <br />10<br />15<br />20<br /> 150<br />200 <br />25<br />Users<br />A year <br />in the life of a semantic database<br />Measuring the re-engineering of ontologies<br />Day 5<br /><ul><li>Seeding</li></ul>Day 365<br /><ul><li>Calibration</li></ul>Day 17<br />Time (days)<br />Day 152<br />Growth<br />Day 25<br /><ul><li>Maturation</li></li></ul><li>Exploring TCGA via S3DB<br />
  27. 27. 2001: The Semantic Web<br />Semantic Web<br />A web where computers, not just humans, can read and write<br />

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