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Early Challenges in Building an Ontology for Pharmacogenomics

Early Challenges in Building an Ontology for Pharmacogenomics. R uss B. Altman Stanford Biomedical Informatics Stanford University altman@smi.stanford.edu http://www.smi.stanford.edu/people/altman/ http://pharmgkb.org/. Outline. 1. PharmGKB: challenges

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Early Challenges in Building an Ontology for Pharmacogenomics

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  1. Early Challenges in Building an Ontology for Pharmacogenomics • Russ B. Altman • Stanford Biomedical Informatics • Stanford University • altman@smi.stanford.edu • http://www.smi.stanford.edu/people/altman/ • http://pharmgkb.org/

  2. Outline • 1. PharmGKB: challenges • 2. Preliminary result: Riboweb ontology for experimental data. • 3. Method for PharmGKB data model • 4. A word on infrastructure.

  3. Pharmacogenetics • Understand how genetic variation leads to variation in responses to drugs. • One of the promises of the genome project • Pharmacogenomics = interacting systems of genes determining responses. • Some high profile examples derived from dramatic phenotypes, variants found.

  4. Pharmacogenetics Database Methyl- transferases Transporters Cyto P450 Steroid receptors Sulfur transferases Leukotriene metabolism Adrenergic receptors ??? NIGMS Pharmacogenetic Research Network & Database http://www.nih.gov/grants/guide/rfa-files/RFA-GM-99-004.html

  5. Molecules Drug Response Systems Drugs Isolated functional measures Integrated functional measures Molecular and Cellular Phenotype Genomic Information Clinical Phenotype Observable Phenotypes Variations in genome Observable Phenotypes Coding Relationship Physiology Alleles Protein products Genetic makeup Role in organism Molecular variations Treatment protocols Individuals Pharmacologic activities Nongenetic factors Environment

  6. Templates inadequate, change data model Data acquisition forms inadequate Data Model for PharmGKB Templates for data acquisition Use data model to automatically generate Translate into executable HTML forms Deployed data acquisition forms Data Stored within PharmGKB New data for PharmGKB Make available to scientists in research network Store fully linked new data into PharmGKB

  7. Preliminary work: RiboWEB • Hypothesis: Ontology with three main components can support 3D modeling • 1. Experimental data • 2. Physical Objects • 3. Reference information

  8. RiboWEB Architecture

  9. Knowledge Base Summary • 171 journal articles • ~30 templates for experimental data • 15,000 instances of objects, people, data • 8000 experimental data items

  10. RiboWEBControlPanel

  11. Avg Error vs. Three experiment types (all data) OH* CLEAVAGE CROSSLINK FOOTPRINTS

  12. Two Methods for Building the Ontology for PharmGKB • 1. TOP DOWN (borrow from RiboWEB) • Physical Objects • Experimental Data • Reference terminologies • 2. BOTTOM UP (read the grants!) • Particular genes • Individual small molecules • Experimental methods

  13. Reference Terminologies • Principle: don’t reinvent things we don’t have to! • 1. ICD-9 for diseases • 2. SNOMED/RDC codes for symptoms • 3. EC and Cytochrome classification system • 4. GO • 5. Cellular localization vocabulary • 6. SMILES for small compounds • 7. Others... • These are imported on an “as needed basis” for now. Normally don’t have instances in our KB of terminology classes.

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