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Copyright 2000 Russ Altman Early Challenges in Building an Ontology for Pharmacogenomics Russ B. Altman Stanford Biomedical Informatics Stanford University.

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Presentation on theme: "Copyright 2000 Russ Altman Early Challenges in Building an Ontology for Pharmacogenomics Russ B. Altman Stanford Biomedical Informatics Stanford University."— Presentation transcript:

1 Copyright 2000 Russ Altman Early Challenges in Building an Ontology for Pharmacogenomics Russ B. Altman Stanford Biomedical Informatics Stanford University altman@smi.stanford.eduhttp://www.smi.stanford.edu/people/altman/http://pharmgkb.org/

2 Copyright 2000 Russ Altman Outline 1. PharmGKB: challenges 2. Preliminary result: Riboweb ontology for experimental data. 3. Method for PharmGKB data model 4. A word on infrastructure.

3 Copyright 2000 Russ Altman Pharmacogenetics Understand how genetic variation leads to variation in responses to drugs.Understand how genetic variation leads to variation in responses to drugs. One of the promises of the genome projectOne of the promises of the genome project Pharmacogenomics = interacting systems of genes determining responses.Pharmacogenomics = interacting systems of genes determining responses. Some high profile examples derived from dramatic phenotypes, variants found.Some high profile examples derived from dramatic phenotypes, variants found.

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

7 Copyright 2000 Russ Altman Genomic Information Molecular and Cellular Phenotype Clinical Phenotype Drug Response Systems Molecules Individuals Alleles Variations in genome Protein products Observable Phenotypes Observable Phenotypes Role in organism Genetic makeup Environment Nongenetic factors Coding Relationship Pharmacologic activities Physiology Isolated functional measures Integrated functional measures Drugs Molecular variations Treatment protocols

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

10 Copyright 2000 Russ Altman Preliminary work: RiboWEB Hypothesis: Ontology with three main components can support 3D modeling 1. Experimental data 2. Physical Objects 3. Reference information

11 Copyright 2000 Russ Altman RiboWEB Architecture

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

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36 RiboWEB Control Panel

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

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42 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

43 Copyright 2000 Russ Altman 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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53 Non-ontological issues for PharmGKB 1. Putting patient data on the web…non trivial. Need technologies for data scrubbing and aggregation to protect privacy/confidentiality. 2. Intellectual property protection for research groups. -We are establishing submission policies for both genotype and phenotype information. 3. Relationships with other databases: dbSNP, GENBANK, PDB and assessing adequacy of our ontology for those communications.

54 Copyright 2000 Russ Altman Infrastructure for Ontology Modeling SOPHIA --academic-level software --ACCESS97 and SQL Server backends --uses ASP and Visual Basic for logic --PERL interface (serialized) --simple security/access control model --web-based browser --web-based editor --available for distribution Not the current platform for development --speed and robustness worries...

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59 Infrastructure for Ontology Modeling Protégé 2000 --professional-level software --runs in Java on many platforms --nice Java class structure for KB --somewhat simplistic ORACLE backend --see talk this afternoon The current platform for development --add Java servlet for API to app writers --add access security model for classes/slots --create major league ORACLE backend --tune performance

60 Copyright 2000 Russ Altman RiboWEB Thanks Michael Bada: RiboWEB knowledge base contents Michelle Carrillo: RiboWEB analysis modules Allison Waugh: RNA XML format Yali Wang: SOPHIA infrastructure ALSO: Harry Noller, Madison Syntax Ad Hoc Group...

61 Copyright 2000 Russ Altman PharmGKB Thanks Teri Klein (Project Director) Daniel Rubin (Ontology design) Farhad Shafa (Datacenter Director) Ray Fergerson (Protégé-2000 Architect) Marty Mayberry (KB infrastructure) Micheal Hewett (KB infrastructure) Mildred Cho (Bioethics) Mark Musen (Co-PI) Josh StuartSoumya Raychaudhuri Jeff ChangDiane Oliver Vincent LiuIrene Liu Zhen LinLouisa Crosby

62 Copyright 2000 Russ Altman Class: Person Slots: Name Address Sex Collaborator Class: Man Slots: Name Address Sex Male Collaborator Y-allele Class: Woman Slots: Name Address Sex Female Collaborator X-alleles Instance: Russ23 Slots: Name Russ Address 251 Campus Sex Male Y-allele Y234112 Collab. Kathy123, Mark666 Instance: Mark666 Slots: Name Mark Address 253 Campus Sex Male Y-allele Y534033 Collab. Russ23 Instance: Kathy123 Slots: Name Kathy Address 201 Parnassus Sex Female X-alleles X234, X454 Collab. Russ23 IS-A Links Classes (concepts, frames) Instances Attributes (slots) Attribute values Basics of Frame Representations for Data Modeling


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