1. MGED Ontology Working Group MGED4 Boston, MA Feb. 15, 2002 Chris Stoeckert, Center for Bioinformatics, U. Penn Helen Parkinson, EBI

2. Agenda Overview of ontologies Status of MGED Ontology Incorporating ontologies into microarray database annotation forms - Helen Parkinson Discussion –Annotation experience –Use Cases: needs besides retrieving experiments? –issues: Missing concepts? (quick tour of ontology) Relationship between MAGE and MGED ontology

3. What Does an Ontology Do? Captures knowledge Creates a shared understanding – between humans and for computers Makes knowledge machine processable Makes meaning explicit – by definition and context From Building and Using Ontologies, Robert Stevens, U. of Manchester

4. What is an Ontology? Catalog/ ID General Logical constraints Terms/ glossary Thesauri “narrower term” relation Formal is-a Frames (properties) Informal is-a Formal instance Value Restrs.Disjointness, Inverse, part- of… From Building and Using Ontologies, Robert Stevens, U. of Manchester

5. Uses of Ontology Community reference -- neutral authoring. Either defining database schema or defining a common vocabulary for database annotation -- ontology as specification. Providing common access to information. Ontology-based search by forming queries over databases. Understanding database annotation and technical literature. Guiding and interpreting analyses and hypothesis generation From Building and Using Ontologies, Robert Stevens, U. of Manchester

6. Components of an Ontology Concepts: Class of individuals – The concept Protein and the individual `human cytochrome C’ Relationships between concepts Is a kind of relationship forms a taxonomy Other relationships give further structure – is a part of Axioms – Disjointness, covering, equivalence,… From Building and Using Ontologies, Robert Stevens, U. of Manchester

7. Languages Vocabularies using natural language –Hand crafted, flexible but difficult to evolve, maintain and keep consistent, with weak semantics –Gene Ontology Object-based KR: frames –Extensively used, good structuring, intuitive. Semantics defined by OKBC standard –EcoCyc (uses Ocelot) and RiboWeb (uses Ontolingua) Logic-based: Description Logics –Very expressive, model is a set of theories, well defined semantics –Automatic derived classification taxonomies –Concepts are defined and primitive From Building and Using Ontologies, Robert Stevens, U. of Manchester

8. Microarray Information to be Captured Figure from: David J. Duggan et al. (1999) Expression Profiling using cDNA microarrays. Nature Genetics 21: 10-14

9. MGED Ontology Working Group Goals 1.Identify concepts 2.Collect available controlled vocabularies and ontologies for concepts 3.Define concepts 4.Formalize concept relationships

10. Relationship of MGED Efforts MAGE MIAME DB MIAME DB External Ontologies/CVs MGED Ontology  Annotation  Format  Ontologies  External  Internal Ontologies provide common terms and their definitions for describing microarray experiments.

11. http://www.cbil.upenn.edu/Ontology/

12. Species Resources

13. Concept Definitions

14. Usage of Concepts and Resources for Microarrays MIAME glossary –Provide definitions for types of information (concepts) listed in MIAME MIAME qualifier, value, source –Provide pointers to relevant sources that can be used to annotate experiments

15. sample source and treatment ID as used in section 1 organism (NCBI taxonomy) additional "qualifier, value, source" list; the list includes: cell source and type (if derived from primary sources (s)) sex age growth conditions development stage organism part (tissue) animal/plant strain or line genetic variation (e.g., gene knockout, transgenic variation) individual individual genetic characteristics (e.g., disease alleles, polymorphisms) disease state or normal target cell type cell line and source (if applicable) in vivo treatments (organism or individual treatments) in vitro treatments (cell culture conditions) treatment type (e.g., small molecule, heat shock, cold shock, food deprivation) compound is additional clinical information available (link) separation technique (e.g., none, trimming, microdissection, FACS) laboratory protocol for sample treatment MIAME Section on Sample Source and Treatment

16. External References ©- BioMaterialDescription © -Biosource Property © -Organism © -Age © -DevelopmentStage © -Sex © -StrainOrLine © -BiosourceProvider © -OrganismPart © -BioMaterialManipulation © -EnvironmentalHistory ©- CultureCondition ©- Temperature ©- Humidity ©- Light © -PathogenTests © -Water © -Nutrients © -Treatment © -CompoundBasedTreatment (Compound) (Treatment_application) (Measurement) MGED Ontology Instances NCBI Taxonomy Mouse Anatomical Dictionary International Committee on Standardized Genetic Nomenclature for Mice International Committee on Standardized Genetic Nomenclature for Mice Mouse Anatomical Dictionary ChemIDplus Mus musculus musculus id: 39442 7 weeks after birth Stage 28 Female C57BL/6N Charles River, Japan Liver 22  2  C 55  5% 12 hours light/dark cycle Specified pathogen free conditions ad libitum MF, Oriental Yeast, Tokyo, Japan Fenofibrate, CAS 49562-28-9 in vivo, oral gavage 100mg/kg body weight An example of microarray sample annotation using the MGED ontology Susanna A. Sansone, Helen Parkinson, Philippe Rocca-Serra, Chris Stoeckert and Alvis Brazma

17. MAGE BioMaterial Model

19. MGED Biomaterial Ontology Under construction –Using OILed (Not wedded to any one tool) –Generate multiple formats: RDFS, DAML+OIL Define classes, provide relations and constraints, identify instances Motivated by MIAME and coordinated with MAGE

20. http://www.ontoknowledge.org/oil/

21. Building a Microarray Ontology http://www.cbil.upenn.edu/Ontology/Build_Ontology2.html

22. http://mged.sourceforge.net/Ontologies.shtml

23. Ontology in Browseable Form

24. Example of Internal Terms

25. Example of External Terms

26. Example of Combined Internal and External: Treatment

27. OWG Use Cases Make it easier and more accurate to annotate a microarray experiment. –Build forms that provide menus of terms and links to external resources. –Only ask for relevant terms and fill in terms that can be inferred. Return a summary of all experiments that use a specified type of biosource. –Use “age” to select and order experiments –Use Mouse Anatomical Dictionary Stage 28 to pick experiments according to “organism part” Return a summary of all experiments done examining effects of a specified treatment –E.g., Look for “CompoundBasedTreatment”, “in vivo” –Select “Compound” based on CAS registry number –Order based on “CompoundMeasurement” ? Use to check if “MIAME-compliant.” –Assess only fields that are relevant –Check for proper use of terms ? Build gene networks based on biomaterial description –Generate a distance metric based on biosource and use in calculation of correlation with gene expression level –Generate an error estimation based on biosample (i.e., even when biosources are identical, there will be variation resulting from different treatments)

28. MGED Ontology Plans More Concepts? Improve definitions? –Extend to other parts of MIAME More instances! Add identifiers to all classes (facilitate neutral authoring). Instances? Add constraints. Prevent nonsense associations (e.g., only time units for age) Write a paper describing and explaining MGED ontology by next meeting with example applications and datasets. –Mechanism to establish a consensus “standard.”