1. Sharing Microarray Experiment Knowledge Chips to Hits Oct. 28, 2002 Chris Stoeckert, Ph.D. Dept. of Genetics & Center for Bioinformatics University of Pennsylvania

2. Nature, October 3, 2002 http://plasmodb.org/ David Roos, Jessie Kissinger, Bindu Gajria, Martin Fraunholz, Jules Milgram, Phil Labo, Amit Bahl, Dave Pearson, Dinesh Gupta, Hagai Ginsburg Jonathan Crabtree, Jonathan Schug, Brian Brunk, Greg Grant, Trish Whetzel, Matt Mailman, Li Li

4. Desirable Microarray Queries Return all experiments using developmental stage X. –Sort by platform type –Which are untreated? Treated? Treated by what How comparable are these? What can these experiments tell me?

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

6. The Computational View of Microarray Information Need an ontology to unambiguously represent this information.

7. What is an Ontology? In philosophy, an ontology is a systematic account of Existence. In AI, an ontology is a systematic account of what can be represented. The knowledge of a domain is represented in a declarative formalism. –Classes, relations, functions, or other objects are defined with human-readable text describing what the names mean, and formal axioms that constrain the interpretation. A common ontology defines the vocabulary with which queries and assertions are exchanged. Excerpted and adapted from: http://www-ksl.stanford.edu/kst/what-is-an-ontology.html

8. An Experimental Ontology An ontology for microarray experiments –Not an ontology of life but of experiments –Parts are applicable to describing experiments in general Our approach to interfacing with other ontologies is “experimental” –Not mapping terms from related ontologies –Provide a framework to hang other ontologies off of Know where to find different types of annotation How to interpret that annotation

9. http://www.mged.org

10. Relationship of MGED Efforts MAGE MIAME DB MIAME DB External Ontologies/CVs MGED Ontology Software and database developers Investigators annotating experiments

11. The MGED Ontology Home Page http://www.cbil.upenn.edu/Ontology

12. The MGED Ontology Home Page http://mged.sourceforge.net/ontologies/

13. The MGED Ontology Provides a Listing of Resources for Many Species

14. The MGED Ontology Organizes the Resources According to Concepts

15. The MGED Ontology is Structured in DAML+OIL using OILed 3.4

16. MGED Ontology: BiomaterialDescription: BiosourceProperty: Age

17. MGED Ontology: BiosourceOntologyEntry: DiseaseState

18. 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

19. The MGED Ontology in Action: MIAMExpress

20. Journals are Adopting the MGED Standards Use of Minimal Information About Microarray Experiment (MIAME)

21. The MGED Ontology in Action: RAD

23. Generating Forms from the MGED Ontology OntologyEntry ExternalDatabases PHP/SQL WWW RAD Forms MGED Ontology Anatomy DevelopmentalStage Disease Lineage PATOAttribute Phenotype Taxon SRES RAD3 MGED Ontology

24. Using the MGED standards in RAD RAD: RNA Abundance Database –Stoeckert et al.(2000) Bioinformatics RAD 3.0 –MIAME compliant and MAGE supportive –Building Importers, exporters for MAGE Incorporates MGED ontology –Uses OntologyEntry to point to internal tables and external resources Expand processing and analysis information storage –Driven by experience and new approaches

25. RAD schema uses MAGE/MIAME MAGE Experiment Array BioMaterial BioAssay BioAssayData Protocol, Descr. HigherLevelAnalysis MAGE Experiment Array BioMaterial BioAssay BioAssayData Protocol, Descr. HigherLevelAnalysis MIAME Experimental Design Array design Samples Hybridization, Measure Normalization. MIAME Experimental Design Array design Samples Hybridization, Measure Normalization.

26. RAD is now part of GUS-3.0 GUS has 5 name spaces compartmentalizing different types of information. NamespaceDomainFeatures CoreData ProvenanceWorkflows SresShared resorurcesOntologies DoTS sequence and annotation Central dogma RADGene expresssionMIAME/MAGE TESSGene regulationGrammars

27. Data Integration GO Species Tissue Dev. Stage Ontologies SRes acute myeloid leukemia Data Provenance Ownership Protection Algorithms Similarity Versioning Workflow Core with sequence similarity to c-fos Genomic Sequence Genes, gene models STSs, repeats, etc Cross-species analysis Transcribed Sequence Characterize transcripts RH mapping Library analysis Cross-species analysis DOTS Protein Sequence Domains Function Structure Cross-species analysis DoTS Transcription factors Arrays SAGE Conditions Transcript Expression RAD up-regulated in Binding Sites Patterns Grammars Gene Regulation TESS and common promoter motifs

28. GUS Supports Multiple Projects AllGenes PlasmoDB EPConDB CoreSRESTESSRADDoTS Oracle RDBMS Object Layer for Data Loading Java Servlets Other sites, Other projects, e.g. GeneDB Other sites, Other projects, e.g. GeneDB Available at http://www.gusdb.org

29. Summary The MGED ontology is being developed within the microarray community to provide consistent terminology for experiments. –Make it easier and more accurate to annotate a microarray experiment. –Use structured fields and controlled terms to query databases. This community effort has resulted in a list of multiple resources for many species and a machine-readable document of microarray concepts, definitions, and values. –The MGED Ontology is a work in progress but can be used now to build forms for databases RAD has incorporated the MGED ontology for forms –Can export data from RAD into MAGE –RAD as part of GUS provides integration of gene expression, annotation, and sequence.

30. Acknowledgements MGED Ontology –Helen Parkinson (EBI) –Trish Whetzel –The MGED Ontology Working Group –MAGE working group RAD/GUS –Brian Brunk –Jonathan Crabtree –Steve Fischer –Yongchang Gan –Greg Grant –Hongxian He –Li Li –Junmin Liu –Matt Mailman –Elizabetta Manduchi –Joan Mazzarelli –Shannon McWeeney (OHSU) –Debbie Pinney –Angel Pizarro –Jonathan Schug –Trish Whetzel www.mged.org www.cbil.upenn.edu

31. http://www.ebi.ac.uk/SOFG