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MGED Ontology: An Ontology of Biomaterial Descriptions for Microarrays

Explore the utilization of gene ontology in EpoDB system, understand MGED group's efforts in setting standards for microarray data representation, and delve into the role of ontology in capturing microarray information. Learn about ontology types and how they enhance data analysis.

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MGED Ontology: An Ontology of Biomaterial Descriptions for Microarrays

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  1. MGED Ontology:An Ontology of Biomaterial Descriptions for Microarrays Microarray Data Analysis and Management: Bio-ontologies for Microarrays EMBL-EBI, Hinxton, Cambridge, UK Dec. 5, 2001 Chris Stoeckert, U. Penn

  2. Ontology Usage for Genes in EpoDB • EpoDB is a prototype system of genes expressed during erythropoiesis • Built before microarrays were readily available • Illustrate usage of an ontology of gene parts and controlled vocabularies of gene (and gene family) names

  3. http://www.cbil.upenn.edu/EpoDB Stoeckert, Salas, Brunk, Overton (1999) Nucl. Acids Res. 26:288

  4. EpoDB “Gene Ontology”

  5. EpoDB Gene Landmark Query

  6. Standardisation of Microarray Data and Annotations -MGED Group The MGED group is a grass roots movement initially established at the Microarray Gene Expression Database meeting MGED 1 (14-15 November, 1999, Cambridge, UK). The goal of the group is to facilitate the adoption of standards for DNA-array experiment annotation and data representation, as well as the introduction of standard experimental controls and data normalisation methods. Members are from around the world in academia, government, and industry. http://www.mged.org

  7. MGED Working Groups • Annotation: Experiment description and data representation standards (Alvis Brazma, EMBL-EBI) • Format: Microarray data XML exchange format (Paul Spellman, UC Berkeley) • Ontology: Ontologies for sample description (Chris Stoeckert, U Penn) • Normalization: Normalization, quality control and cross-platform comparison (Gavin Sherlock, Stanford U)

  8. MGED Documents • Annotation -> Minimal Information About a Microarray Experiment (MIAME) • What should go into a microarray database • Brazma et al. Nature Genetics 29:365-371, 2001 • Format -> Microarray Gene Expression (MAGE) Object Model and XML DTD • How microarray databases will talk to each other

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

  10. What is an ontology?(In the computer science not philosophy sense) • An ontology is a specification of concepts that includes the relationships between those concepts. • Removes ambiguity. Provides semantics and constraints. • Allows for computational inferences and reliable comparisons

  11. Types of Ontologies • Taxonomy • Tree structure. IS-A hierachy • Variants - Gene Ontology (DAG) • Frame-based (object-oriented) • Classes and attributes • EcoCyc • Description logic (DL) • Reasoning about concept (class) relationships • Combine terms with constraints (sanctioning) • GRAIL (GALEN, TAMBIS) • Ontology Inference Layer (OIL) • Combines Frames and DLs • Uses Web standards XML and RDF

  12. Taxonomy • Terms for common usage • Homo sapiens, not human, not homo sapeins • NCBI ID = 9606 • Hierarchy provides unambiguous levels of equivalence • Homo sapiens and Mus musculus are of the class Mammalia but Drosophila melanogaster is not. • Can use taxonomic hierarchies for other types of information • e.g., Human Developmental Anatomy (U. of Edinburgh)

  13. CBIL Anatomy Hierarchy

  14. Microarray Information to be Captured Figure from: David J. Duggan et al. (1999)Expression Profiling using cDNA microarrays. Nature Genetics21: 10-14

  15. Devel. Stage Disease Treatment Taxon Sample Anatomy Label ExperimentSample Exp.ControlGenes Hybridization Conditions ControlGenes Groups ExpGroups RelExperiments Tables Describing Samples in RAD (RNA Abundance Database) Experiment

  16. Anatomy Table Used by RAD

  17. Usage of Anatomy Hierarchy to Query RAD

  18. MGED Ontology Working Group Goals • Identify concepts • Collect available controlled vocabularies and ontologies for concepts • Define concepts • Formalize concept relationships

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

  20. Species Resources

  21. Concept Definitions

  22. MGED Ontology Working Group Goals • Identify concepts • Collect available controlled vocabularies and ontologies for concepts • Define concepts • Formalize concept relationships

  23. 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

  24. MIAME Section on Sample Source and Treatment • 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

  25. Excerpts from a Sample Descriptioncourtesy of M. Hoffman, S. Schmidtke, Lion BioSciences • Organism: mus musculus [ NCBI taxonomy browser ] • Cell source: in-house bred mice (contact: norma.howells@itg.fzk.de) • Sex: female [ MGED ] • Age: 3 - 4 weeks after birth [ MGED ] • Growth conditions: normal • controlled environment • 20 - 22 oC average temperature • housed in cages according to German and EU legislation • specified pathogen free conditions (SPF) • 14 hours light cycle • 10 hours dark cycle • Developmental stage: stage 28(juvenile (young) mice) [ GXD "Mouse Anatomical Dictionary" ] • Organism part: thymus [ GXD "Mouse Anatomical Dictionary" ] • Strain or line: C57BL/6 [International Committee on Standardized Genetic Nomenclature for Mice] • Genetic Variation: Inbr (J) 150. Origin: substrains 6 and 10 were separated prior to 1937. This substrain is now probably the most widely used of all inbred strains. Substrain 6 and 10 differ at the H9, Igh2 and Lv loci. Maint. by J,N, Ola. [International Committee on Standardized Genetic Nomenclature for Mice ] • Treatment: in vivo [MGED] intraperitoneal injection of Dexamethasone into mice, 10 microgram per 25 g bodyweight of the mouse • Compound: drug [MGED] synthetic glucocorticoid Dexamethasone, dissolved in PBS

  26. MGED Ontology Working Group Goals • Identify concepts • Collect available controlled vocabularies and ontologies for concepts • Define concepts • Formalize concept relationships

  27. 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

  28. MAGE BioMaterial Model

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

  30. Ontology Available as RDFS

  31. Ontology in Browseable Form

  32. Example of Internal Terms

  33. Example of External Terms

  34. Example of Combined Internal and External: Treatment

  35. OWG Use Cases • 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” • 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)

  36. Ontology Working Group Highlights • First pass ontology of biomaterial descriptions • Participated in Bio-ontologies Consortium Meeting at ISMB 2001. • Mail list of about 200 subscribers

  37. Ontology Working Group Plans • Finish building biomaterial description ontology • Expand efforts to include remaining parts of a microarray experiment • Demonstrate usage to the microarray community

  38. Acknowledgements • The members of the MGED Ontology Working Group for their contributions • The Bio-Ontologies Consortium for encouragement and guidance • This presentation is available at http://www.cbil.upenn.edu/Ontology/MGEDOntology1201.ppt

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