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.
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.

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: , 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 Frame-based (object-oriented)
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
Debate on what’s an ontology - not address that here
Nor a complete primer on ontologies

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 Genetics 21: 10-14

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

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

20. Species
Resources

22. Concept
Definitions

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

25. 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
What can be done without structured concepts

26. 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

27. Excerpts from a Sample Description courtesy of M. Hoffman, S
Excerpts from a Sample Description courtesy of M. Hoffman, S. Schmidtke, Lion BioSciences
Organism: mus musculus [ NCBI taxonomy browser ]
Cell source: in-house bred mice (contact:
Sex: female [ MGED ]
Age: weeks after birth [ MGED ]
Growth conditions: normal
controlled environment
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 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
Use for MIAME qualifier, value, source triplet
Use for MAGE ontology entry

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

29. 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

30. MAGE BioMaterial Model

31. Building a Microarray Ontology

32. Ontology Available as RDFS

33. Ontology in Browseable Form

34. Example of Internal Terms

35. Example of External Terms

36. Example of Combined Internal and External: Treatment

37. 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)

38. 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

39. 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

40. 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