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

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

EpoDB “Gene Ontology”

http://www.cbil.upenn.edu/EpoDB

Stoeckert, Salas, Brunk, Overton (1999) Nucl. Acids Res. 26:288

EpoDB Gene Landmark Query

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

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

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)

Microarray Information to be Captured

Figure from:David J. Duggan et al. (1999) Expression Profiling using cDNA microarrays. Nature Genetics 21: 10-14

Tables Describing Samples in RAD (RNA Abundance Database)

Experiment

ExpGroupsGroups RelExperiments

Exp.ControlGenes

ControlGenes

HybridizationConditions

Label

Sample

TreatmentDiseaseDevel. Stage

ExperimentSample

Taxon

Anatomy

CBIL Anatomy Hierarchy

Anatomy Table Used by RAD

Usage of Anatomy Hierarchy to Query RAD

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

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)

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

Relationship of MGED Efforts

MAGEMIAMEDB

MIAMEDBExternal

Ontologies/CVs

MGED Ontology

AnnotationFormatOntologies External Internal

Ontologies provide common terms and their definitions for describing microarray experiments.

MGED Ontology Working Group Goals

1. Identify concepts

2. Collect available controlled vocabularies and ontologies for concepts

3. Define concepts

4. Formalize concept relationships

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

SpeciesResources

ConceptDefinitions

MGED Ontology Working Group Goals

1. Identify concepts

2. Collect available controlled vocabularies and ontologies for concepts

3. Define concepts

4. Formalize concept relationships

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

sample source and treatment ID as used in section 1organism (NCBI taxonomy)additional "qualifier, value, source" list; the list includes:

cell source and type (if derived from primary sources (s))sexagegrowth conditionsdevelopment stageorganism part (tissue)animal/plant strain or linegenetic variation (e.g., gene knockout, transgenic variation)individualindividual genetic characteristics (e.g., disease alleles, polymorphisms)disease state or normaltarget cell typecell 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)compoundis 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

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 environment20 - 22 oC average temperaturehoused in cages according to German and EU legislationspecified pathogen free conditions (SPF)14 hours light cycle10 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 mouseCompound: drug [MGED] synthetic glucocorticoid Dexamethasone, dissolved in PBS

MGED Ontology Working Group Goals

1. Identify concepts

2. Collect available controlled vocabularies and ontologies for concepts

3. Define concepts

4. Formalize concept relationships

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

MAGE BioMaterial Model

Building a Microarray Ontology

http://www.cbil.upenn.edu/Ontology/Build_Ontology2.html

Ontology Available as RDFS

Ontology in Browseable Form

Example of Internal Terms

Example of External Terms

Example of Combined Internal and External: Treatment

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)

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

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

Acknowledgements

• Past and present members of CBIL for their work on EpoDB and RAD

• 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