mged ontology: an ontology of biomaterial descriptions for microarrays
DESCRIPTION
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. - PowerPoint PPT PresentationTRANSCRIPT
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: [email protected]) 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