# Presentation: 066985

## ArrayExpress – a public database for microarray gene expression data

- Helen Parkinson
- Microarray Informatics Team
- European Bioinformatics Institute
- Transcriptome 2002, Seattle

**Notes:**
EBI, centre for bioinformatics, European and worldwide links for microarray data

## Why have a public database?

- Easy data access
- Resolves local storage issues
- Common data exchange formats can be developed
- Improved data comparison
- Curation can be applied
- Annotation can be controlled
- So that a public standard can be applied (peer review) – MIAME
- Additional info can be stored that is missing in publications

## Or, to put it another way

- “...to encourage and empower biologists to provide results in a structured and computable format alongside publication”
-                              Mark Boguski

## 
MIAME standard
Sample description and annotation Ontologies
ArrayExpress
Submission and annotation tool
The future

- MIAME standard
- Sample description and annotation Ontologies
- ArrayExpress
- Submission and annotation tool
- The future

- Talk structure

## Problems of microarray dataanalysis

- Size of the datasets
- Different platforms - nylon, glass
- Different technologies on platforms- oligo/spotted
- Referencing external databases which are not stable
- Sample annotation
- Array annotation
- Need for LIMS systems and the need for bioinformaticians

## Standardisation of microarray data and annotations -MGED group

- 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. 	           www.mged.org
- Includes most of the worlds largest microarray laboratories and companies (TIGR,Affymetrix,Stanford,Sanger,Agilent, Rosetta, etc)

## Glossary

- MIAME is a standard
- MAGE-OM is an object model
- ArrayExpress is a database implementation which uses that model
- MAGE-ML is a mark-up language auto generated from MAGE-OM
- MIAMExpress is a tool for generating data in MAGE-ML format

## General MIAME principles

- Recorded info should be sufficient to interpret and replicate the experiment
- Information should be structured so that querying and automated data analysis and mining are feasible
- Brazma *et al,*.Nature Genetics, 2001

## MIAME – Minimum Information About a Microarray Experiment

- Publication

**External links**

**6 parts of a **
**microarray **
**experiment **

- _[www.mged.org](http://www.mged.org/)_

**Notes:**
This is how MIAME is organised

## The annotation challenge

- Use of controlled terms
- Data curation at source (LIMS)
- Avoidance of free text
- Integration of terms into query interfaces
- Removal of synonyms/or use of synonym mappings
- Provision of definitions and sources

## A gene expression database from the data analyst’s point of view

- Samples

- Genes

- Gene expression levels

- Gene expression matrix

## Gene Annotation

- Can be given by links to gene sequence databases and GO can be used on the analysis side
- MIAME is flexible, allows many kinds of sequence identifiers or even sequence itself
- In some cases it’s more useful to include a real sequence than an inaccurate id
- Submitters are encourage to submit seqs to public databases

## Sample annotation

- Gene expression data only have meaning in the context of detailed sample descriptions
- If the data is going to be interpreted by independent parties, sample information has to be searchable and in the database
- Controlled vocabularies and ontologies (species, cell types, compound nomenclature, treatments, etc) are needed for unambiguous sample description
- These resources need mapping

**Notes:**
Adding sample data to databases is new, EMBL doesn’t do this, it refers users to the publications, we need to do this is this data is being made public now and people want to compare between experiments  

Querying EMBL on anything other than Accession number can be problematic due to use of free text everywhere, also a huge curation workload

## What Does an Ontology Do?

- Captures knowledge
- Creates a shared understanding – between humans and for computers
- Makes knowledge machine processable
- Makes meaning explicit – by definition and context
- It is more than a controlled vocabulary

*From Building and Using Ontologies, Robert Stevens, U. of Manchester*

## Examples of usable external ontologies

- NCBI taxonomy database
- Jackson Lab mouse strains and genes
- Edinburgh mouse atlas anatomy
- HUGO nomenclature for Human genes
- Chemical and compound Ontologies
- TAIR
- Flybase
- GO (www.geneontology.org)

## Sample annotation- what can be done?

- Build an ontology for gene expression data (MGED)
- Incorporate the ontology into the database and tools
- Use existing ontologies
- Develop internal editing tools for the ontology
- Develop browser or other interface for the ontology and link to LIMS
- Some use of free text descriptions are unavoidable (curation workload)

**Notes:**
Model organism databases leading they way on this esp flybase

## MGED Biomaterial (sample) Ontology

- Under construction – by MGED ontologists
- Using OILed (though other tools exist)
- Motivated by MIAME and coordinated with the database model (mapping available)
- We are extending classes, provide constraints, define terms, provide new terms and develop cv’s for submissions
- Other ontologies are under development, MAGE-OM ref’s ontologies in ~50 places, these are being added to the MGED effort

**Notes:**
Part of the larger bioontologies group, which includes GO, which is coordinated from the EBI and others interested in the bio computing aspects of ontologies

## Excerpts from a Sample Descriptioncourtesy of M. Hoffman, Lion BioSciences

**Organism****: Mus musculus **[ NCBI taxonomy browser ]
**Cell source****: in-house bred mice (contact: person@somewhere.ac.uk) **
**Sex****: female [**** MGED ****]**
**Age: 3 - 4 weeks after birth **[ **MGED**** **]
**Growth conditions****: normal **
**controlled environment**
**20 - 22 ****o****C average temperature**
**housed in cages according to 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 **

**Notes:**
This is a good example of a MIAME compliant expt with externally referenced ontologies. But we still have a problem in inputing this data through a submission tool as this is very complex and takes a while to fill in, and anything that takes too long is not goong to be used, there needs to be a clear benefit for users for them to participate in this

## Part of the MGED biomaterial ontology

- class Age
- documentation:
-     The time period elapsed since an identifiable point in the life cycle of an organism. If a developmental stage is specified, the identifiable point would be the beginning of that stage. Otherwise the identifiable point must be specified such as planting.
- type:
-     primitive
- superclasses:
-     BiosourceProperty
- constraints:
-     slot-constraint has_measurement has-value Measurementslot-constraint initial_time_point has-value one-of (planting
-     beginning_of_stage)
- used in slots:
-     initial_time_point

## ArrayExpress

- Is an implementation of the MAGE-OM model
- MAGE-OM has been accepted by the OMG as a biosciences standard
- MAGE-OM is a platform independent model developed in UML

## ArrayExpress conceptual model

- Publication

**External links**

- Normalisation

## Simplified ArrayExpress model

## ArrayExpress details

- Database schema derived from MAGE-OM
- Standard SQL, we use Oracle
- Validating data loader for MAGE-ML - generated
- Web interface  (first release 12.2.2002)
- Queries - experiment, array, sample
- Browsing – views on expt
- Object model-based query mechanism, automatic mapping to SQL

## Data in ArrayExpress

- Human data (ironchip) from EMBL
- Yeast data from EMBL
- S. pombe data Sanger Institute
- Available as example annotated and curated data sets

- TIGR array descriptions and data
- Affymetrix array designs
- Direct pipeline from Sanger Institute database
- HGMP mouse data
- EMBL Anopheles data

- Near future -

- Currently-

## ArrayExpress - queries

## Expression Profiler

- EPCLUST
-  Expression data

- GENOMES
- sequence, function,
-  annotation

- SPEXS
- discover patterns

- URLMAP
- provide links

- http://ep.ebi.ac.uk/

- Expression data

- External data, tools
- pathways, function,
- etc.

- PATMATCH
- visualise patterns

- EP:GO
- GeneOntology

- EP:PPI
- Prot-Prot ia.

- SEQLOGO

## ArrayExpress curation effort

- User support and help documentation
- Curation at source (not destination)
- Support on ontologies and CV’s
- Minimize free text, removal of synonyms
- MIAME encouragement
- Help on MAGE-ML
**Goal:** to provide high-quality, well-annotated data to allow automated data analysis

## Data Submission routes

- Via MAGE-ML generated from a local database,  (array, protocol and experiment submissions)
- Via MIAMExpress, a MIAME compliant data annotation tool (array, protocol and experiment submissions)

## MIAMExpress submission and annotation tool

- Based on MIAME concepts and questionnaire
- Experiment, Array, Protocol submissions
- Uses CV/Ontology wherever possible
- Future versions organism specific pages and related linked ontologies
- Allows user driven ontology development
- Will be developed according to user needs
- Can be used  as an update tool
- Can be used as basis of LIMS

## Slide 30

## Expected Users

- Users with limited local bioinformatics support
- Users of bought in arrays without LIMS
- Small scale users with self made arrays who will need to provide a description
- Commercial array descriptions will be provided

## MIAMExpress future developments

- Species and domain specific pages and ontologies, ontology development
- Life-span of data submissions is long
- Integrated curation control, submissions tracking
- Full compatibility with ArrayExpress
- Full MAGE-OM, data updating
- Usability, flexibility, scalability, platform independence
- User needs, free in-house installation

##  ArrayExpress Future

- Loading of public data in MAGE-ML format (TIGR, EMBL, DESPRAD partners) into ArrayExpress
- V2.0 MIAMExpress, the KeyLargoExpress
- Improved query interfaces
- Further ontology development and integration into tools
- Curation tools
- Join MGED _[www.mged.org](http://www.mged.org/)_

## Resources

- Schemas for both ArrayExpress and MIAMExpress, access to code
- Annotation examples in MAGE-ML
- MIAME glossary, MAGE-MIAME-ontology mappings
- List of ontology resources from MGED pages
- MAGE-OM tutorials at MGED meetings
- MAGE-OM support for submitters from EBI
- MAGE-stk API’s
- _[www.mged.org](http://www.mged.org/)_ _[www.ebi.ac.uk/microarray](http://www.ebi.ac.uk/microarray)_

## Acknowledgments 

- Microarray Informatics Team, EBI
- Chris Stoeckert, U. Penn.
- Members of MGED
- Sanger Institute - Rob Andrews,     Jurg Bahler, Adam Butler, Kate Rice,
- EMBL Heidelberg - Wilhelm Ansorge, Martina Muckenthaler,Thomas Preiss