Generating Useful Information in Toxicogenomics: Focused Efforts: Microarray Standards Feb. 6, 2003, The National Academies Chris Stoeckert, Ph.D. Center.

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Presentation transcript:

Generating Useful Information in Toxicogenomics: Focused Efforts: Microarray Standards Feb. 6, 2003, The National Academies Chris Stoeckert, Ph.D. Center for Bioinformatics University of Pennsylvania

Microarray Standards Are Needed To Facilitate Moving Data Around Stoeckert, Causton, & Ball Nature Genetics 2002

Standards will Aid Data Integration in Toxicogenomics Efforts EHP Toxicogenomics 2003

Microarray Standards Exist... …And You Are Being Asked To Use Them. … also being adopted by Cell and The Lancet - others to follow…

Microarray Standards Are A Good Thing Because… You won’t have to take people’s results on faith anymore. Get the full dataset and annotations in a form you can use. You will be able to get useful data from public repositories You won’t have to worry about some supplementary web site going away …but it is going to take some effort on your part to comply! Need to learn the standards and use them!

MGED Board of Directors Alvis Brazma - EBI Michael Miller - Rosetta Inpharmatics Paul Spellman - LBL Charles Troup - Agilent Technologies Barbara Jasny - Science Magazine Chris Stoeckert - University of Pennsylvania Gavin Sherlock - Stanford University Helen Causton - MRC Helen Parkinson - EBI Jason Goncalves - Iobion Jason Stewart - Open Informatics John Quackenbush - TIGR Martin Ringwald - MGI Pascal Hingamp - University of Marseilles Ronald Taylor - UCHSC Terry Gaasterland - Rockefeller Yoshio Tateno - NIG Catherine Ball - Stanford University

Goals of MGED Efforts Elucidate information needed for experiments MIAME Provide a means to share this information MAGE Provide a common language for experiments MGED Ontology Provide standard operating procedures for analysis Data transformation

MGED Working Groups MIAME: Defining data standards Minimal Information About a Microarray Experiment Checklist MAGE: Establishing data formats MicroArray Gene Expression Object model implemented as XML Ontologies: Creating ways to describe data Defined concepts and relationships Terms for annotating experiments Data Transformation: Describing methods of normalization and analysis

MIAME: Information Required to Interpret and Replicate Experimental Design Array Design Biological Samples Hybridizations Measurements Data Normalization and Transformation Brazma et al. Nature Genetics 2001

MIAME Checklist Available from:

Call to Deposit Microarray Experiments in Public Repository Who: Journals: Nature, Genome Res., others Granting agencies: NIH Where: ArrayExpress (EBI) GEO (NCBI) CIBEX (DDBJ, under development) How: MAGE MIAME-compliant systems: MIAMExpress, RAD See also:

MAGE-ML Should Be Used To Transfer Data MAGE-ML is automatically generated as an XML DTD from the MAGE Object Model (MAGE-OM) MAGE-OM represents a data driven model of microarray experiments MAGE was developed following OMG specifications (OMG is an industrial standards group) Spellman et al. Genome Biology 2002 Gene Expression RFP (OMG document) Look for latest GE file under OMG Model at: 076

The MAGE-ML Packages Audit and Security Description BQS BioSequence ArrayDesign * DesignElement Array BioEvent BioMaterial * BioAssay* BioAssayData Experiment * HigherLevelAnalysis * Measurement * Protocol QuantitationType * Correspond to MIAME sections. Note that MAGE is designed to hold MIAME and more.

MAGE UML Model for Experiment Package

MGED Core Ontology Provide defined terms for MAGE v 1.0 documents Controlled vocabularies Complex terms like Age (measurement, initial time point) External resources for terms Built from MGED Ontology v 1.6 Built with input by Ontology Working Group Ontology workshops at U Penn with EBI, Stanford, TIGR Will be extended. Improve limited structure of MAGE 1.0 Cover functional genomics in general Stoeckert & Parkinson, Comparative and Functional Genomics 2003

MGED Ontology is in DAML

ArrayExpress MIAMExpress RAD MAGE-ML data exchange Ontology instances propagated to submission/annotation web forms Curation of user defined terms, before inclusion in the ontology User defined terms collected via forms BioMaterialPackage BioMaterialCharacteristics Sex C C C C Gender documentation: Subclass of sex applicable to heterogametic species (i.e., those in which the sexes produce gametes of markedly different size). Males produce small numerous gametes. Females produce small numbers of large gametes. Hermaphrodites are individuals with both male and female characteristics. Mixed refers to a population of individuals with more than one type of gender. used in individuals: female, hermaphrodite,male,mixed_sex,unknown_sex

MGED Promotes Other Standards Efforts in Functional Genomics Standards and Ontologies in Functional Genomics (SOFG) A multitude of overlapping standards can contribute to the problem First meeting was held Nov at the Wellcome Trust Campus, Hinxton, UK Proteomics HUPO Proteomics Standards Initiative Protein-protein interactions Mass spectrometry

Join the MGED Effort in Microarray Standards Standards are driven by the communities who are willing to help develop them. Go to meetings where tutorials are held AAAS, Denver Feb, 2003 MGED 6, Aix-Provence Sept Join discussion lists Communicate with the people who are working on the standards Have the opportunity to contribute Visit

Thank you!

Active MAGE Community Exists MAGE discussion list Links from MAGE Jamborees and tutorials Programming Jamboree IV: Stanford Dec 6-10, 2002 MAGEstk (software tool kit) Freely available software collection (MIT open source license) Includes MAGE-ML Parsers for: Perl -- Software complete and functional Java -- Very nearly functional C months to completion Data annotation tool (MIAME Express)