1. MIAME and Data Standards Phillip Lord

2. Why Standards? "However, there is a subtle implication that standardization (fixation) is a good thing". An anonymous reviewer

3. Why Standards? The author is wrong in implying such subtly. Standardisation is a throughly good thing. Of course, this does not imply that all standards are good standards, nor does it imply that standards cannot be over ambitious in trying to achieve too much to (sic) early.

4. Why (not) standards? Give three reasons why to use data standards…. and three reasons why not… one per post-it note

5. Why Data Standards Enables efficient sharing of data (mostly) required for setting up a public repository, which stores historical information. Helps standardise experimental design (people will build experiments to fit the standard).

6. Why not Data Standards Tends to be expensive (ArrayExpress was initially costed at 500,000 Euro). Can produce fixed representation too early. Filling in the forms is painful

7. Original Objectives for MGED Store data as close to “raw” as possible Develop standard sets of control probes Describe experimental design with “mixture of the formal and pragmatic”, producing a “flexible data model that can evolve with our understanding”. Brazma et al (2000)

8. MIAMI “Minimal Information About a Microarray Experiment” seeks to define what information is needed to enable another researcher to understand the experiment

9. MAGE Defines MAGE-OM – a standard object representation of the data Defines MAGE-ML – a standard XML schema for transfering the data.

10. MGED Ontology (MO) Defines a controlled vocabulary describing aspects of an experimental sample.

11. Term Gathering Exercise Again, write down three terms describing some aspect of microarray experiment design. Each term should be on one post-it note Keep them short!

12. Six Big Boxes 1.Experimental Design 2.Array Design 3.Samples 4.Hybridisations 5.Measurements 6.Normalisation Controls

13. Comparison Find equivalent terms in the MIAME checklist.

14. Minimum information about a microarray experiment (MIAME)—toward standards for microarray data. Brazma et al (2001) Nature Genetics 29:365-371