Open Access Open Badges Commentary

MIAME/Plant – adding value to plant microarrray experiments

Philip Zimmermann1*, Beatrice Schildknecht2, David Craigon2, Margarita Garcia-Hernandez3, Wilhelm Gruissem1, Sean May2, Gaurab Mukherjee4, Helen Parkinson4, Seung Rhee, Ulrich Wagner1 and Lars Hennig1

  • * Corresponding author: Philip Zimmermann

Author Affiliations

1 Swiss Federal Institute of Technology – ETH Zurich, ETH Center, CH-8092 Zurich, Switzerland

2 The Nottingham Arabidopsis Stock Center (NASC), Division of Plant Sciences, University of Nottingham, Sutton Bonington LE12 5RD, UK

3 The Arabidopsis Information Resource (TAIR), Carnegie Institution of Washington, 260 Panama St, Stanford, CA 94305, USA

4 European Bioinformatics Institute (EBI), European Bioinformatics Institute, EMBL outstation, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK

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Plant Methods 2006, 2:1  doi:10.1186/1746-4811-2-1

Published: 9 January 2006


Appropriate biological interpretation of microarray data calls for relevant experimental annotation. The widely accepted MIAME guidelines provide a generic, organism-independant standard for minimal information about microarray experiments. In its overall structure, MIAME is very general and specifications cover mostly technical aspects, while relevant organism-specific information useful to understand the underlying experiments is largely missing. If plant biologists want to use results from published microarray experiments, they need detailed information about biological aspects, such as growth conditions, harvesting time or harvested organ(s). Here, we propose MIAME/Plant, a standard describing which biological details to be captured for describing microarray experiments involving plants. We expect that a more detailed and more systematic annotation of microarray experiments will greatly increase the use of transcriptome data sets for the scientific community. The power and value of systematic annotation of microarray data is convincingly demonstrated by data warehouses such as Genevestigator® or NASCArrays, and better experimental annotation will make these applications even more powerful.