PHENOPSIS DB: an Information System for Arabidopsis thaliana phenotypic data in an environmental context

PHENOPSIS DB is a publicly available (URL: http://bioweb.supagro.inra.fr/phenopsis/ webcite) information system developed for storage, browsing and sharing of online data generated by the PHENOPSIS platform and offline data collected by experimenters and experimental metadata. It provides modules coupled to a Web interface for (i) the visualisation of environmental data of an experiment, (ii) the visualisation and statistical analysis of phenotypic data, and (iii) the analysis of Arabidopsis thaliana plant images.Firstly, data stored in the PHENOPSIS DB are of interest to the Arabidopsis thaliana community, particularly in allowing phenotypic meta-analyses directly linked to environmental conditions on which publications are still scarce. Secondly, data or image analysis modules can be downloaded from the Web interface for direct usage or as the basis for modifications according to new requirements. Finally, the structure of PHENOPSIS DB provides a useful template for the development of other similar databases related to genotype × environment interactions.Arabidopsis thaliana, a small flowering plant with a rapid life cycle, offers important advantages for researches in genetics and molecular biology. Since 2000, the complete sequencing of its genome has enabled scientists to monitor gene expression on a genome-scale [1] in different organs and in different environmental conditions [e.g. [2,3]]. The broad-based knowledge of this plant includes extensive genetic maps of all five chromosomes, efficient technology for mutagenesis and transformation and a large range of biological resources available at the various Arabidopsis stock centers (Arabidopsis Biological Resource Center, Nottingham Arabidopsis Stock Center, Riken Bioresource Center, INRA-Versailles Genomic Resource Center and Lehle Seeds, a private company). Many structured databases and querying tools have been developed providing repositories of large datasets and efficient applications for the determinatio