Dealing with multi-source and multi-scale information in plant phenomics: the ontology-driven Phenotyping Hybrid Information System.

Phenomic datasets need to be accessible to the scientific community. Their reanalysis requires tracing relevant information on thousands of plants, sensors and events. The open-source Phenotyping Hybrid Information System (PHIS) is proposed for plant phenotyping experiments in various categories of installations (field, glasshouse). It unambiguously identifies all objects and traits in an experiment and establishes their relations via ontologies and semantics that apply to both field and controlled conditions. For instance, the genotype is declared for a plant or plot and is associated with all objects related to it. Events such as successive plant positions, anomalies and annotations are associated with objects so they can be easily retrieved. Its ontology-driven architecture is a powerful tool for integrating and managing data from multiple experiments and platforms, for creating relationships between objects and enriching datasets with knowledge and metadata. It interoperates with external resources via web services, thereby allowing data integration into other systems; for example, modelling platforms or external databases. It has the potential for rapid diffusion because of its ability to integrate, manage and visualize multi-source and multi-scale data, but also because it is based on 10 yr of trial and error in our groups.

PHENOPSIS DB: an information system for Arabidopsis thaliana phenotypic data in an environmental context.

BACKGROUND: Renewed interest in plant×environment interactions has risen in the post-genomic era. In this context, high-throughput phenotyping platforms have been developed to create reproducible environmental scenarios in which the phenotypic responses of multiple genotypes can be analysed in a reproducible way. These platforms benefit hugely from the development of suitable databases for storage, sharing and analysis of the large amount of data collected. In the model plant Arabidopsis thaliana, most databases available to the scientific community contain data related to genetic and molecular biology and are characterised by an inadequacy in the description of plant developmental stages and experimental metadata such as environmental conditions. Our goal was to develop a comprehensive information system for sharing of the data collected in PHENOPSIS, an automated platform for Arabidopsis thaliana phenotyping, with the scientific community. DESCRIPTION: PHENOPSIS DB is a publicly available (URL: http://bioweb.supagro.inra.fr/phenopsis/) 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. CONCLUSIONS: 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.