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.
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.
Authors: A Brazma; P Hingamp; J Quackenbush; G Sherlock; P Spellman; C Stoeckert; J Aach; W Ansorge; C A Ball; H C Causton; T Gaasterland; P Glenisson; F C Holstege; I F Kim; V Markowitz; J C Matese; H Parkinson; A Robinson; U Sarkans; S Schulze-Kremer; J Stewart; R Taylor; J Vilo; M Vingron Journal: Nat Genet Date: 2001-12 Impact factor: 38.330
Authors: Catherine Massonnet; Denis Vile; Juliette Fabre; Matthew A Hannah; Camila Caldana; Jan Lisec; Gerrit T S Beemster; Rhonda C Meyer; Gaëlle Messerli; Jesper T Gronlund; Josip Perkovic; Emma Wigmore; Sean May; Michael W Bevan; Christian Meyer; Silvia Rubio-Díaz; Detlef Weigel; José Luis Micol; Vicky Buchanan-Wollaston; Fabio Fiorani; Sean Walsh; Bernd Rinn; Wilhelm Gruissem; Pierre Hilson; Lars Hennig; Lothar Willmitzer; Christine Granier Journal: Plant Physiol Date: 2010-03-03 Impact factor: 8.340
Authors: Christopher J Mungall; Georgios V Gkoutos; Cynthia L Smith; Melissa A Haendel; Suzanna E Lewis; Michael Ashburner Journal: Genome Biol Date: 2010-01-08 Impact factor: 13.583
Authors: Ramona L Walls; Balaji Athreya; Laurel Cooper; Justin Elser; Maria A Gandolfo; Pankaj Jaiswal; Christopher J Mungall; Justin Preece; Stefan Rensing; Barry Smith; Dennis W Stevenson Journal: Am J Bot Date: 2012-07-30 Impact factor: 3.844
Authors: Benjamin Blonder; François Vasseur; Cyrille Violle; Bill Shipley; Brian J Enquist; Denis Vile Journal: AoB Plants Date: 2015-05-08 Impact factor: 3.276