PHENOPSIS DB: an Information System for Arabidopsis thaliana phenotypic data in an environmental context
ABSTRACT 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.
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.
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.
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ABSTRACT: The leaf economics spectrum (LES) describes strong relationships between multiple functional leaf traits that determine resource fluxes in vascular plants. Five models have been proposed to explain these patterns, two based on patterns of structural allocation, two on venation networks, and one on resource allocation to cell walls and cell contents. Here we test these models using data for leaf and whole-plant functional traits. We use structural equation modelling applied to multiple ecotypes, recombinant inbred lines, near isogenic lines and vascular patterning mutants of Arabidopsis thaliana that express LES trait variation. We show that wide variation in multiple functional traits recapitulates the leaf economics spectrum at the whole-plant scale. The Wright et al. (2004) model and the Blonder et al. (2013) venation network model cannot be rejected by data, while two simple models and the Shipley et al. (2006) allocation model are rejected. Venation networks remain a key hypothesis for the origin of the LES, but simpler explanations also cannot be ruled out. Published by Oxford University Press on behalf of the Annals of Botany Company.AoB PLANTS 05/2015; DOI:10.1093/aobpla/plv049 · 1.74 Impact Factor
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DESCRIPTION: Growth is a complex trait determined by the interplay between many genes, some of which play a role at a specific moment during development whereas others play a more general role. To identify the genetic basis of growth, natural variation in Arabidopsis rosette growth was followed in 324 accessions by a combination of top-view imaging, high-throughput image analysis, modelling of growth dynamics, and end-point fresh weight determination. Genome-wide association (GWA) mapping of the temporal growth data resulted in the detection of time-specific quantitative trait loci (QTLs), whereas mapping of model parameters resulted in another set of QTLs related to the whole growth curve. The positive correlation between projected leaf area (PLA) at different time points during the course of the experiment suggested the existence of general growth factors with a function in multiple developmental stages or with prolonged downstream effects. Many QTLs could not be identified when growth was evaluated only at a single time point. Eleven candidate genes were identified, which were annotated to be involved in the determination of cell number and size, seed germination, embryo development, developmental phase transition, or senescence. For eight of these, a mutant or overexpression phenotype related to growth has been reported, supporting the identification of true positives. In addition, the detection of QTLs without obvious candidate genes implies the annotation of novel functions for underlying genes.
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ABSTRACT: Plant breeding and genetics demand fast, exact and reproducible phenotyping. Efficient statistical evaluation of phenotyping data requires standardised data storage ensuring long-term data availability while maintaining intellectual property rights. This is state of the art at phenomics centres, which, however, are unavailable for most scientists. For them we developed a simple and cost-efficient system, the Phenotyper, which employs mobile devices or personal digital assistants (PDA) for on-site data entry and open-source software for data management. A graphical user interface (GUI) on a PDA replaces paper-based form sheet and data entry on a desktop. The user can define his phenotyping schemes in a web tool without in-depth knowledge of the system and thus adjust it more easily to new research aspects than in a classical laboratory information management system (LIMS). In the Phenotyper, schemes are built from controlled vocabulary gained from published ontologies. Vocabulary and schemes are stored in a database that also manages the user access. From the web page, schemes are downloaded as extended markup language (XML) files for the transfer to the PDA and the exchange between users. On the PDA, the GUI displays the schemes and stores data in comma separated value format and XML format. After manual quality control, data are uploaded via a web page to an independently hosted results database, in which data are stored in an entity-attribute-value structure to provide maximum flexibility. Datasets are linked to the original and curated data files stored on a file server. The ownership stamp, project affiliation and date stamp of a dataset are used to regulate data access, which is restricted to data belonging to the user or to his projects and data, for which the embargo period has ended. By export of standardised ASCII reports to long-term data storage facility, long-term accessibility allows searching, citing and use of raw data beyond the lifetime of the database. The Phenotyper is available to the scientific community for use and further development. The Phenotyper provides a well-structured, but flexible data acquisition and management structure for mobile on-site measurements for efficient evaluation and shared use of data.Plant Methods 04/2015; 11(1). DOI:10.1186/s13007-015-0069-3 · 2.59 Impact Factor