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

Laboratoire d'Ecophysiologie des Plantes sous Stress Environnementaux (LEPSE), INRA-AGRO-M, UMR 759, 2 Place Viala, 34060 Montpellier Cedex 1 France.
BMC Plant Biology (Impact Factor: 3.81). 05/2011; 11(1):77. DOI: 10.1186/1471-2229-11-77
Source: PubMed


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: 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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    • "W ) / ( TW À DW ) ( Table S1 ) . At day 28 , the rosette of each plant was harvested , and the rosette FW was determined ( Table S1 ) . Growth was monitored by taking pictures twice a day . These pictures were processed in ImageJ using the macros developed for PHENOPSIS . All pictures and the ImageJ macros are publically available on PHENOPSISDB ( Fabre et al . 2011 Q34 , http : / / bioweb . supagro . inra . fr / phenopsis ) . The projected leaf area ( PLA ) of each plant was determined semi - automatically on the following days : 8 , 11 , 14 , 16 , 18 , 20 , 22 , 24 , 25 , 26 , 27 and 28 ( Table S1 ) . When more than one plant was present in a pot before thinning , the largest one close to the mid"
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    ABSTRACT: Large areas of arable land are often confronted with irregular rainfall resulting in limited water availability for part(s) of the growing seasons, which demands research for drought tolerance of plants. Natural variation was observed for biomass accumulation upon controlled moderate drought stress in 324 natural accessions of Arabidopsis. Improved performance under drought stress was correlated with early flowering and lack of vernalization requirement, indicating overlap in the regulatory networks of flowering time and drought response or correlated responses of these traits to natural selection. In addition, plant size was negatively correlated with relative water content (RWC) independent of the absolute water content (WC) indicating a prominent role for soluble compounds. Growth in control and drought conditions was determined over time, and modelled by an exponential function. GWA mapping of temporal plant size data and of model parameters resulted in the detection of six, time-dependent, QTLs strongly associated with drought. Most QTLs would not have been identified if plant size was determined at a single time-point. Analysis of earlier reported gene expression changes upon drought enabled us to identify for each QTL the most likely candidates. This article is protected by copyright. All rights reserved. This article is protected by copyright. All rights reserved.
    Plant Cell and Environment 07/2015; DOI:10.1111/pce.12595 · 6.96 Impact Factor
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    • "petioles as well as raw biomass measurements are available at under experiment C3M13B (Fabre et al., 2011). Images of chemically cleared leaves (grayscale) and hand-traced subregions (annotated with yellow for fully-cleared regions; with red for veins) are available at under the collection name 'Arabidopsis thaliana genotypes'. "
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    AoB PLANTS 05/2015; 7(1). DOI:10.1093/aobpla/plv049 · 2.27 Impact Factor
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    • "These pictures were processed in ImageJ using the macros developed for PHENOPSIS. All pictures and the ImageJ macros are publically available on PHENOPSISDB (Fabre et al., 2011; http:// The projected leaf area (PLA) of each plant was determined semi-automatically on days 8, 11, 14, 16, 18, 20, 22, 24, 25, 26, 27, and 28. "
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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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