GnpIS-Ephesis, the phenotypic data integration platform for INRA networks experimental data – data discovery and dataset building use cases
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Pommier, C.; Alaux, M.; Letellier, T.; Michotey, C.; Cornut, G.; Lebreton, A.; Labernadiere, M.; Laine, M.; Arnaud, E.; Adam-Blondon, A-F.; Quesneville, H. (2016) GnpIS-Ephesis, the phenotypic data integration platform for INRA networks experimental data – data discovery and dataset building use cases. [Abstract] presented at: XXIV Plant and Animal Genome Conference. San Diego, CA (USA) 9-13 Jan 2016.
Permanent link to this item: http://hdl.handle.net/10568/78780
Phenotype data are collected in trials conducted by experimental facilities including multilocal field networks and high throughput phenotyping facilities in controlled environments or fields. A given germplasm panel can therefore have been phenotyped in very different conditions and using very different protocols. As a result, a collection of phenotype datasets is usually highly heterogeneous and hard to integrate. GnpIS is an integrative information system dedicated to plant and their pathogens. The integration of heterogeneous phenotypic datasets implies identifying common pivot resources like germplasm, observation variables following the Cropontology model, experimental locations and years. GnpIS allows performing data discovery on those data, which can lead to datasets building through the GnpIS-Ephesis application. GnpIS-Ephesis allows creating datasets to study the relations between yield, including its components, stress and disease tolerance from public provided by the INRA Wheat Network Phenotypic. It includes fifteen years of observations on eleven experimental sites. Those study datasets can be narrowed from a maximum number of observations on several years and locations to a minimal dataset on comparable locations/years pairs to reduce the environmental variability. This variability can be evaluated thanks to reference germplasms. Quercus, Populus or Vitis public data available in GnpIS-Ephesis allows to study adaptation to climate change. For instance, a dataset including phenology variables like budbreak or flowering can be extracted and used as input for statistical analysis tools or model to evaluate adaptability of several hundreds of vitis variety.