Sandra Dèrozier1,2, Franck Samson1,2, Jean-Philippe Tamby1, Cécile Guichard1, Véronique Brunaud1, Philippe Grevet1, Séverine Gagnot1,3, Philippe Label4, Jean-Charles Leplé4, Alain Lecharny1, Sébastien Aubourg1. 1. Unité de Recherche en Génomique Végétale (URGV), UMR INRA 1165 - Université d'Evry Val d'Essonne - ERL CNRS 8196, 2 Rue Gaston Crémieux, CP 5708, F-91057 Evry Cedex, France. 2. Unité Mathématique Informatique et Génome (MIG), UR INRA 1077, Domaine de Vilvert, F-78352 Jouy-en-Josas Cedex, France. 3. Laboratoire de Chimie Bactérienne (LCB), UPR CNRS 9043 - IFR 88, 31 Chemin Joseph Aiguier, F-13009 Marseille, France. 4. Unité Amélioration, Génétique et Physiologie Forestières (UAGPF), UR INRA 588, 2163 avenue de la Pomme de Pin, CS 4001 Ardon, F-45075 Orléans, France.
Abstract
BACKGROUND: In the contexts of genomics, post-genomics and systems biology approaches, data integration presents a major concern. Databases provide crucial solutions: they store, organize and allow information to be queried, they enhance the visibility of newly produced data by comparing them with previously published results, and facilitate the exploration and development of both existing hypotheses and new ideas. RESULTS: The FLAGdb++ information system was developed with the aim of using whole plant genomes as physical references in order to gather and merge available genomic data from in silico or experimental approaches. Available through a JAVA application, original interfaces and tools assist the functional study of plant genes by considering them in their specific context: chromosome, gene family, orthology group, co-expression cluster and functional network. FLAGdb++ is mainly dedicated to the exploration of large gene groups in order to decipher functional connections, to highlight shared or specific structural or functional features, and to facilitate translational tasks between plant species (Arabidopsis thaliana, Oryza sativa, Populus trichocarpa and Vitis vinifera). CONCLUSION: Combining original data with the output of experts and graphical displays that differ from classical plant genome browsers, FLAGdb++ presents a powerful complementary tool for exploring plant genomes and exploiting structural and functional resources, without the need for computer programming knowledge. First launched in 2002, a 15th version of FLAGdb++ is now available and comprises four model plant genomes and over eight million genomic features.
BACKGROUND: In the contexts of genomics, post-genomics and systems biology approaches, data integration presents a major concern. Databases provide crucial solutions: they store, organize and allow information to be queried, they enhance the visibility of newly produced data by comparing them with previously published results, and facilitate the exploration and development of both existing hypotheses and new ideas. RESULTS: The FLAGdb++ information system was developed with the aim of using whole plant genomes as physical references in order to gather and merge available genomic data from in silico or experimental approaches. Available through a JAVA application, original interfaces and tools assist the functional study of plant genes by considering them in their specific context: chromosome, gene family, orthology group, co-expression cluster and functional network. FLAGdb++ is mainly dedicated to the exploration of large gene groups in order to decipher functional connections, to highlight shared or specific structural or functional features, and to facilitate translational tasks between plant species (Arabidopsis thaliana, Oryza sativa, Populus trichocarpa and Vitis vinifera). CONCLUSION: Combining original data with the output of experts and graphical displays that differ from classical plant genome browsers, FLAGdb++ presents a powerful complementary tool for exploring plant genomes and exploiting structural and functional resources, without the need for computer programming knowledge. First launched in 2002, a 15th version of FLAGdb++ is now available and comprises four model plant genomes and over eight million genomic features.
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