The e-ROSA project seeks to build a shared vision of a future sustainable e-infrastructure for research and education in agriculture in order to promote Open Science in this field and as such contribute to addressing related societal challenges. In order to achieve this goal, e-ROSA’s first objective is to bring together the relevant scientific communities and stakeholders and engage them in the process of coelaboration of an ambitious, practical roadmap that provides the basis for the design and implementation of such an e-infrastructure in the years to come.
This website highlights the results of a bibliometric analysis conducted at a global scale in order to identify key scientists and associated research performing organisations (e.g. public research institutes, universities, Research & Development departments of private companies) that work in the field of agricultural data sources and services. If you have any comment or feedback on the bibliometric study, please use the online form.
You can access and play with the graphs:
- Evolution of the number of publications between 2005 and 2015
- Map of most publishing countries between 2005 and 2015
- Network of country collaborations
- Network of institutional collaborations (+10 publications)
- Network of keywords relating to data - Link
Identification of altered metabolic pathways of gamma-irradiated rice mutant via network-based transcriptome analysis
In order to develop rice mutants for crop improvement, we applied gamma-irradiation mutagenesis and selected a rice seed color mutant (MT) in the M-14 targeting-induced local lesions in genome lines. This mutant exhibited differences in germination rate, plant height, and root length in seedlings compared to the wild-type plants. We found 1645 different expressed probes of MT by microarray hybridization. To identify the modified metabolic pathways, we conducted integrated genomic analysis such as weighted correlation network analysis with a module detection method of differentially expressed genes (DEGs) in MT on the basis of large-scale microarray transcriptional profiling. These modules are largely divided into three subnetworks and mainly exhibit overrepresented gene ontology functions such as oxidation-related function, ion-binding, and kinase activity (phosphorylation), and the expressional coherences of module genes mainly exhibited in vegetative and maturation stages. Through a metabolic pathway analysis, we detected the significant DEGs involved in the major carbohydrate metabolism (starch degradation), protein degradation (aspartate protease), and signaling in sugars and nutrients. Furthermore, the accumulation of amino acids (asparagine and glutamic acid), sucrose, and starch in MT were affected by gamma rays. Our results provide an effective approach for identification of metabolic pathways associated with useful agronomic traits in mutation breeding.
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